An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition

USGS Publications Warehouse

Brown, Jesslyn; Howard, Daniel M.; Wylie, Bruce K.; Friesz, Aaron M.; Ji, Lei; Gacke, Carolyn

2015-01-01

Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: an example at corn fields in western Mexico.

PubMed

Chen, Pei-Yu; Fedosejevs, Gunar; Tiscareño-López, Mario; Arnold, Jeffrey G

2006-08-01

Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.

Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?

Treesearch

Yulong Zhang; Conghe Song; Lawrence E. Band; Ge Sun; Junxiang Li

2017-01-01

Accurately monitoring global vegetation dynamics with modern remote sensing is critical for understanding the functions and processes of the biosphere and its interactions with the planetary climate. The MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for this purpose. To date, theMODIS teamhad released...

Global Enhanced Vegetation Index

NASA Technical Reports Server (NTRS)

2002-01-01

By carefully measuring the wavelengths and intensity of visible and near-infrared light reflected by the land surface back up into space, the Moderate-resolution Imaging Spectroradiometer (MODIS) Team can quantify the concentrations of green leaf vegetation around the world. The above MODIS Enhanced Vegetation Index (EVI) map shows the density of plant growth over the entire globe. Very low values of EVI (white and brown areas) correspond to barren areas of rock, sand, or snow. Moderate values (light greens) represent shrub and grassland, while high values indicate temperate and tropical rainforests (dark greens). The MODIS EVI gives scientists a new tool for monitoring major fluctuations in vegetation and understanding how they affect, and are affected by, regional climate trends. For more information, read NASA Unveils Spectacular Suite of New Global Data Products from MODIS. Image courtesy MODIS Land Group/Vegetation Indices, Alfredo Huete, Principal Investigator, and Kamel Didan, University of Arizona

Initial Validation of NDVI time seriesfrom AVHRR, VEGETATION, and MODIS

NASA Technical Reports Server (NTRS)

Morisette, Jeffrey T.; Pinzon, Jorge E.; Brown, Molly E.; Tucker, Jim; Justice, Christopher O.

2004-01-01

The paper will address Theme 7: Multi-sensor opportunities for VEGETATION. We present analysis of a long-term vegetation record derived from three moderate resolution sensors: AVHRR, VEGETATION, and MODIS. While empirically based manipulation can ensure agreement between the three data sets, there is a need to validate the series. This paper uses atmospherically corrected ETM+ data available over the EOS Land Validation Core Sites as an independent data set with which to compare the time series. We use ETM+ data from 15 globally distributed sites, 7 of which contain repeat coverage in time. These high-resolution data are compared to the values of each sensor by spatially aggregating the ETM+ to each specific sensors' spatial coverage. The aggregated ETM+ value provides a point estimate for a specific site on a specific date. The standard deviation of that point estimate is used to construct a confidence interval for that point estimate. The values from each moderate resolution sensor are then evaluated with respect to that confident interval. Result show that AVHRR, VEGETATION, and MODIS data can be combined to assess temporal uncertainties and address data continuity issues and that the atmospherically corrected ETM+ data provide an independent source with which to compare that record. The final product is a consistent time series climate record that links historical observations to current and future measurements.

Optimal Grid Size for Inter-Comparability of MODIS And VIIRS Vegetation Indices at Level 2G or Higher

NASA Astrophysics Data System (ADS)

Campagnolo, M.; Schaaf, C.

2016-12-01

Due to the necessity of time compositing and other user requirements, vegetation indices, as well as many other EOS derived products, are distributed in a gridded format (level L2G or higher) using an equal area sinusoidal grid, at grid sizes of 232 m, 463 m or 926 m. In this process, the actual surface signal suffers somewhat of a degradation, caused by both the sensor's point spread function and this resampling from swath to the regular grid. The magnitude of that degradation depends on a number of factors, such as surface heterogeneity, band nominal resolution, observation geometry and grid size. In this research, the effect of grid size is quantified for MODIS and VIIRS (at five EOS validation sites with distinct land covers), for the full range of view zenith angles, and at grid sizes of 232 m, 253 m, 309 m, 371 m, 397 m and 463 m. This allows us to compare MODIS and VIIRS gridded products for the same scenes, and to determine the grid size at which these products are most similar. Towards that end, simulated MODIS and VIIRS bands are generated from Landsat 8 surface reflectance images at each site and gridded products are then derived by using maximum obscov resampling. Then, for every grid size, the original Landsat 8 NDVI and the derived MODIS and VIIRS NDVI products are compared. This methodology can be applied to other bands and products, to determine which spatial aggregation overall is best suited for EOS to S-NPP product continuity. Results for MODIS (250 m bands) and VIIRS (375 m bands) NDVI products show that finer grid sizes tend to be better at preserving the original signal. Significant degradation for gridded NDVI occurs when grid size is larger then 253 m (MODIS) and 371 m (VIIRS). Our results suggest that current MODIS "500 m" (actually 463 m) grid size is best for product continuity. Note however, that up to that grid size value, MODIS gridded products are somewhat better at preserving the surface signal than VIIRS, except for at very high VZA.

FLUXNET to MODIS: Connecting the dots to capture heterogenious biosphere metabolism

NASA Astrophysics Data System (ADS)

Woods, K. D.; Schwalm, C.; Huntzinger, D. N.; Massey, R.; Poulter, B.; Kolb, T.

2015-12-01

Eddy co-variance flux towers provide our most widely distributed network of direct observations for land-atmosphere carbon exchange. Carbon flux sensitivity analysis is a method that uses in situ networks to understand how ecosystems respond to changes in climatic variables. Flux towers concurrently observe key ecosystem metabolic processes (e..g. gross primary productivity) and micrometeorological variation, but only over small footprints. Remotely sensed vegetation indices from MODIS offer continuous observations of the vegetated land surface, but are less direct, as they are based on light use efficiency algorithms, and not on the ground observations. The marriage of these two data products offers an opportunity to validate remotely sensed indices with in situ observations and translate information derived from tower sites to globally gridded products. Here we provide correlations between Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and MODIS gross primary production with FLUXNET derived estimates of gross primary production, respiration and net ecosystem exchange. We demonstrate remotely sensed vegetation products which have been transformed to gridded estimates of terrestrial biosphere metabolism on a regional-to-global scale. We demonstrate anomalies in gross primary production, respiration, and net ecosystem exchange as predicted by both MODIS-carbon flux sensitivities and meteorological driver-carbon flux sensitivities. We apply these sensitivities to recent extreme climatic events and demonstrate both our ability to capture changes in biosphere metabolism, and differences in the calculation of carbon flux anomalies based on method. The quantification of co-variation in these two methods of observation is important as it informs both how remotely sensed vegetation indices are correlated with on the ground tower observations, and with what certainty we can expand these observations and relationships.

[Temporal and spatial variation of MODIS vegetation indices in Hunan Province].

PubMed

Lin, Hui; Xiong, Yu-Jiu; Wan, Ling-Feng; Mo, Deng-Kui; Sun, Hua

2007-03-01

Based on MODIS images and by using the algorithm of maximum value composite (MVC), the monthly vegetation indices (VIs) in 2005 in Hunan Province were obtained. Through the analysis of the MODIS VIs, Hunan Province was divided into six districts to describe the spatial distribution of the VIs, and by using the monthly mean temperature and rainfall data collected from 5 climatic monitoring stations in this province, the temporal variation of the VIs was analyzed. The results showed that the spatial distribution of MODIS VIs was positively correlated with vegetation cover, and appeared regional characteristics. The MODIS VIs varied with season, and the curves of their monthly mean values were downwards opening quadratic parabolas, with the maximum appeared in July. The value of MODIS EVI was smaller than that of MODIS NDVI. MODIS VI was mainly affected by monthly mean temperature, but this effect was decreased with decreasing latitude. The variation pattern of MODIS EVI was more apparent than that of MODIS NDVI, i. e. , the quadratic parabola of MODIS EVI was smoother, going gradually from minimum to maximum and then going down, while that of MODIS NDVI had tiny fluctuations on both sides of the maximum point.

MODIS GPP/NPP for complex land use area: a case study of comparison between MODIS GPP/NPP and ground-based measurements over Korea

NASA Astrophysics Data System (ADS)

Shim, C.

2013-12-01

The Moderate Resolution Imaging Radiometer (MODIS) Gross Primary Productivity (GPP)/Net Primary Productivity (NPP) has been widely used for the study on global terrestrial ecosystem and carbon cycle. The current MODIS product with ~ 1 km spatial resolution, however, has limitation on the information on local scale environment (< 1km), particularly on the regions with complex land-use types. Here we try to test the performance of MODIS annual GPP/NPP for a case of Korea, where the vegetation types are mostly heterogeneous within a size of MODIS products (~1km). We selected the sites where the ground/tower flux measurements and MODIS retrievals were simultaneously available and the land classification of sites agreed the forest type map (~71m) (1 site over Gwangneung flux tower (GDK) for 2006-2008 and 2 sites of ground measurements over Cheongju (CJ1 and CJ2) for 2011). The MODIS GPP are comparable to that of GDK (largely deciduous forest) within -6.3 ~ +2.3% of bias (-104.5 - 37.9 gCm-2yr-1). While the MODIS NPP of CJ1 at Cheongju (largely Larix leptolepis) underestimated NPP by 34% (-224.5 gCm-2yr-1), the MODIS NPP of CJ2 (largely Pinus densiflora) agreed well with -0.2% of bias (1.6 gCm-2yr-1). The fairly comparable values of the MODIS here however, cannot assure the quality of the MOD17 over the complex vegetation area of Korea since the ground measurements except the eddy covariance tower flux measurements are highly inconsistent. Therefore, the comprehensive experiments to represents GPP/NPP over diverse vegetation types for a comparable scale of MODIS with a consistent measurement technique are necessary in order to evaluate the MODIS vegetation productivity data over Korea, which contains a large portion of highly heterogeneous vegetation area.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

Wang, Dongdong; Morton, Douglas; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2011-01-01

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.

Estimating the Effect of Gypsy Moth Defloiation Using MODIS

NASA Technical Reports Server (NTRS)

deBeurs, K. M.; Townsend, P. A.

2008-01-01

The area of North American forests affected by gypsy moth defoliation continues to expand despite efforts to slow the spread. With the increased area of infestation, ecological, environmental and economic concerns about gypsy moth disturbance remain significant, necessitating coordinated, repeatable and comprehensive monitoring of the areas affected. In this study, our primary objective was to estimate the magnitude of defoliation using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for a gypsy moth outbreak that occurred in the US central Appalachian Mountains in 2000 and 2001. We focused on determining the appropriate spectral MODIS indices and temporal compositing method to best monitor the effects of gypsy moth defoliation. We tested MODIS-based Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and two versions of the Normalized Difference Infrared index (NDIIb6 and NDIIb7, using the channels centered on 1640 nm and 2130 nm respectively) for their capacity to map defoliation as estimated by ground observations. In addition, we evaluated three temporal resolutions: daily, 8-day and 16-day data. We validated the results through quantitative comparison to Landsat based defoliation estimates and traditional sketch maps. Our MODIS based defoliation estimates based on NDIIb6 and NDIIb7 closely matched Landsat defoliation estimates derived from field data as well as sketch maps. We conclude that daily MODIS data can be used with confidence to monitor insect defoliation on an annual time scale, at least for larger patches (greater than 0.63 km2). Eight-day and 16-day MODIS composites may be of lesser use due to the ephemeral character of disturbance by the gypsy moth.

Inter-Comparison of MODIS and VIIRS Vegetation Indices Using One-Year Global Data

NASA Astrophysics Data System (ADS)

Miura, T.; Muratsuchi, J.; Obata, K.; Kato, A.; Vargas, M.; Huete, A. R.

2016-12-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor series of the Joint Polar Satellite System program is slated to continue the highly calibrated data stream initiated with the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. A number of geophysical products are being/to be produced from VIIRS data, including the "Top-of-the-Atmosphere (TOA)" Normalized Difference Vegetation Index (NDVI), "Top-of-Canopy (TOC)" Enhanced Vegetation Index (EVI), and TOC NDVI. In this study, we cross-compared vegetation indices (VIs) from the first VIIRS sensor aboard the Suomi National Polar-orbiting Partnership satellite with the Aqua MODIS counterparts using one-year global data. This study was aimed at developing a thorough understanding of radiometric compatibility between the two VI datasets across globe, seasons, a range of viewing angle, and land cover types. VIIRS and MODIS VI data of January-December 2015 were obtained at monthly intervals when their orbital tracks coincided. These data were projected and spatially-aggregated into a .0036-degree grid while screening for cloud and aerosol contaminations using their respective quality flags. VIIRS-MODIS observation pairs with near-identical sun-target-view angles were extracted from each of these monthly image pairs for cross-comparison. The four VIs of TOA NDVI, TOC NDVI, TOC EVI, and TOC EVI2 (a two-band version of the EVI) were analyzed. Between MODIS and VIIRS, TOA NDVI, TOC NDVI, and TOC EVI2 had very small overall mean differences (MD) of .014, .013, and .013 VI units, respectively, whereas TOC EVI had a slightly larger overall MD of 0.023 EVI units attributed to the disparate blue bands of the two sensors. These systematic differences were consistent across the one-year period. With respect to sun-target-viewing geometry, MDs were also consistent across the view zenith angle range, but always lower for forward- than backward-viewing geometry. MDs showed large land cover dependencies for TOA NDVI and TOC NDVI, varying 10 folds from .002 for forests to .02 for sparsely-vegetated areas. They were consistent across land cover types for TOC EVI and TOC EVI2. Future studies should address the impact of sun-target-view geometry on corss-sensor VI comparisons.

Rice Crop Monitoring and Yield Assessment with MODIS 250m Gridded Vegetation Products: A Case Study of Sa Kaeo Province, Thailand

NASA Astrophysics Data System (ADS)

Wijesingha, J. S. J.; Deshapriya, N. L.; Samarakoon, L.

2015-04-01

Billions of people in the world depend on rice as a staple food and as an income-generating crop. Asia is the leader in rice cultivation and it is necessary to maintain an up-to-date rice-related database to ensure food security as well as economic development. This study investigates general applicability of high temporal resolution Moderate Resolution Imaging Spectroradiometer (MODIS) 250m gridded vegetation product for monitoring rice crop growth, mapping rice crop acreage and analyzing crop yield, at the province-level. The MODIS 250m Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series data, field data and crop calendar information were utilized in this research in Sa Kaeo Province, Thailand. The following methodology was used: (1) data pre-processing and rice plant growth analysis using Vegetation Indices (VI) (2) extraction of rice acreage and start-of-season dates from VI time series data (3) accuracy assessment, and (4) yield analysis with MODIS VI. The results show a direct relationship between rice plant height and MODIS VI. The crop calendar information and the smoothed NDVI time series with Whittaker Smoother gave high rice acreage estimation (with 86% area accuracy and 75% classification accuracy). Point level yield analysis showed that the MODIS EVI is highly correlated with rice yield and yield prediction using maximum EVI in the rice cycle predicted yield with an average prediction error 4.2%. This study shows the immense potential of MODIS gridded vegetation product for keeping an up-to-date Geographic Information System of rice cultivation.

How will Shrub Expansion Impact Soil Carbon Sequestration in Arctic Tundra?

NASA Astrophysics Data System (ADS)

Czimczik, C. I.; Holden, S. R.; He, Y.; Randerson, J. T.

2015-12-01

Multiple lines of evidence suggest that plant productivity, and especially shrub abundance, is increasing in the Arctic in response to climate change. This greening is substantiated by increases in the Normalized Difference Vegetation Index, repeat photography and field observations. The implications of a greener Arctic on carbon sequestration by tundra ecosystems remain poorly understood. Here, we explore existing datasets of plant productivity and soil carbon stocks to quantify how greening, and in particular an expansion of woody shrubs, may translate to the sequestration of carbon in arctic soils. As an estimate of carbon storage in arctic tundra soils, we used the Northern Circumpolar Soil Carbon Database v2. As estimates of tundra type and productivity, we used the Circumpolar Arctic Vegetation map as well as the MODIS and Landsat Vegetation Continuous Fields, and MODIS GPP/NPP (MOD17) products. Preliminary findings suggest that in graminoid tundra and erect-shrub tundra higher shrub abundance is associated with greater soil carbon stocks. However, this relationship between shrub abundance and soil carbon is not apparent in prostrate-shrub tundra, or when comparing across graminoid tundra, erect-shrub tundra and prostrate-shrub tundra. Uncertainties originate from the extreme spatial (vertical and horizontal) heterogeneity of organic matter distribution in cryoturbated soils, the fact that (some) permafrost carbon stocks, e.g. yedoma, reflect previous rather than current vegetative cover, and small sample sizes, esp. in the High Arctic. Using Vegetation Continuous Fields and MODIS GPP/NPP (MOD17), we develop quantitative trajectories of soil carbon storage as a function of shrub cover and plant productivity in the Arctic (>60°N). We then compare our greening-derived carbon sequestration estimates to projected losses of carbon from thawing permafrost. Our findings will reduce uncertainties in the magnitude and timing of the carbon-climate feedback from the terrestrial Arctic, and thus provide guidance for future climate mitigation and adaptation strategies.

Variation of MODIS reflectance and vegetation indices with viewing geometry and soybean development.

PubMed

Breunig, Fábio M; Galvão, Lênio S; Formaggio, Antônio R; Epiphanio, José C N

2012-06-01

Directional effects introduce a variability in reflectance and vegetation index determination, especially when large field-of-view sensors are used (e.g., Moderate Resolution Imaging Spectroradiometer - MODIS). In this study, we evaluated directional effects on MODIS reflectance and four vegetation indices (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI(1640) and NDWI(2120)) with the soybean development in two growing seasons (2004-2005 and 2005-2006). To keep the reproductive stage for a given cultivar as a constant factor while varying viewing geometry, pairs of images obtained in close dates and opposite view angles were analyzed. By using a non-parametric statistics with bootstrapping and by normalizing these indices for angular differences among viewing directions, their sensitivities to directional effects were studied. Results showed that the variation in MODIS reflectance between consecutive phenological stages was generally smaller than that resultant from viewing geometry for closed canopies. The contrary was observed for incomplete canopies. The reflectance of the first seven MODIS bands was higher in the backscattering. Except for the EVI, the other vegetation indices had larger values in the forward scattering direction. Directional effects decreased with canopy closure. The NDVI was lesser affected by directional effects than the other indices, presenting the smallest differences between viewing directions for fixed phenological stages.

eMODIS Expedited: Overview of a Near Real Time MODIS Production System for Operational Vegetation Monitoring

NASA Astrophysics Data System (ADS)

Jenkerson, C.; Meyer, D. J.; Werpy, J.; Evenson, K.; Merritt, M.

2010-12-01

The expedited MODIS, or eMODIS production system derives near-real time Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance provided by the Land and Atmosphere Near-real time Capability for EOS (LANCE). There are currently three regions covered by this U.S. Geological Survey (USGS) capability, including the continental U.S., Africa, and the Central America/Caribbean regions. Each of the eMODIS production streams is configured to output its data in map projections, compositing intervals, spatial resolutions, and file formats specific to its region and user community. The challenges of processing 1,000-meter, 500-m, and especially 250-m products by midnight on the last day of a product interval have been met with increasingly effective software and system architecture. An anonymous file transfer protocol (FTP) distribution site (ftp://emodisftp.cr.usgs.gov/eMODIS) allows users direct access to eMODIS NDVI products for operational (near-real time) monitoring of vegetation conditions like drought, crop failure, insect infestation, and other threats, thus supporting subsequent early warning of famine and the targeting of vulnerable populations for insecure food situations.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

Wang, Dongdong; Morton, Douglas Christopher; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2012-01-01

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr-1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

Application and Evaluation of MODIS LAI, FPAR, and Albedo ...

EPA Pesticide Factsheets

MODIS vegetation and albedo products provide a more realistic representation of surface conditions for input to the WRF/CMAQ modeling system. However, the initial evaluation of ingesting MODIS data into the system showed mixed results, with increased bias and error for 2-m temperature and reduced bias and error for 2-m mixing ratio. Recently, the WRF/CMAQ land surface and boundary laywer processes have been updated. In this study, MODIS vegetation and albedo data are input to the updated WRF/CMAQ meteorology and air quality simulations for 2006 over a North American (NA) 12-km domain. The evaluation of the simulation results shows that the updated WRF/CMAQ system improves 2-m temperature estimates over the pre-update base modeling system estimates. The MODIS vegetation input produces a realistic spring green-up that progresses through time from the south to north. Overall, MODIS input reduces 2-m mixing ration bias during the growing season. The NA west shows larger positive O3 bias during the growing season because of reduced gas phase deposition resulting from lower O3 deposition velocities driven by reduced vegetation cover. The O3 bias increase associated with the realistic vegetation representation indicates that further improvement may be needed in the WRF/CMAQ system. The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s rese

Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico.

PubMed

Jarchow, Christopher J; Didan, Kamel; Barreto-Muñoz, Armando; Nagler, Pamela L; Glenn, Edward P

2018-05-13

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000⁻2011), 8 (2013⁻2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013⁻2016) to MODIS EVI (2000⁻2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R² = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R² = 0.27) and riparian vegetation (R² = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R² = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.

Phenological Parameters Estimation Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney D.; Ross, Kenton W.; Spruce, Joseph P.; Smoot, James C.; Ryan, Robert E.; Gasser, Gerald E.; Prados, Donald L.; Vaughan, Ronald D.

2010-01-01

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico

PubMed Central

Jarchow, Christopher J.; Didan, Kamel; Barreto-Muñoz, Armando; Glenn, Edward P.

2018-01-01

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R2 = 0.27) and riparian vegetation (R2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area. PMID:29757265

Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

EPA Science Inventory

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Qual...

Using VIIRS to Provide Data Continuity with MODIS

NASA Technical Reports Server (NTRS)

Murphy, Robert E.; Barnes, William L.; Lyapustin, Alexei I.; Privette, Jeffrey; Welsch, Carol; DeLuccia, Frank; Schueler, Carl F.; Ardanuy, Philip E.; Kealy, Peter S. M.; Smith, David E. (Technical Monitor)

2001-01-01

Long-term continuity of the data series being initiated by the MODIS (MODerate Resolution Imaging Spectroradiometer) on NASA's Terra mission will be obtained using the VIIRS (Visible Infrared Imaging Radiometer Suite) flying on the converged National Polar-Orbiting Environmental Satellite System (NPOESS) and on the NPOESS Preparatory Project (NPP). The data series include critical parameters such as cloud and aerosol properties, vegetation index, land use and land cover, ocean chlorophyll and sea surface temperature. VIIRS is being designed and built by Raytheon for the Integrated Program Office (IPO), the DoD, NOAA and NASA consortium that is responsible for NPOESS. In addition to meeting the requirements for operational environmental monitoring, VIIRS will meet the needs of the global change research community through the use of state-of-the-art algorithms and calibration and characterization activities.

Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China

NASA Astrophysics Data System (ADS)

Wu, Xiaodan; Wen, Jianguang; Xiao, Qing; Yu, Yunyue; You, Dongqin; Hueni, Andreas

2017-12-01

In this paper, the accuracy of Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) land surface albedo, which is derived from the direct estimation algorithm, was assessed using ground-based albedo observations from a wireless sensor network over a heterogeneous cropland in the Huailai station, northern China. Data from six nodes spanning 2013-2014 over vegetation, bare soil, and mixed terrain surfaces were utilized to provide ground reference at VIIRS pixel scale. The performance of VIIRS albedo was also compared with Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) albedos (Collection 5 and 6). The results indicate that the current granular VIIRS albedo has a high accuracy with a root-mean-square error of 0.02 for typical land covers. They are significantly correlated with ground references indicated by a correlation coefficient (R) of 0.73. The VIIRS albedo shows distinct advantages to GLASS and MODIS albedos over bare soil and mixed-cover surfaces, while it is inferior to the other two products over vegetated surfaces. Furthermore, its time continuity and the ability to capture the abrupt change of surface albedo are better than that of GLASS and MODIS albedo.

Improved meteorology from an updated WRF/CMAQ modeling ...

EPA Pesticide Factsheets

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim-Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green-up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvement

Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations

PubMed Central

de Moura, Yhasmin Mendes; Hilker, Thomas; Goncalves, Fabio Guimarães; Galvão, Lênio Soares; dos Santos, João Roberto; Lyapustin, Alexei; Maeda, Eduardo Eiji; de Jesus Silva, Camila Valéria

2018-01-01

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r2= 0.54, RMSE=0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy (0.52≤ r2≤ 0.61; p<0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (σ0) from SeaWinds/QuikSCAT presented an r2 of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon. PMID:29618964

MODIS NDVI Response Following Fires in Siberia

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Sun, G.; Kovacs, K.; Kharuk, V. I.

2003-01-01

The Siberian boreal forest is considered a carbon sink but may become an important source of carbon dioxide if climatic warming predictions are correct. The forest is continually changing through various disturbance mechanisms such as insects, logging, mineral exploitation, and especially fires. Patterns of disturbance and forest recovery processes are important factors regulating carbon flux in this area. NASA's Terra MODIS provides useful information for assessing location of fires and post fire changes in forests. MODIS fire (MOD14), and NDVI (MOD13) products were used to examine fire occurrence and post fire variability in vegetation cover as indicated by NDVI. Results were interpreted for various post fire outcomes, such as decreased NDVI after fire, no change in NDVI after fire and positive NDVI change after fire. The fire frequency data were also evaluated in terms of proximity to population centers, and transportation networks.

Effect of Spectrally Varying Albedo of Vegetation Surfaces on Shortwave Radiation Fluxes and Aerosol Direct Radiative Forcing

NASA Technical Reports Server (NTRS)

Zhu, L.; Martins, J. V.; Yu, H.

2012-01-01

This study develops an algorithm for representing detailed spectral features of vegetation albedo based on Moderate Resolution Imaging Spectrometer (MODIS) observations at 7 discrete channels, referred to as the MODIS Enhanced Vegetation Albedo (MEVA) algorithm. The MEVA algorithm empirically fills spectral gaps around the vegetation red edge near 0.7 micrometers and vegetation water absorption features at 1.48 and 1.92 micrometers which cannot be adequately captured by the MODIS 7 channels. We then assess the effects of applying MEVA in comparison to four other traditional approaches to calculate solar fluxes and aerosol direct radiative forcing (DRF) at the top of atmosphere (TOA) based on the MODIS discrete reflectance bands. By comparing the DRF results obtained through the MEVA method with the results obtained through the other four traditional approaches, we show that filling the spectral gap of the MODIS measurements around 0.7 micrometers based on the general spectral behavior of healthy green vegetation leads to significant improvement in the instantaneous aerosol DRF at TOA (up to 3.02Wm(exp -2) difference or 48% fraction of the aerosol DRF, .6.28Wm(exp -2), calculated for high spectral resolution surface reflectance from 0.3 to 2.5 micrometers for deciduous vegetation surface). The corrections of the spectral gaps in the vegetation spectrum in the near infrared, again missed by the MODIS reflectances, also contributes to improving TOA DRF calculations but to a much lower extent (less than 0.27Wm(exp -2), or about 4% of the instantaneous DRF). Compared to traditional approaches, MEVA also improves the accuracy of the outgoing solar flux between 0.3 to 2.5 micrometers at TOA by over 60Wm(exp -2) (for aspen 3 surface) and aerosol DRF by over 10Wm(exp -2) (for dry grass). Specifically, for Amazon vegetation types, MEVA can improve the accuracy of daily averaged aerosol radiative forcing in the spectral range of 0.3 to 2.5 micrometers at equator at the equinox by 3.7Wm(exp -2). These improvements indicate that MEVA can contribute to regional climate studies over vegetated areas and can help to improve remote sensing-based studies of climate processes and climate change.

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing.

PubMed

Richardson, Andrew D; Hufkens, Koen; Milliman, Tom; Frolking, Steve

2018-04-09

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both "greenness rising" and "greenness falling" transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011

PubMed Central

Zhang, Yangjian; Dong, Jinwei; Xiao, Xiangming

2013-01-01

As the Earth’s third pole, the Tibetan Plateau has experienced a pronounced warming in the past decades. Recent studies reported that the start of the vegetation growing season (SOS) in the Plateau showed an advancing trend from 1982 to the late 1990s and a delay from the late 1990s to 2006. However, the findings regarding the SOS delay in the later period have been questioned, and the reasons causing the delay remain unknown. Here we explored the alpine vegetation SOS in the Plateau from 1982 to 2011 by integrating three long-term time-series datasets of Normalized Difference Vegetation Index (NDVI): Global Inventory Modeling and Mapping Studies (GIMMS, 1982–2006), SPOT VEGETATION (SPOT-VGT, 1998–2011), and Moderate Resolution Imaging Spectroradiometer (MODIS, 2000–2011). We found GIMMS NDVI in 2001–2006 differed substantially from SPOT-VGT and MODIS NDVIs and may have severe data quality issues in most parts of the western Plateau. By merging GIMMS-based SOSs from 1982 to 2000 with SPOT-VGT–based SOSs from 2001 to 2011 we found the alpine vegetation SOS in the Plateau experienced a continuous advancing trend at a rate of ∼1.04 d·y−1 from 1982 to 2011, which was consistent with observed warming in springs and winters. The satellite-derived SOSs were proven to be reliable with observed phenology data at 18 sites from 2003 to 2011; however, comparison of their trends was inconclusive due to the limited temporal coverage of the observed data. Longer-term observed data are still needed to validate the phenology trend in the future. PMID:23440201

Scaling Estimates of Vegetation Structure in Amazonian Tropical Forests Using Multi-Angle MODIS Observations

NASA Technical Reports Server (NTRS)

Mendes De Moura, Yhasmin; Hilker, Thomas; Goncalves, Fabio Guimaraes; Galvao, Lenio Soares; Roberto dos Santos, Joao; Lyapustin, Alexei; Maeda, Eduardo Eiji; de Jesus Silva, Camila Valeria

2016-01-01

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r(exp 2)= 0.54, RMSE= 0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy(0.52 less than or equal to r(exp 2) less than or equal to 0.61; p less than 0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (sigma(sup 0)) from SeaWinds/QuikSCAT presented an r(exp 2) of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.

Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

Simulating Carbon Flux Dynamics with the Product of PAR Absorbed by Chlorophyll (fAPARchl)

NASA Astrophysics Data System (ADS)

Yao, T.; Zhang, Q.

2016-12-01

A common way to estimate the gross primary production (GPP) is to use the fraction of photosynthetically radiation (PAR) absorbed by vegetation (FPAR). However, only the PAR absorbed by chlorophyll of the canopy, not the PAR absorbed by the foliage or by the entire canopy, is used for photosynthesis. MODIS fAPARchl product, which refers to the fraction of PAR absorbed by chlorophyll of the canopy, is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance by using an advanced leaf-canopy-soil-water-snow coupled radiative transfer model PROSAIL4. PROSAIL4 can retrieve surface water cover fraction, snow cover fraction, and physiologically active canopy chemistry components (chlorophyll concentration and water content), fraction of photosynthetically active radiation (PAR) absorbed by a canopy (fAPARcanopy), fraction of PAR absorbed by photosynthetic vegetation (PV) component (mainly chlorophyll) throughout the canopy (fAPARPV, i.e., fAPARchl) and fraction of PAR absorbed by non-photosynthetic vegetation (NPV) component of the canopy (fAPARNPV). We have successfully retrieved these vegetation parameters for selected areas with PROSAIL4 and the MODIS images, or simulated spectrally MODIS-like images. In this study, the product of PAR absorbed by chlorophyll (fAPARchl) has been used to simulate carbon flux over different kinds of vegetation types. The results show that MODIS fAPARchl product has the ability to better characterize phenology than current phenology model in the Community Land Model and it also will likely be able to increase the accuracy of carbon fluxes simulations.

Comparing MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers

NASA Astrophysics Data System (ADS)

Rankine, C.; Sánchez-Azofeifa, G. A.; Guzmán, J. Antonio; Espirito-Santo, M. M.; Sharp, Iain

2017-10-01

Tropical dry forests (TDFs) present strong seasonal greenness signals ideal for tracking phenology and primary productivity using remote sensing techniques. The tightly synchronized relationship these ecosystems have with water availability offer a valuable natural experiment for observing the complex interactions between the atmosphere and the biosphere in the tropics. To investigate how well the MODIS vegetation indices (normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI)) represented the phenology of different successional stages of naturally regenerating TDFs, within a widely conserved forest fragment in the semi-arid southeast of Brazil, we installed several canopy towers with radiometric sensors to produce high temporal resolution near-surface vegetation greenness indices. Direct comparison of several years of ground measurements with a combined Aqua/Terra 8 day satellite product showed similar broad temporal trends, but MODIS often suffered from cloud contamination during the onset of the growing season and occasionally during the peak growing season. The strength of the in-situ and MODIS linear relationship was greater for NDVI than for EVI across sites but varied with forest stand age. Furthermore, we describe the onset dates and duration of canopy development phases for three years of in-situ monitoring. A seasonality analysis revealed significant discrepancies between tower and MODIS phenology transitions dates, with up to five weeks differences in growing season length estimation. Our results indicate that 8 and 16 day MODIS satellite vegetation monitoring products are suitable for tracking general patterns of tropical dry forest phenology in this region but are not temporally sufficient to characterize inter-annual differences in phenology phase onset dates or changes in productivity due to mid-season droughts. Such rapid transitions in canopy greenness are important indicators of climate change sensitivity of these already endangered forest ecosystems and should be further monitored using both ground and satellite approaches.

Monitoring start of season in Alaska with GLOBE, AVHRR, and MODIS data

NASA Astrophysics Data System (ADS)

Robin, Jessica; Dubayah, Ralph; Sparrow, Elena; Levine, Elissa

2008-03-01

This work evaluates whether continuity between Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) is achievable for monitoring phenological changes in Alaska. This work also evaluates whether NDVI can detect changes in start of the growing season (SOS) in this region. Six quadratic regression models with NDVI as a function of accumulated growing degree days (AGDD) were developed from 2001 through 2004 AVHRR and MODIS NDVI data sets for urban, mixed, and forested land covers. Model parameters determined NDVI values for start of the observational period as well as peak and length of the growing season. NDVI values for start of the growing season were determined from the model equations and field observations of SOS made by GLOBE students and researchers at University of Alaska Fairbanks. AGDD was computed from daily air temperature. AVHRR and MODIS models were significantly different from one another with differences in the start of the observational season as well as start, peak, and length of the growing season. Furthermore, AGDD for SOS was significantly lower during the 1990s than the 1980s. NDVI values at SOS did not detect this change. There are limitations with using NDVI to monitor phenological changes in these regions because of snow, the large extent of conifers, and clouds, which restrict the composite period. In addition, differing processing and spectral characteristics restrict continuity between AVHRR and MODIS NDVI data sets.

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

USGS Publications Warehouse

Cheng, X.; Vierling, Lee; Deering, D.; Conley, A.

2005-01-01

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r 2 = 0.61 and r 2 = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r 2 = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.

Estimating photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions using Landsat and MODIS data: Effects of site heterogeneity, soil properties and land cover

NASA Astrophysics Data System (ADS)

Guerschman, J. P.; Scarth, P.; McVicar, T.; Malthus, T. J.; Stewart, J.; Rickards, J.; Trevithick, R.; Renzullo, L. J.

2013-12-01

Vegetation fractional cover is a key indicator for land management monitoring, both in pastoral and agricultural settings. Maintaining adequate vegetation cover protects the soil from the effects of water and wind erosion and also ensures that carbon is returned to soil through decomposition. Monitoring vegetation fractional cover across large areas and continuously in time needs good remote sensing techniques underpinned by high quality ground data to calibrate and validate algorithms. In this study we used Landsat and MODIS reflectance data together with field measurements from 1476 observations across Australia to produce estimates of vegetation fractional cover using a linear unmixing technique. Specifically, we aimed at separating fractions of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (B). We used Landsat reflectance averaged over a 3x3 pixel window representing the area actually measured on the ground and also a 'degraded' Landsat reflectance 40x40 pixel window to simulate the effect of a coarser sensor. Using these two Landsat reflectances we quantified the heterogeneity of each site. We used data from two MODIS-derived reflectance products: the Nadir BRDF-Adjusted surface Reflectance product (MCD43A4) and the MODIS 8-day surface reflectance (MOD09A1). We derived endmembers from the data and estimated fractional cover using a linear unmixing technique. Log transforms and band interaction terms were added to account for non-linearities in the spectral mixing. For each reflectance source we investigated if the residuals were correlated with site heterogeneity, soil colour, soil moisture and land cover type. As expected, the best model was obtained when Landsat data for a small region around each site was used. We obtained root mean square error (RMSE) values of 0.134, 0.175 and 0.153 for PV, NPV and B respectively. When we degraded the Landsat data to an area of ~1 km2 around each site the model performance decreased to RMSE of 0.142, 0.181 and 0.166 for PV, NPV and B. Using MODIS reflectance data (from the MCD43A4 and MOD09A1 products) we obtained similar results as when using the 'degraded' Landsat reflectance, with no significant differences between them. Model performance decreased (i.e. RMSE increased) with site heterogeneity when coarse resolution reflectance data was used. We did not find any evidence of soil colour or moisture influence on model performance. We speculate that the unmixing models may be insensitive to soil colour and/or that the soil moisture in the top few millimetres of soil, which influence reflectance in optical sensors, is decoupled from the soil moisture in the top layer (i.e. a few cm) as measured by passive microwave sensors or estimated by models. The models tended to overestimate PV in cropping areas, possibly due to a strong red/ near infrared signal in homogeneous crops which do not have a high green cover. This study sets the basis for an operational Landsat/ MODIS combined product which would benefit users with varying requirements of spatial, temporal resolution and latency and could potentially be applied to other regions in the world.

Dynamics of Vegetatin Indices in Tropical and Subtropical Savannas Defined by Ecoregions and Moderate Resolution Imaging Spectoradiometer (MODIS) Land Cover

NASA Technical Reports Server (NTRS)

Hill, Michael J.; Roman, Miguel O.; Schaaf, Crytal B.

2011-01-01

In this study, we explored the capacity of vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products to characterize global savannas in Australia, Africa and South America. The savannas were spatially defined and subdivided using the World Wildlife Fund (WWF) global ecoregions and MODIS land cover classes. Average annual profiles of Normalized Difference Vegetation Index, shortwave infrared ratio (SWIR32), White Sky Albedo (WSA) and the Structural Scattering Index (SSI) were created. Metrics derived from average annual profiles of vegetation indices were used to classify savanna ecoregions. The response spaces between vegetation indices were used to examine the potential to derive structural and fractional cover measures. The ecoregions showed distinct temporal profiles and formed groups with similar structural properties, including higher levels of woody vegetation, similar forest savanna mixtures and similar grassland predominance. The potential benefits from the use of combinations of indices to characterize savannas are discussed.

Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Friedl, Mark A.; Schaaf, Crystal B.

2006-12-01

In the last two decades the availability of global remote sensing data sets has provided a new means of studying global patterns and dynamics in vegetation. The vast majority of previous work in this domain has used data from the Advanced Very High Resolution Radiometer, which until recently was the primary source of global land remote sensing data. In recent years, however, a number of new remote sensing data sources have become available that have significantly improved the capability of remote sensing to monitor global ecosystem dynamics. In this paper, we describe recent results using data from NASA's Moderate Resolution Imaging Spectroradiometer to study global vegetation phenology. Using a novel new method based on fitting piecewise logistic models to time series data from MODIS, key transition dates in the annual cycle(s) of vegetation growth can be estimated in an ecologically realistic fashion. Using this method we have produced global maps of seven phenological metrics at 1-km spatial resolution for all ecosystems exhibiting identifiable annual phenologies. These metrics include the date of year for (1) the onset of greenness increase (greenup), (2) the onset of greenness maximum (maturity), (3) the onset of greenness decrease (senescence), and (4) the onset of greenness minimum (dormancy). The three remaining metrics are the growing season minimum, maximum, and summation of the enhanced vegetation index derived from MODIS. Comparison of vegetation phenology retrieved from MODIS with in situ measurements shows that these metrics provide realistic estimates of the four transition dates identified above. More generally, the spatial distribution of phenological metrics estimated from MODIS data is qualitatively realistic, and exhibits strong correspondence with temperature patterns in mid- and high-latitude climates, with rainfall seasonality in seasonally dry climates, and with cropping patterns in agricultural areas.

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

Ulsig, Laura; Nichol, Caroline J.; Huemmrich, Karl F.; Landis, David R.; Middleton, Elizabeth M.; Lyapustin, Alexei I.; Mammarella, Ivan; Levula, Janne; Porcar-Castell, Albert

2017-01-01

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.; Foster, James L.

2009-01-01

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Vegetation Phenology Metrics Derived from Temporally Smoothed and Gap-filled MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeff; Wolfe, Robert; Esaias, Wayne; Gao, Feng; Ederer, Greg; Nightingale, Joanne; Nickeson, Jamie E.; Ma, Pete; Pedely, Jeff

2012-01-01

Smoothed and gap-filled VI provides a good base for estimating vegetation phenology metrics. The TIMESAT software was improved by incorporating the ancillary information from MODIS products. A simple assessment of the association between retrieved greenup dates and ground observations indicates satisfactory result from improved TIMESAT software. One application example shows that mapping Nectar Flow Phenology is tractable on a continental scale using hive weight and satellite vegetation data. The phenology data product is supporting more researches in ecology, climate change fields.

Evaluating the Usefulness of High-Temporal Resolution Vegetation Indices to Identify Crop Types

NASA Astrophysics Data System (ADS)

Hilbert, K.; Lewis, D.; O'Hara, C. G.

2006-12-01

The National Aeronautical and Space Agency (NASA) and the United States Department of Agriculture (USDA) jointly sponsored research covering the 2004 to 2006 South American crop seasons that focused on developing methods for the USDA's Foreign Agricultural Service's (FAS) Production Estimates and Crop Assessment Division (PECAD) to identify crop types using MODIS-derived, hyper-temporal Normalized Difference Vegetation Index (NDVI) images. NDVI images were composited in 8 day intervals from daily NDVI images and aggregated to create a hyper-termporal NDVI layerstack. This NDVI layerstack was used as input to image classification algorithms. Research results indicated that creating high-temporal resolution Normalized Difference Vegetation Index (NDVI) composites from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data products provides useful input to crop type classifications as well as potential useful input for regional crop productivity modeling efforts. A current NASA-sponsored Rapid Prototyping Capability (RPC) experiment will assess the utility of simulated future Visible Infrared Imager / Radiometer Suite (VIIRS) imagery for conducting NDVI-derived land cover and specific crop type classifications. In the experiment, methods will be considered to refine current MODIS data streams, reduce the noise content of the MODIS, and utilize the MODIS data as an input to the VIIRS simulation process. The effort also is being conducted in concert with an ISS project that will further evaluate, verify and validate the usefulness of specific data products to provide remote sensing-derived input for the Sinclair Model a semi-mechanistic model for estimating crop yield. The study area encompasses a large portion of the Pampas region of Argentina--a major world producer of crops such as corn, soybeans, and wheat which makes it a competitor to the US. ITD partnered with researchers at the Center for Surveying Agricultural and Natural Resources (CREAN) of the National University of Cordoba, Argentina, and CREAN personnel collected and continue to collect field-level, GIS-based in situ information. Current efforts involve both developing and optimizing software tools for the necessary data processing. The software includes the Time Series Product Tool (TSPT), Leica's ERDAS Imagine, and Mississippi State University's Temporal Map Algebra computational tools.

Mapping Daily Evapotranspiration based on Spatiotemporal Fusion of ASTER and MODIS Images over Irrigated Agricultural Areas in the Heihe River Basin, Northwest China

NASA Astrophysics Data System (ADS)

Huang, C.; LI, Y.

2017-12-01

Continuous monitoring of daily evapotranspiration (ET) is crucial for allocating and managing water resources in irrigated agricultural areas in arid regions. In this study, continuous daily ET at a 90-m spatial resolution was estimated using the Surface Energy Balance System (SEBS) by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) images with high temporal resolution and Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) images with high spatial resolution. The spatiotemporal characteristics of these sensors were obtained using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The performance of this approach was validated over a heterogeneous oasis-desert region covered by cropland, residential, woodland, water, Gobi desert, sandy desert, desert steppe, and wetland areas using in situ observations from automatic meteorological systems (AMS) and eddy covariance (EC) systems in the middle reaches of the Heihe River Basin in Northwest China. The error introduced during the data fusion process based on STARFM is within an acceptable range for predicted LST at a 90-m spatial resolution. The surface energy fluxes estimated using SEBS based on predicted remotely sensed data that combined the spatiotemporal characteristics of MODIS and ASTER agree well with the surface energy fluxes observed using EC systems for all land cover types, especially for vegetated area with MAP values range from 9% to 15%, which are less than the uncertainty (18%) of the observed in this study area. Time series of daily ET modelled from SEBS were compared to that modelled from PT-JPL (one of Satellite-based Priestley-Taylor ET model) and observations from EC systems. SEBS performed generally better than PT-JPL for vegetated area, especially irrigated cropland with bias, RMSE, and MAP values of 0.29 mm/d, 0.75 mm/d, 13% at maize site, -0.33 mm/d, 0.81 mm/d, and 14% at vegetable sites.

Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images.

Treesearch

Xiangming Xiao; Stephen Hagen; Qingyuan Zhang; Michael Keller; Berrien Moore III

2006-01-01

Leaf phenology of tropical evergreen forests affects carbon and water fluxes. In an earlier study of a seasonally moist evergreen tropical forest site in the Amazon basin, time series data of Enhanced Vegetation Index (EVI) from the VEGETATION and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showed an unexpected seasonal pattern, with higher EVI in the...

Scientific impact of MODIS C5 calibration degradation and C6+ improvements

NASA Astrophysics Data System (ADS)

Lyapustin, A.; Wang, Y.; Xiong, X.; Meister, G.; Platnick, S.; Levy, R.; Franz, B.; Korkin, S.; Hilker, T.; Tucker, J.; Hall, F.; Sellers, P.; Wu, A.; Angal, A.

2014-12-01

The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Ångström exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra-Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1-B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

Scientific Impact of MODIS C5 Calibration Degradation and C6+ Improvements

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Xiong, X.; Meister, G.; Platnick, S.; Levy, R.; Franz, B.; Korkin, S.; Hilker, T.; Tucker, J.;

Vegetation Health and Productivity Indicators for Sustained National Climate Assessments

NASA Astrophysics Data System (ADS)

Jones, M. O.; Running, S. W.

2014-12-01

The National Climate Assessment process is developing a system of physical, ecological, and societal indicators that communicate key aspects of the physical climate, climate impacts, vulnerabilities, and preparedness for the purpose of informing both decision makers and the public. Implementing a 14 year record of Gross and Net Primary Productivity (GPP/NPP) derived from the NASA EOS MODIS satellite sensor we demonstrate how these products can serve as Ecosystem Productivity and Vegetation Health National Climate Indicators for implementation in sustained National Climate Assessments. The NPP product combines MODIS vegetation data with daily global meteorology to calculate annual growth of all plant material at 1 sq. km resolution. NPP anomalies identify regions with above or below average plant growth that may result from climate fluctuations and can inform carbon source/sink dynamics, agricultural and forestry yield measures, and response to wildfire or drought conditions. The GPP product provides a high temporal resolution (8-day) metric of vegetation growth which can be used to monitor short-term vegetation response to extreme events and implemented to derive vegetation phenology metrics; growing season start, end, and length, which can elucidate land cover and regionally specific vegetation responses to a changing climate. The high spatial resolution GPP and NPP indicators can also inform and clarify responses seen from other proposed Pilot Indicators such as forest growth/productivity, land cover, crop production, and phenology. The GPP and NPP data are in continuous production and will be sustained into the future with the next generation satellite missions. The long-term Ecosystem Productivity and Vegetation Health Indicators are ideal for use in sustained National Climate Assessments, providing regionally specific responses to a changing climate and complete coverage at the national scale.

Remote Sensing of Ecosystem Light Use Efficiency Using MODIS

NASA Astrophysics Data System (ADS)

Huemmrich, K. F.; Middleton, E.; Landis, D.; Black, T. A.; Barr, A. G.; McCaughey, J. H.; Hall, F.

2009-12-01

Understanding the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic CO2 uptake by terrestrial vegetation. Optimal photosynthetic function is negatively affected by stress factors that cause down-regulation (i.e., reduced rate of photosynthesis). Present modeling approaches to determine ecosystem carbon exchange rely on meteorological data as inputs to models that predict the relative photosynthetic function in response to environmental conditions inducing stress (e.g., drought, high/low temperatures). This study examines the determination of ecosystem photosynthetic light use efficiency (LUE) from remote sensing, through measurement of vegetation spectral reflectance changes associated with physiologic stress responses exhibited by photosynthetic pigments. This approach uses the Moderate-Resolution Spectroradiometer (MODIS) on Aqua and Terra to provide frequent, narrow-band measurements. The reflective ocean MODIS bands were used to calculate the Photochemical Reflectance Index (PRI), an index that is sensitive to reflectance changes near 531nm associated with vegetation stress responses exhibited by photosynthetic pigments in the xanthophyll cycle. MODIS PRI values were compared with LUE calculated from CO2 flux measured at four Canadian forest sites: A mature Douglas fir site in British Columbia, mature aspen and black spruce sites in Saskatchewan, and a mixed forest site in Ontario, all part of the Canadian Carbon Program network. The relationships between LUE and MODIS PRI were different among forest types, with clear differences in the slopes of the relationships for conifer and deciduous forests. The MODIS based LUE measurements provide a more accurate estimation of observed LUE than the values calculated in the MODIS GPP model. This suggests the possibility of a GPP model that uses MODIS LUE instead of modeled LUE. This type of model may provide a useful contrast to existing models driven by meteorological data. The main impediment to developing such a model is the lack of a MODIS product that provides surface reflectance for the MODIS ocean bands over land.

Application and Evaluation of MODIS LAI, FPAR, and Albedo Products in the WRF/CMAQ System

EPA Science Inventory

MODIS vegetation and albedo products provide a more realistic representation of surface conditions for input to the WRF/CMAQ modeling system. However, the initial evaluation of ingesting MODIS data into the system showed mixed results, with increased bias and error for 2-m temper...

Simulating Visible/Infrared Imager Radiometer Suite Normalized Difference Vegetation Index Data Using Hyperion and MODIS

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; Russell, Jeffrey; Ryan, Robert E.

2006-01-01

The success of MODIS (the Moderate Resolution Imaging Spectrometer) in creating unprecedented, timely, high-quality data for vegetation and other studies has created great anticipation for data from VIIRS (the Visible/Infrared Imager Radiometer Suite). VIIRS will be carried onboard the joint NASA/Department of Defense/National Oceanic and Atmospheric Administration NPP (NPOESS (National Polar-orbiting Operational Environmental Satellite System) Preparatory Project). Because the VIIRS instruments will have lower spatial resolution than the current MODIS instruments 400 m versus 250 m at nadir for the channels used to generate Normalized Difference Vegetation Index data, scientists need the answer to this question: how will the change in resolution affect vegetation studies? By using simulated VIIRS measurements, this question may be answered before the VIIRS instruments are deployed in space. Using simulated VIIRS products, the U.S. Department of Agriculture and other operational agencies can then modify their decision support systems appropriately in preparation for receipt of actual VIIRS data. VIIRS simulations and validations will be based on the ART (Application Research Toolbox), an integrated set of algorithms and models developed in MATLAB(Registerd TradeMark) that enables users to perform a suite of simulations and statistical trade studies on remote sensing systems. Specifically, the ART provides the capability to generate simulated multispectral image products, at various scales, from high spatial hyperspectral and/or multispectral image products. The ART uses acquired ( real ) or synthetic datasets, along with sensor specifications, to create simulated datasets. For existing multispectral sensor systems, the simulated data products are used for comparison, verification, and validation of the simulated system s actual products. VIIRS simulations will be performed using Hyperion and MODIS datasets. The hyperspectral and hyperspatial properties of Hyperion data will be used to produce simulated MODIS and VIIRS products. Hyperion-derived MODIS data will be compared with near-coincident MODIS collects to validate both spectral and spatial synthesis, which will ascertain the accuracy of converting from MODIS to VIIRS. MODIS-derived VIIRS data is needed for global coverage and for the generation of time series for regional and global investigations. These types of simulations will have errors associated with aliasing for some scene types. This study will help quantify these errors and will identify cases where high-quality, MODIS-derived VIIRS data will be available.

Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data

PubMed Central

Scharlemann, Jörn P. W.; Benz, David; Hay, Simon I.; Purse, Bethan V.; Tatem, Andrew J.; Wint, G. R. William; Rogers, David J.

2008-01-01

Background Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling. PMID:18183289

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.

PubMed

Scharlemann, Jörn P W; Benz, David; Hay, Simon I; Purse, Bethan V; Tatem, Andrew J; Wint, G R William; Rogers, David J

2008-01-09

Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amazônicos Savanna Vegetation Enclave (Brazilian Amazon).

PubMed

Alves, Daniel Borini; Pérez-Cabello, Fernando

2017-12-01

Fire activity plays an important role in the past, present and future of Earth system behavior. Monitoring and assessing spatial and temporal fire dynamics have a fundamental relevance in the understanding of ecological processes and the human impacts on different landscapes and multiple spatial scales. This work analyzes the spatio-temporal distribution of burned areas in one of the biggest savanna vegetation enclaves in the southern Brazilian Amazon, from 2000 to 2016, deriving information from multiple remote sensing data sources (Landsat and MODIS surface reflectance, TRMM pluviometry and Vegetation Continuous Field tree cover layers). A fire scars database with 30 m spatial resolution was generated using a Landsat time series. MODIS daily surface reflectance was used for accurate dating of the fire scars. TRMM pluviometry data were analyzed to dynamically establish time limits of the yearly dry season and burning periods. Burned area extent, frequency and recurrence were quantified comparing the results annually/seasonally. Additionally, Vegetation Continuous Field tree cover layers were used to analyze fire incidence over different types of tree cover domains. In the last seventeen years, 1.03millionha were burned within the study area, distributed across 1432 fire occurrences, highlighting 2005, 2010 and 2014 as the most affected years. Middle dry season fires represent 86.21% of the total burned areas and 32.05% of fire occurrences, affecting larger amount of higher density tree surfaces than other burning periods. The results provide new insights into the analysis of burned areas of the neotropical savannas, spatially and statistically reinforcing important aspects linked to the seasonality patterns of fire incidence in this landscape. Copyright © 2017 Elsevier B.V. All rights reserved.

Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.

Analysis of Post-Fire Vegetation Recovery in the Mediterranean Basin using MODIS Derived Vegetation Indices

NASA Astrophysics Data System (ADS)

Hawtree, Daniel; San Miguel, Jesus; Sedano, Fernando; Kempeneers, Pieter

2010-05-01

The Mediterranean basin region is highly susceptible to wildfire, with approximately 60,000 individual fires and half a million ha of natural vegetation burnt per year. Of particular concern in this region is the impact of repeated wildfires on the ability of natural lands to return to a pre-fire state, and of the possibility of desertification of semi-arid areas. Given these concerns, understanding the temporal patterns of vegetation recovery is important for the management of environmental resources in the region. A valuable tool for evaluating these recovery patterns are vegetation indices derived from remote sensing data. Previous research on post-fire vegetation recovery conducted in this region has found significant variability in recovery times across different study sites. It is unclear what the primary variables are affecting the differences in the rates of recovery, and if any geographic patterns of behavior exist across the Mediterranean basin. This research has primarily been conducted using indices derived from Landsat imagery. However, no extensive analysis of vegetation regeneration for large regions has been published, and assessment of vegetation recovery on the basis of medium-spatial resolution imagery such as that of MODIS has not yet been analyzed. This study examines the temporal pattern of vegetation recovery in a number of fire sites in the Mediterranean basin, using data derived from MODIS 16 -day composite vegetation indices. The intent is to develop a more complete picture of the temporal sequence of vegetation recovery, and to evaluate what additional factors impact variations in the recovery sequence. In addition, this study evaluates the utility of using MODIS derived vegetation indices for regeneration studies, and compares the findings to earlier studies which rely on Landsat data. Wildfires occurring between the years 2000 and 2004 were considered as potential study sites for this research. Using the EFFIS dataset, all wildfires covering an area of at least 1,000 ha were identified. The land-cover / land-use of these large fires sites were then evaluated using the CORINE land-cover data set, and the sites dominated primarily by natural vegetation were identified. Once these candidate sites were identified, a subset was selected across a range of locations and site characteristics for post-fire recovery analysis. To evaluate the post-fire recovery sequence in these locations, time-series of NDVI, EVI, and LAI were derived using 250 meter resolution MODIS data (MOD13Q). The vegetation index values were then compared to pre-fire values to determine recovery relative to the pre-fire vegetative state. The variability in rates of recovery are then considered with respect to moisture availability, vegetation type, and local site conditions to evaluate if any patterns of recovery can be determined.

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

NASA Technical Reports Server (NTRS)

Shuai, Yanmin; Schaaf, Crystal; Zhang, Xiaoyang; Strahler, Alan; Roy, David; Morisette, Jeffrey; Wang, Zhuosen; Nightingale, Joanne; Nickeson, Jaime; Richardson, Andrew D.;

Relationship Between Surface Reflectance in the Visible and Mid-IR used in MODIS Aerosol Algorithm-Theory

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Gobron, Nadine; Pinty, Bernard; Widlowski, Jean-Luc; Verstraete, Michel M.; Lau, William K. M. (Technical Monitor)

2002-01-01

Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument that flies in polar orbit on the Terra platform, are used to derive the aerosol optical thickness and properties over land and ocean. The relationships between visible reflectance (at blue, rho(sub blue), and red, rho(sub red)) and mid-infrared (at 2.1 microns, rho(sub 2.1)) are used in the MODIS aerosol retrieval algorithm to derive global distribution of aerosols over the land. These relations have been established from a series of measurements indicating that rho(sub blue) is approximately 0.5 rho(sub red) is approximately 0.25 rho(sub 2.1). Here we use a model to describe the transfer of radiation through a vegetation canopy composed of randomly oriented leaves to assess the theoretical foundations for these relationships. Calculations for a wide range of leaf area indices and vegetation fractions show that rho(sub blue) is consistently about 1/4 of rho(sub 2.1) as used by MODIS for the whole range of analyzed cases, except for very dark soils, such as those found in burn scars. For its part, the ratio rho(sub red)/rho(sub 2.1) varies from less than the empirically derived value of 1/2 for dense and dark vegetation, to more than 1/2 for bright mixture of soil and vegetation. This is in agreement with measurements over uniform dense vegetation, but not with measurements over mixed dark scenes. In the later case the discrepancy is probably mitigated by shadows due to uneven canopy and terrain on a large scale. It is concluded that the value of this ratio should ideally be made dependent on the land cover type in the operational processing of MODIS data, especially over dense forests.

Deriving Vegetation Dynamics of Natural Terrestrial Ecosystems from MODIS NDVI/EVI Data over Turkey.

PubMed

Evrendilek, Fatih; Gulbeyaz, Onder

2008-09-01

The 16-day composite MODIS vegetation indices (VIs) at 500-m resolution for the period between 2000 to 2007 were seasonally averaged on the basis of the estimated distribution of 16 potential natural terrestrial ecosystems (NTEs) across Turkey. Graphical and statistical analyses of the time-series VIs for the NTEs spatially disaggregated in terms of biogeoclimate zones and land cover types included descriptive statistics, correlations, discrete Fourier transform (DFT), time-series decomposition, and simple linear regression (SLR) models. Our spatio-temporal analyses revealed that both MODIS VIs, on average, depicted similar seasonal variations for the NTEs, with the NDVI values having higher mean and SD values. The seasonal VIs were most correlated in decreasing order for: barren/sparsely vegetated land > grassland > shrubland/woodland > forest; (sub)nival > warm temperate > alpine > cool temperate > boreal = Mediterranean; and summer > spring > autumn > winter. Most pronounced differences between the MODIS VI responses over Turkey occurred in boreal and Mediterranean climate zones and forests, and in winter (the senescence phase of the growing season). Our results showed the potential of the time-series MODIS VI datasets in the estimation and monitoring of seasonal and interannual ecosystem dynamics over Turkey that needs to be further improved and refined through systematic and extensive field measurements and validations across various biomes.

The contribution of brown vegetation to vegetation dynamics

USDA-ARS?s Scientific Manuscript database

Indices of vegetation dynamics that include both green vegetation (GV) and non-photosynthetic vegetation (NPV), that is, brown vegetation, were applied to MODIS surface reflectance data from 2000 to 2006 for the southwestern United States. These indices reveal that the cover of NPV, a measure of veg...

A MODIS-based begetation index climatology

USDA-ARS?s Scientific Manuscript database

Passive microwave soil moisture algorithms must account for vegetation attenuation of the signal in the retrieval process. One approach to accounting for vegetation is to use vegetation indices such as the Normalized Difference Vegetation Index (NDVI) to estimate the vegetation optical depth. The pa...

Monitoring Phenology as Indicator for Timing of Nutrient Inputs in Northern Gulf Watersheds

DTIC Science & Technology

2010-06-01

region and compared to nutrient monitoring data. A. Image Data This project uses MODIS normalized difference vegetation index ( NDVI ) to create a time...series of land vegetation canopies. MODIS provides a near-daily repeat time for the elimination of cloud contamination, and NDVI has been widely adopted...steps and NDVI was calculated by the defined formula NDVI = (near-infrared reflectance - red reflectance) / (near-infrared reflectance + red

Theoretical Basis for the Surface Spectral Reflectance Relationships Used in the MODIS Aerosol Algorithm

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Gobron, Nadine; Pinty, Bernard; Widlowski, Jean-Luc; Verstraete, Michel M.; Lau, William K. M. (Technical Monitor)

2001-01-01

The analysis of data from the MODIS instrument on the Terra platform to derive global distribution of aerosols assumes a set of relationships between the blue, rho (sub blue), the red, rho (sub red), and 2.1 micrometers, rho (sub 2.1), spectral channels. These relations have been established from a series of measurements indicating that rho (sub blue) approximately 0.5 rho (sub red) approximately 0.25 rho (sub 2.1). Here we use a model to describe the transfer of radiation through a vegetation canopy composed of randomly oriented leaves to assess the theoretical foundations for these relationships. The influence of varying fractional vegetation coverage is simulated simply as a linear combination of pure soil and pure vegetation conditions, also known as Independent Pixel Approximation (IPA). Calculations for a wide range of leaf area indices and vegetation fractions show that rho (sub blue) is consistently about 1/4 of rho (sub 2.1) as used by MODIS for the whole range of analyzed cases, except for very dark soils, such as those found in burn scars. For its part, the ratio rho (sub red)/rho (sub 2.1) varies from less than the empirically derived value of 1/2 for dense and dark vegetation (rho (sub 2.1) less than 0.1), to more than 1/2 for bright mixture of soil and vegetation. This is in agreement with measurements over uniform dense vegetation, but not with measurements over mixed dark scenes. In the later case, the discrepancy is probably mitigated by shadows due to uneven canopy and terrain on a large scale. It is concluded that the value of this ratio should ideally be made dependent on the land cover type in the operational processing of MODIS data, especially over dense forests.

Evaluation of MODIS aerosol optical depth for semi­-arid environments in complex terrain

NASA Astrophysics Data System (ADS)

Holmes, H.; Loria Salazar, S. M.; Panorska, A. K.; Arnott, W. P.; Barnard, J.

2015-12-01

The use of satellite remote sensing to estimate spatially resolved ground level air pollutant concentrations is increasing due to advancements in remote sensing technology and the limited number of surface observations. Satellite retrievals provide global, spatiotemporal air quality information and are used to track plumes, estimate human exposures, model emissions, and determine sources (i.e., natural versus anthropogenic) in regulatory applications. Ground level PM2.5 concentrations can be estimated using columnar aerosol optical depth (AOD) from MODIS, where the satellite retrieval serves as a spatial surrogate to simulate surface PM2.5 gradients. The spatial statistical models and MODIS AOD retrieval algorithms have been evaluated for the dark, vegetated eastern US, while the semi-arid western US continues to be an understudied region with associated complexity due to heterogeneous emissions, smoke from wildfires, and complex terrain. The objective of this work is to evaluate the uncertainty of MODIS AOD retrievals by comparing with columnar AOD and surface PM2.5 measurements from AERONET and EPA networks. Data is analyzed from multiple stations in California and Nevada for three years where four major wildfires occurred. Results indicate that MODIS retrievals fail to estimate column-integrated aerosol pollution in the summer months. This is further investigated by quantifying the statistical relationships between MODIS AOD, AERONET AOD, and surface PM2.5 concentrations. Data analysis indicates that the distribution of MODIS AOD is significantly (p<0.05) different than AERONET AOD. Further, using the results of distributional and association analysis the impacts of MODIS AOD uncertainties on the spatial gradients are evaluated. Additionally, the relationships between these uncertainties and physical parameters in the retrieval algorithm (e.g., surface reflectance, Ångström Extinction Exponent) are discussed.

Mapping wildfire susceptibility in Southern California using live and dead fractions of vegetation derived from Multiple Endmember Spectral Mixture Analysis of MODIS imagery

NASA Astrophysics Data System (ADS)

Schneider, P.; Roberts, D. A.

2008-12-01

Wildfire is a significant natural disturbance mechanism in Southern California. Assessing spatial patterns of wildfire susceptibility requires estimates of the live and dead fractions of vegetation. The Fire Potential Index (FPI), which is currently the only operationally computed fire susceptibility index incorporating remote sensing data, estimates such fractions using a relative greenness measure based on time series of vegetation index images. This contribution assesses the potential of Multiple Endmember Spectral Mixture Analysis (MESMA) for deriving such fractions from single MODIS images without the need for a long remote sensing time series, and investigates the applicability of such MESMA-derived fractions for mapping dynamic fire susceptibility in Southern California. Endmembers for MESMA were selected from a library of reference endmembers using Constrained Reference Endmember Selection (CRES), which uses field estimates of fractions to guide the selection process. Fraction images of green vegetation, non-photosynthetic vegetation, soil, and shade were then computed for all available 16-day MODIS composites between 2000 and 2006 using MESMA. Initial results indicate that MESMA of MODIS imagery is capable of providing reliable estimates of live and dead vegetation fraction. Validation against in situ observations in the Santa Ynez Mountains near Santa Barbara, California, shows that the average fraction error for two tested species was around 10%. Further validation of MODIS-derived fractions was performed against fractions from high-resolution hyperspectral data. It was shown that the fractions derived from data of both sensors correlate with R2 values greater than 0.95. MESMA-derived live and dead vegetation fractions were subsequently tested as a substitute to relative greenness in the FPI algorithm. FPI was computed for every day between 2000 and 2006 using the derived fractions. Model performance was then tested by extracting FPI values for historical fire events and random no-fire events in Southern California for the same period and developing a logistic regression model. Preliminary results show that an FPI based on MESMA-derived fractions has the potential to deliver similar performance as the traditional FPI but requiring a greatly reduced data volume and using an approach based on physical rather than empirical relationships.

Retrieval of canopy moisture content for dynamic fire risk assessment using simulated MODIS bands

NASA Astrophysics Data System (ADS)

Maffei, Carmine; Leone, Antonio P.; Meoli, Giuseppe; Calabrò, Gaetano; Menenti, Massimo

2007-10-01

Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation of intervention resources. Various factors contribute to the quantification of fire hazard, and among them vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements might be problematic, since it is calculated from two biophysical properties that independently affect vegetation reflectance spectrum. The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer (MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC. However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared (SWIR) channels to improve the retrieval accuracy of FMC (r2 = 0.73). The effects of canopy structural properties on MODIS estimates of FMC have been evaluated, and it has been found that the limiting factor is leaf area index (LAI). The best results are recorded for LAI>2 (r2 = 0.83), while acceptable results (r2 = 0.58) can still be achieved for lower vegetation cover density.

Object-based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

NASA Technical Reports Server (NTRS)

Ransom, Kenneth J.; Montesano, Paul M.; Nelson, Ross F.

2011-01-01

The circumpolar taiga-tundra ecotone was delineated using an image segmentation based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 - 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation grouped pixels representing similar tree cover into polygonal features (objects) that form the map of the transition zone. Eachfeature represents an area much larger than the 500m MODIS pixel to characterize thepatterns of sparse forest patches on a regional scale. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1deg longitudinal interval in North America and Eurasia and (2) Landsat-derived Canadianproportion of forest cover for Canada. The adjusted TCC from MODIS VCF shows, on average, greater than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values between 5-20% , or (2) mean adjusted TCC values <5% but with a standard deviation > 5% were used to identify the ecotone.

Tracking Trends in Fractional Forest Cover Change using Long Term Data from AVHRR and MODIS

NASA Astrophysics Data System (ADS)

Kim, D. H.; DiMiceli, C.; Sohlberg, R. A.; Hansen, M.; Carroll, M.; Kelly, M.; Townshend, J. R.

2014-12-01

Tree cover affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes. Accurate and long-term continuous observation of tree cover change is critical for the study of the gradual ecosystem change. Tree cover is most commonly inferred from categorical maps which may inadequately represent within-class heterogeneity for many analyses. Alternatively, Vegetation Continuous Fields data measures fractions or proportions of pixel area. Recent development in remote sensing data processing and cross sensor calibration techniques enabled the continuous, long-term observations such as Land Long-Term Data Records. Such data products and their surface reflectance data have enhanced the possibilities for long term Vegetation Continuous Fields data, thus enabling the estimation of long term trend of fractional forest cover change. In this presentation, we will summarize the progress in algorithm development including automation of training selection for deciduous and evergreen forest, the preliminary results, and its future applications to relate trends in fractional forest cover change and environmental change.

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Bell, Jordan R.; LaFontaine, Frank J.; Peters-Lidard, Christa D.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Monitoring Coastal Marshes for Persistent Saltwater Intrusion

DTIC Science & Technology

2010-06-01

for the normalized difference indices (vegetation, soil, and water– NDVI , NDSI, and NDWI) for both MODIS and Landsat 5 and 7, referred to as the...Normalized Difference Index transformation [4]. The MODIS indices are 250 m ( NDVI ) and 500 m (NDWI and NDSI), and the Landsat indices are 30 m...indices are shown for two locations in Fig. 1 and Fig 2. Each figure shows the NDSI (soil), NDVI (vegetation), and NDWI (water) index as a function of

VIS and NIR land surface albedo sensitivity of the Ent Terrestrial Biosphere Model to forcing leaf area index

NASA Astrophysics Data System (ADS)

Montes, C.; Kiang, N. Y.; Ni-Meister, W.; Yang, W.; Schaaf, C.; Aleinov, I. D.; Jonas, J.; Zhao, F. A.; Yao, T.; Wang, Z.; Sun, Q.; Carrer, D.

2016-12-01

Land surface albedo is a major controlling factor in vegetation-atmosphere transfers, modifying the components of the energy budget, the ecosystem productivity and patterns of regional and global climate. General Circulation Models (GCMs) are coupled to Dynamic Global Vegetation Models (DGVMs) to solve vegetation albedo by using simple schemes prescribing albedo based on vegetation classification, and approximations of canopy radiation transport for multiple plant functional types (PFTs). In this work, we aim at evaluating the sensitivity of the NASA Ent Terrestrial Biosphere Model (TBM), a demographic DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM, in estimating VIS and NIR surface albedo by using variable forcing leaf area index (LAI). The Ent TBM utilizes a new Global Vegetation Structure Dataset (GVSD) to account for geographically varying vegetation tree heights and densities, as boundary conditions to the gap-probability based Analytical Clumped Two-Stream (ACTS) canopy radiative transfer scheme (Ni-Meister et al., 2010). Land surface and vegetation characteristics for the Ent GVSD are obtained from a number of earth observation platforms and algorithms, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), soil albedo derived from MODIS (Carrer et al., 2014), and vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014). Three LAI products are used as input to ACTS/Ent TBM: MODIS MOD15A2H product (Yang et al., 2006), Beijing Normal University LAI (Yuan et al., 2011), and Global Data Sets of Vegetation (LAI3g) (Zhu et al. 2013). The sensitivity of the Ent TBM VIS and NIR albedo to the three LAI products is assessed, compared against the previous GISS GCM vegetation classification and prescribed Lambertian albedoes (Matthews, 1984), and against MODIS snow-free black-sky and white-sky albedo estimates. In addition, we test the sensitivity of the Ent/ACTS albedo to different sets of leaf spectral albedos derived from the literature.

Crop Surveillance Demonstration Using a Near-Daily MODIS Derived Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Ryan, Robert E.; Blonski, Slawomir; Prados, Don

2005-01-01

Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.

Monitoring vegetation phenology using MODIS

USGS Publications Warehouse

Zhang, Xiayong; Friedl, Mark A.; Schaaf, Crystal B.; Strahler, Alan H.; Hodges, John C.F.; Gao, Feng; Reed, Bradley C.; Huete, Alfredo

2003-01-01

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.

Analysis of MODIS 250 m Time Series Product for LULC Classification and Retrieval of Crop Biophysical Parameter

NASA Astrophysics Data System (ADS)

Verma, A. K.; Garg, P. K.; Prasad, K. S. H.; Dadhwal, V. K.

2016-12-01

Agriculture is a backbone of Indian economy, providing livelihood to about 70% of the population. The primary objective of this research is to investigate the general applicability of time-series MODIS 250m Normalized difference vegetation index (NDVI) and Enhanced vegetation index (EVI) data for various Land use/Land cover (LULC) classification. The other objective is the retrieval of crop biophysical parameter using MODIS 250m resolution data. The Uttar Pradesh state of India is selected for this research work. A field study of 38 farms was conducted during entire crop season of the year 2015 to evaluate the applicability of MODIS 8-day, 250m resolution composite images for assessment of crop condition. The spectroradiometer is used for ground reflectance and the AccuPAR LP-80 Ceptometer is used to measure the agricultural crops Leaf Area Index (LAI). The AccuPAR measures Photosynthetically Active Radiation (PAR) and can invert these readings to give LAI for plant canopy. Ground-based canopy reflectance and LAI were used to calibrate a radiative transfer model to create look-up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS-derived LAI was used to find crop parameter by adjusting the LAI simulated from climate-based crop yield model. Cloud free MODIS images of 250m resolution (16 day composite period) were downloaded using LP-DAAC website over a period of 12 months (Jan to Dec 2015). MODIS both the VI products were found to have sufficient spectral, spatial and temporal resolution to detect unique signatures for each class (water, fallow land, urban, dense vegetation, orchard, sugarcane and other crops). Ground truth data were collected using JUNO GPS. Multi-temporal VI signatures for vegetation classes were consistent with its general phenological characteristic and were spectrally separable at some point during the growing season. The MODIS NDVI and EVI multi-temporal images tracked similar seasonal responses for all croplands and were highly correlated across the growing season. The confusion matrix method is used for accuracy assessment and reference data which has been taken during the field visit. Total 520 pixels have been selected for various classes to determine the accuracy. The classification accuracy and kappa coefficient is found to be 79.76% and 0.78 respectively.

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

NASA Astrophysics Data System (ADS)

Wang, Zhuosen; Schaaf, Crystal B.; Sun, Qingsong; Kim, JiHyun; Erb, Angela M.; Gao, Feng; Román, Miguel O.; Yang, Yun; Petroy, Shelley; Taylor, Jeffrey R.; Masek, Jeffrey G.; Morisette, Jeffrey T.; Zhang, Xiaoyang; Papuga, Shirley A.

2017-07-01

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

USGS Publications Warehouse

Wang, Zhuosen; Schaaf, Crystal B.; Sun, Qingson; Kim, JiHyun; Erb, Angela M.; Gao, Feng; Roman, Miguel O.; Yang, Yun; Petroy, Shelley; Taylor, Jeffrey; Masek, Jeffrey G.; Morisette, Jeffrey T.; Zhang, Xiaoyang; Papuga, Shirley A.

2017-01-01

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Monitoring Land Surface Albedo and Vegetation Dynamics Using High Spatial and Temporal Resolution Synthetic Time Series from Landsat and the MODIS BRDF/NBAR/Albedo Product

NASA Technical Reports Server (NTRS)

Wang, Zhuosen; Schaaf, Crystal B.; Sun, Quingsong; Kim, Jihyun; Erb, Angela M.; Gao, Feng; Roman, Miguel O.; Yang, Yun; Petroy, Shelley; Taylor, Jeffrey R.;

Correlation between the habitats productivity and species richness (amphibians and reptiles) in Portugal through remote sensed data

NASA Astrophysics Data System (ADS)

Teodoro, A. C.; Sillero, N.; Alves, S.; Duarte, L.

2013-10-01

Several biogeographic theories propose that the species richness depends on the structure and ecosystems diversity. The habitat productivity, a surrogate for these variables, can be evaluated through satellite imagery, namely using vegetation indexes (e.g. NDVI). We analyzed the correlation between species richness (from the Portuguese Atlas of Amphibians and Reptiles) and NDVI (from Landsat, MODIS, and Vegetation images). The species richness database contains more than 80000 records, collected from bibliographic sources (at 1 or 10 km of spatial resolution) and fieldwork sampling stations (recorded with GPS devices). Several study areas were chosen for Landsat images (three subsets), and all Portugal for MODIS and Vegetation images. The Landsat subareas had different climatic and habitat characteristics, located in the north, center and south of Portugal. Different species richness datasets were used depending on the image spatial resolution: data with metric resolution were used for Landsat, and with 1 km resolution, for MODIS and Vegetation images. The NDVI indexes and all the images were calculated/processed in an open source software (Quantum GIS). Several plug-ins were applied in order to automatize several procedures. We did not find any correlation between the species richness of amphibians and reptiles (not even after separating both groups by species of Atlantic and Mediterranean affinity) and the NDVI calculated with Landsat, MODIS and Vegetation images. Our results may fail to find a relationship because as the species richness is not correlated with only one variable (NDVI), and thus other environmental variables must be considered.

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation.

Treesearch

David P. Turner; William D. Ritts; Warren B. Cohen; Stith T. Gower; Maosheng Zhao; Steve W. Running; Steven C. Wofsy; Shawn Urbanski; Allison L. Dunn; J.W. Munger

2003-01-01

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using...

Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Jiang, Zhiben; Gong, Peng; Xu, Bing

2012-08-01

Land cover fractions (LCFs) derived through spectral mixture analysis are useful in understanding sub-pixel information. However, few studies have been conducted on the analysis of time-series LCFs. Although multi-scale comparisons of spectral index, hard classification, and land surface temperature images have received attention, rarely have these approaches been applied to LCFs. This study compared the LCFs derived through Multiple Endmember Spectral Mixture Analysis (MESMA) using the time-series Landsat Thematic Mapper (TM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired in the Poyang Lake area, China between 2004 and 2005. Specifically, we aimed to: (1) propose an approach for optimal endmember (EM) selection in time-series MESMA; (2) understand the trends in time-series LCFs derived from the TM and MODIS data; and (3) examine the trends in the correlation between the bi-scale LCFs derived from the time-series TM and MODIS data. Our results indicated: (1) the EM spectra chosen according to the proposed hierarchical three-step approach (overall, seasonal, and individual) accurately modeled the both the TM and MODIS images; (2) green vegetation (GV) and NPV/soil/impervious surface (N/S/I) classes followed sine curve trends in the overall area, while the two water classes displayed the water level change pattern in the areas primarily covered with wetland vegetation; and (3) GV, N/S/I, and bright water classes indicated a moderately high agreement between the TM and MODIS LCFs in the whole area (adjusted R2 ⩾ 0.6). However, low levels of correlations were found in the areas primarily dominated by wetland vegetation for all land cover classes.

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

PubMed Central

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2016-01-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting. PMID:27667901

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data.

PubMed

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2015-08-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.

The MODIS Vegetation Canopy Water Content product

NASA Astrophysics Data System (ADS)

Ustin, S. L.; Riano, D.; Trombetti, M.

2008-12-01

Vegetation water stress drives wildfire behavior and risk, having important implications for biogeochemical cycling in natural ecosystems, agriculture, and forestry. Water stress limits plant transpiration and carbon gain. The regulation of photosynthesis creates close linkages between the carbon, water, and energy cycles and through metabolism to the nitrogen cycle. We generated systematic weekly CWC estimated for the USA from 2000-2006. MODIS measures the sunlit reflectance of the vegetation in the visible, near-infrared, and shortwave infrared. Radiative transfer models, such as PROSPECT-SAILH, determine how sunlight interacts with plant and soil materials. These models can be applied over a range of scales and ecosystem types. Artificial Neural Networks (ANN) were used to optimize the inversion of these models to determine vegetation water content. We carried out multi-scale validation of the product using field data, airborne and satellite cross-calibration. An Algorithm Theoretical Basis Document (ATBD) of the product is under evaluation by NASA. The CWC product inputs are 1) The MODIS Terra/Aqua surface reflectance product (MOD09A1/MYD09A1) 2) The MODIS land cover map product (MOD12Q1) reclassified to grassland, shrub-land and forest canopies; 3) An ANN trained with PROSPECT-SAILH; 4) A calibration file for each land cover type. The output is an ENVI file with the CWC values. The code is written in Matlab environment and is being adapted to read not only the 8 day MODIS composites, but also daily surface reflectance data. We plan to incorporate the cloud and snow mask and generate as output a geotiff file. Vegetation water content estimates will help predicting linkages between biogeochemical cycles, which will enable further understanding of feedbacks to atmospheric concentrations of greenhouse gases. It will also serve to estimate primary productivity of the biosphere; monitor/assess natural vegetation health related to drought, pollution or diseases; improve irrigation scheduling by reducing over-watering and under-watering. These estimates will also allow researchers to identify wildfire behavior/risk: drives ignition probability and burning efficiency; to be used as an indicator of soil moisture and Leaf Area Index.

Role of MODIS Vegetation Phenology Products in the U.S. for Warn Early Warning System for Forest Threats

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Norman, Steve; Gasser, Gerald; Smoot, James; Kuper, Philip

2012-01-01

U.S. forests occupy approx 751 million acres (approx 1/3 of total land). Several abiotic and biotic damage agents disturb, damage, kill, and/or threaten these forests. Regionally extensive forest disturbances can also threaten human life and property, bio-diversity and water supplies. timely regional forest disturbance monitoring products are needed to aid forest health management work at finer scales. daily MODIS data provide a means to monitor regional forest disturbances on a weekly basis, leveraging vegetation phenology. In response, the USFS and NASA began collaborating in 2006 to develop a Near Real Time (NRT) forest monitoring capability, based on MODIS NDVI data, as part of a national forest threat Early Warning System (EWS).

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

USGS Publications Warehouse

Boyte, Stephen; Wylie, Bruce K.; Rigge, Matthew B.; Dahal, Devendra

2018-01-01

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

Land and cryosphere products from Suomi NPP VIIRS: Overview and status

PubMed Central

Justice, Christopher O; Román, Miguel O; Csiszar, Ivan; Vermote, Eric F; Wolfe, Robert E; Hook, Simon J; Friedl, Mark; Wang, Zhuosen; Schaaf, Crystal B; Miura, Tomoaki; Tschudi, Mark; Riggs, George; Hall, Dorothy K; Lyapustin, Alexei I; Devadiga, Sadashiva; Davidson, Carol; Masuoka, Edward J

2013-01-01

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA’s Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA’s focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team’s evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS. PMID:25821661

Land and cryosphere products from Suomi NPP VIIRS: Overview and status.

PubMed

Justice, Christopher O; Román, Miguel O; Csiszar, Ivan; Vermote, Eric F; Wolfe, Robert E; Hook, Simon J; Friedl, Mark; Wang, Zhuosen; Schaaf, Crystal B; Miura, Tomoaki; Tschudi, Mark; Riggs, George; Hall, Dorothy K; Lyapustin, Alexei I; Devadiga, Sadashiva; Davidson, Carol; Masuoka, Edward J

2013-09-16

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS.

Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data

USGS Publications Warehouse

Gu, Yingxin; Hunt, E.; Wardlow, B.; Basara, J.B.; Brown, Jesslyn F.; Verdin, J.P.

2008-01-01

The evaluation of the relationship between satellite-derived vegetation indices (normalized difference vegetation index and normalized difference water index) and soil moisture improves our understanding of how these indices respond to soil moisture fluctuations. Soil moisture deficits are ultimately tied to drought stress on plants. The diverse terrain and climate of Oklahoma, the extensive soil moisture network of the Oklahoma Mesonet, and satellite-derived indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) provided an opportunity to study correlations between soil moisture and vegetation indices over the 2002-2006 growing seasons. Results showed that the correlation between both indices and the fractional water index (FWI) was highly dependent on land cover heterogeneity and soil type. Sites surrounded by relatively homogeneous vegetation cover with silt loam soils had the highest correlation between the FWI and both vegetation-related indices (r???0.73), while sites with heterogeneous vegetation cover and loam soils had the lowest correlation (r???0.22). Copyright 2008 by the American Geophysical Union.

Comparisons of MODIS vegetation index products with biophysical and flux tower measurements

NASA Astrophysics Data System (ADS)

Sirikul, Natthanich

Vegetation indices (VI) play an important role in studies of global climate and biogeochemical cycles, and are also positively related to many biophysical parameters and satellite products, such as leaf area index (LAI), gross primary production (GPP), land surface water index (LSWI) and land surface temperature (LST). In this study we found that VI's had strong relationships with some biophysical products, such as gross primary production, yet were less well correlated with biophysical structural parameters, such as leaf area index. The relationships between MODIS VI's and biophysical field measured LAI showed poor correlation at semi-arid land and broadleaf forest land cover type whereas cropland showed stronger correlations than the other vegetation types. In addition, the relationship between the enhanced vegetation index (EVI)-LAI and normalized difference vegetation index (NDVI)-LAI did not show significant differences. Comparisons of the relationships between the EVI and NDVI with tower-measured GPP from 11 flux towers in North America, showed that MODIS EVI had much stronger relationships with tower-GPP than did NDVI, and EVI was better correlated with the seasonal dynamics of GPP than was NDVI. In addition, there were no significant differences among the 1x1, 3x3 and 7x7 pixel sample sizes. The comparisons of VIs from the 3 MODIS products from which VI's are generated (Standard VI (MOD13)), Nadir Adjusted Surface Reflectance (NBAR (MOD43)), and Surface Reflectance (MOD09)), showed that MODIS NBAR-EVI (MOD43) was best correlated with GPP compared with the other VI products. In addition, the MODIS VI - tower GPP relationships were significantly improved using NBAR-EVI over the more complex canopy structures, such as the broadleaf and needleleaf forests. The relationship of tower-GPP with other MODIS products would be useful in more thorough characterization of some land cover types in which the VI's have encountered problems. The land surface temperature (LST) product were found useful for empirical estimations of GPP in needleleaf forests, but were not useful for the other land cover types, whereas the land surface water index (LSWI) was more sensitive to noise from snowmelt, ground water table levels, and wet soils than to the canopy moisture levels. Also the MODIS EVI was better correlated with LST than was NDVI. Finally, the cross-site comparisons of GPP and multi-products from MODIS showed that the relationships between EVI and GPP were the strongest while LST and GPP was the weakest. EVI may thus be useful in scaling across landscapes, including heterogeneous ones, for regional estimations of GPP, especially if BRDF effects have been taken into account (such as with the NBAR product). Thus, the relationships of EVI-GPP over space and time would potentially provide much useful information for studies of the global carbon cycle.

Use of MODIS Satellite Data to Evaluate Juniperus spp. Pollen Phenology to Support a Pollen Dispersal Model, PREAM, to Support Public Health Allergy Alerts

NASA Technical Reports Server (NTRS)

Luvall, J. C.; Sprigg, W.; Levetin, E.; Huete, A.; Nickovic, S.; Pejanovic, G. A.; Vukovic, A.; VandeWater, P.; Budge, A.; Hudspeth, W.;

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

USGS Publications Warehouse

Shermeyer, Jacob S.; Haack, Barry N.

2015-01-01

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Monitoring Agricultural Cropping Patterns in the Great Lakes Basin Using MODIS-NDVI Time Series Data

EPA Science Inventory

This research examined changes in agricultural cropping patterns across the Great Lakes Basin (GLB) using the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. Specific research objectives were to characterize the distribut...

Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) Global Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; Scharfen, Greg R.

2000-01-01

Following the 1999 launch of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), the capability exists to produce global snow-cover maps on a daily basis at 500-m resolution. Eight-day composite snow-cover maps will also be available. MODIS snow-cover products are produced at Goddard Space Flight Center and archived and distributed by the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. The products are available in both orbital and gridded formats. An online search and order tool and user-services staff will be available at NSIDC to assist users with the snow products. The snow maps are available at a spatial resolution of 500 m, and 1/4 degree x 1/4 degree spatial resolution, and provide information on sub-pixel (fractional) snow cover. Pre-launch validation work has shown that the MODIS snow-mapping algorithms perform best under conditions of continuous snow cover in low vegetation areas, but can also map snow cover in dense forests. Post-launch validation activities will be performed using field and aircraft measurements from a February 2000 validation mission, as well as from existing satellite-derived snow-cover maps from NOAA and Landsat-7 Enhanced Thematic Mapper Plus (ETM+).

Improving the prediction of African savanna vegetation variables using time series of MODIS products

NASA Astrophysics Data System (ADS)

Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

2017-09-01

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Sea Surface Temperature and Vegetation Index from MODIS

NASA Technical Reports Server (NTRS)

2002-01-01

This is a composite MODIS image showing the 'green wave' of spring in North America and sea surface temperature in the ocean, collected over an 8-day period during the first week in April 2000. On land, the darker green pixels show where the most green foliage is being produced due to photosynthetic activity. Yellows on land show where there is little or no productivity and red is a boundary zone. In the ocean, orange and yellows show warmer waters and blues show colder values. (MODIS Data Type: MODIS-PFM)

BASIC STUDY ON APPLICABILITY OF MODIS DATA FOR VEGETATION MONITORING IN ASHIO AREA

NASA Astrophysics Data System (ADS)

Ikeda, Hirokazu; Todate, Hikaru; Tanaka, Hiroshi; Ota, Tametomo

Ashio Basin was once lushly green until 1800's. However, the forest had been almost lost by copper mine de velopment and forest fire by 1956. From that time on, afforestation has been carried out for over 50 years, and the vegetation is being recovered. Therefore, it is very important to estimate the past afforestation activities, and to propose future directions. There exists an earlier research on vegetation monitoring in Ashio area, but it was performed more than 15 years ago, and used expensive commercial GIS and LANDSAT data. The present study examined a sustainable and inexpensive system with using preferably free data and software. It is shown that, by comparison with aerial photographs and digital national land information, vegetation index (NDVI) by MODIS data, available to download free, are easy to obtain and manipulate, and applicable for vegetation monitoring in Ashio area.

Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence

NASA Astrophysics Data System (ADS)

Gentine, P.; Alemohammad, S. H.

2018-04-01

Solar-induced fluorescence (SIF) observations from space have resulted in major advancements in estimating gross primary productivity (GPP). However, current SIF observations remain spatially coarse, infrequent, and noisy. Here we develop a machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) channels to reproduce SIF normalized by clear sky surface irradiance from the Global Ozone Monitoring Experiment-2 (GOME-2). The resulting product is a proxy for ecosystem photosynthetically active radiation absorbed by chlorophyll (fAPARCh). Multiplying this new product with a MODIS estimate of photosynthetically active radiation provides a new MODIS-only reconstruction of SIF called Reconstructed SIF (RSIF). RSIF exhibits much higher seasonal and interannual correlation than the original SIF when compared with eddy covariance estimates of GPP and two reference global GPP products, especially in dry and cold regions. RSIF also reproduces intense productivity regions such as the U.S. Corn Belt contrary to typical vegetation indices and similarly to SIF.

Terrestrial Carbon Sinks in the Brazilian Amazon and Cerrado Region Predicted from MODIS Satellite Data and Ecosystem Modeling

NASA Technical Reports Server (NTRS)

Potter, C.; Klooster, S.; Huete, A.; Genovese, V.; Bustamante, M.; Ferreira, L. Guimaraes; deOliveira, R. C., Jr.; Zepp, R.

2009-01-01

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2004. Net ecosystem production (NEP) flux for atmospheric CO2 in the region for these years was estimated. Consistently high carbon sink fluxes in terrestrial ecosystems on a yearly basis were found in the western portions of the states of Acre and Rondonia and the northern portions of the state of Par a. These areas were not significantly impacted by the 2002-2003 El Nino event in terms of net annual carbon gains. Areas of the region that show periodically high carbon source fluxes from terrestrial ecosystems to the atmosphere on yearly basis were found throughout the state of Maranhao and the southern portions of the state of Amazonas. As demonstrated though tower site comparisons, NEP modeled with monthly MODIS Enhanced Vegetation Index (EVI) inputs closely resembles the measured seasonal carbon fluxes at the LBA Tapajos tower site. Modeling results suggest that the capacity for use of MODIS Enhanced Vegetation Index (EVI) data to predict seasonal uptake rates of CO2 in Amazon forests and Cerrado woodlands is strong.

Compositing MODIS Terra and Aqua 250m daily surface reflectance data sets for vegetation monitoring

USDA-ARS?s Scientific Manuscript database

Remote sensing based vegetation Indices have been proven valuable in providing a spatially complete view of crop’s vegetation condition, which also manifests the impact of the disastrous events such as massive flood and drought. VegScape, a web GIS application for crop vegetation condition monitorin...

Modeling spatial patterns of wildfire susceptibility in southern California: Applications of MODIS remote sensing data and mesoscale numerical weather models

NASA Astrophysics Data System (ADS)

Schneider, Philipp

This dissertation investigates the potential of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and mesoscale numerical weather models for mapping wildfire susceptibility in general and for improving the Fire Potential Index (FPI) in southern California in particular. The dissertation explores the use of the Visible Atmospherically Resistant Index (VARI) from MODIS data for mapping relative greenness (RG) of vegetation and subsequently for computing the FPI. VARI-based RG was validated against in situ observations of live fuel moisture. The results indicate that VARI is superior to the previously used Normalized Difference Vegetation Index (NDVI) for computing RG. FPI computed using VARI-based RG was found to outperform the traditional FPI when validated against historical fire detections using logistic regression. The study further investigates the potential of using Multiple Endmember Spectral Mixture Analysis (MESMA) on MODIS data for estimating live and dead fractions of vegetation. MESMA fractions were compared against in situ measurements and fractions derived from data of a high-resolution, hyperspectral sensor. The results show that live and dead fractions obtained from MODIS using MESMA are well correlated with the reference data. Further, FPI computed using MESMA-based green vegetation fraction in lieu of RG was validated against historical fire occurrence data. MESMA-based FPI performs at a comparable level to the traditional NDVI-based FPI, but can do so using a single MODIS image rather than an extensive remote sensing time series as required for the RG approach. Finally this dissertation explores the potential of integrating gridded wind speed data obtained from the MM5 mesoscale numerical weather model in the FPI. A new fire susceptibility index, the Wind-Adjusted Fire Potential Index (WAFPI), was introduced. It modifies the FPI algorithm by integrating normalized wind speed. Validating WAFPI against historical wildfire events using logistic regression indicates that gridded data sets of wind speed are a valuable addition to the FPI as they can significantly increase the probability range of the fitted model and can further increase the model's discriminatory power over that of the traditional FPI.

Monitoring Agricultural Cropping Patterns across the Laurentian Great Lakes Basin Using MODIS-NDVI Data

EPA Science Inventory

The Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composite data product (MOD12Q) was used to develop annual cropland and crop-specific map products (corn, soybeans, and wheat) for the Laurentian Great Lakes Basin (GLB). Th...

Satellite-based peatland mapping: potential of the MODIS sensor.

Treesearch

D. Pflugmacher; O.N. Krankina; W.B. Cohen

2006-01-01

Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to capture extent and distribution of peatlands in the St. Petersburg region of Russia.

Terrestrial Carbon Sinks in the Brazilian Amazon and Cerrado Region Predicted from MODIS Satellite Data and Ecosystem Modeling

EPA Science Inventory

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2004. Pr...

Sub-Pixel Mapping of Tree Canopy, Impervious Surfaces, and Cropland in the Laurentian Great Lakes Basin Using MODIS Time-Series Data

EPA Science Inventory

This research examined sub-pixel land-cover classification performance for tree canopy, impervious surface, and cropland in the Laurentian Great Lakes Basin (GLB) using both timeseries MODIS (MOderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation In...

LINKING IN SITU TIME SERIES FOREST CANOPY LAI AND PHENOLOGY METRICS WITH MODIS AND LANDSAT NDVI AND LAI PRODUCTS

EPA Science Inventory

The subject of this presentation is forest vegetation dynamics as observed by the TERRA spacecraft's Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper, and complimentary in situ time series measurements of forest canopy metrics related to Leaf Area...

PRESENTED 11/01/05 LAND-COVER CHARACTERIZATION AND CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA

EPA Science Inventory

Land-Cover (LC) composition and conversions are important factors that affect ecosystem condition and function. The purpose of this research and development effort is to investigate the feasibility of using MODIS derived Normalized Difference Vegetation Index (NDVI) data to deli...

LAND-COVER CHARACTERIZATION AND CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA

EPA Science Inventory

The purpose of this research and development effort is to investigate the feasibility of using MODIS derived Normalized Difference Vegetation Index (NDVI) data to delineate areas of LC change on an annual basis and identify the outcome of LC conversions (i.e., new steady state). ...

Progress towards NASA MODIS and Suomi NPP Cloud Property Data Record Continuity

NASA Astrophysics Data System (ADS)

Platnick, S.; Meyer, K.; Holz, R.; Ackerman, S. A.; Heidinger, A.; Wind, G.; Platnick, S. E.; Wang, C.; Marchant, B.; Frey, R.

2017-12-01

The Suomi NPP VIIRS imager provides an opportunity to extend the 17+ year EOS MODIS climate data record into the next generation operational era. Similar to MODIS, VIIRS provides visible through IR observations at moderate spatial resolution with a 1330 LT equatorial crossing consistent with the MODIS on the Aqua platform. However, unlike MODIS, VIIRS lacks key water vapor and CO2 absorbing channels used for high cloud detection and cloud-top property retrievals. In addition, there is a significant mismatch in the spectral location of the 2.2 μm shortwave-infrared channels used for cloud optical/microphysical retrievals and cloud thermodynamic phase. Given these instrument differences between MODIS EOS and VIIRS S-NPP/JPSS, a merged MODIS-VIIRS cloud record to serve the science community in the coming decades requires different algorithm approaches than those used for MODIS alone. This new approach includes two parallel efforts: (1) Imager-only algorithms with only spectral channels common to VIIRS and MODIS (i.e., eliminate use of MODIS CO2 and NIR/IR water vapor channels). Since the algorithms are run with similar spectral observations, they provide a basis for establishing a continuous cloud data record across the two imagers. (2) Merged imager and sounder measurements (i.e.., MODIS-AIRS, VIIRS-CrIS) in lieu of higher-spatial resolution MODIS absorption channels absent on VIIRS. The MODIS-VIIRS continuity algorithm for cloud optical property retrievals leverages heritage algorithms that produce the existing MODIS cloud mask (MOD35), optical and microphysical properties product (MOD06), and the NOAA AWG Cloud Height Algorithm (ACHA). We discuss our progress towards merging the MODIS observational record with VIIRS in order to generate cloud optical property climate data record continuity across the observing systems. In addition, we summarize efforts to reconcile apparent radiometric biases between analogous imager channels, a critical consideration for obtaining inter-sensor climate data record continuity.

Developing a global mixed-canopy, height-variable vegetation structure dataset for estimating global vegetation albedo by a clumped canopy radiative transfer scheme in the NASA Ent Terrestrial Biosphere Model and GISS GCM

NASA Astrophysics Data System (ADS)

Montes, Carlo; Kiang, Nancy Y.; Ni-Meister, Wenge; Yang, Wenze; Schaaf, Crystal; Aleinov, Igor; Jonas, Jeffrey A.; Zhao, Feng; Yao, Tian; Wang, Zhuosen; Sun, Qingsong; Carrer, Dominique

2016-04-01

Processes determining biosphere-atmosphere coupling are strongly influenced by vegetation structure. Thus, ecosystem carbon sequestration and evapotranspiration affecting global carbon and water balances will depend upon the spatial extent of vegetation, its vertical structure, and its physiological variability. To represent this globally, Dynamic Global Vegetation Models (DGVMs) coupled to General Circulation Models (GCMs) make use of satellite and/or model-based vegetation classifications often composed by homogeneous communities. This work aims at developing a new Global Vegetation Structure Dataset (GVSD) by incorporating varying vegetation heights for mixed plant communities to be used as boundary conditions to the Analytical Clumped Two-Stream (ACTS) canopy radiative transfer scheme (Ni-Meister et al., 2010) incorporated into the NASA Ent Terrestrial Biosphere Model (TBM), the DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM. Information sources about land surface and vegetation characteristics obtained from a number of earth observation platforms and algorithms include the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), soil albedo derived from MODIS (Carrer et al., 2014), along with vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014). Three widely used Leaf Area Index (LAI) products are compared as input to the GVSD and ACTS forcing in terms of vegetation albedo: Global Data Sets of Vegetation (LAI)3g (Zhu et al. 2013), Beijing Normal University LAI (Yuan et al., 2011), and MODIS MOD15A2H product (Yang et al., 2006). Further PFT partitioning is performed according to a climate classification utilizing the Climate Research Unit (CRU; Harris et al., 2013) and the NOAA Global Precipitation Climatology Centre (GPCC; Scheider et al., 2014) data. Final products are a GVSD consisting of mixed plant communities (e.g. mixed forests, savannas, mixed PFTs) following the Ecosystem Demography model (Moorcroft et al., 2001) approach represented by multi-cohort community patches at the sub-grid level of the GCM, which are ensembles of identical individuals whose differences are represented by PFTs, canopy height, density and vegetation structure sensitivity to allometric parameters. The performance of the Ent TBM in estimating VIS-NIR vegetation albedo by the new GVSD and ACTS is assessed first by comparison against the previous GISS GCM vegetation classification and prescribed Lambertian albedoes of Matthews (1984), and secondly, against MODIS global estimations and FLUXNET site-scale observations. Ultimately, this GVSD will serve as a template for community data sets, and be used as boundary conditions to the Ent TBM for prediction of biomass, carbon balances and GISS GCM climate.

Near Real-time Operational Use of eMODIS Expedited NDVI for Monitoring Applications and Famine Early Warning

NASA Astrophysics Data System (ADS)

Rowland, J.; Budde, M. E.

2010-12-01

The Famine Early Warning Systems Network (FEWS NET) has requirements for near real-time monitoring of vegetation conditions for food security applications. Accurate and timely assessments of crop conditions are an important element of food security decision making. FEWS NET scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center are utilizing a new Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset for operational monitoring of crop and pasture conditions in parts of the world where food availability is highly dependent on subsistence agriculture and animal husbandry. The expedited MODIS, or eMODIS, production system processes NDVI data using MODIS surface reflectance provided by the Land Atmosphere Near-real-time Capability for EOS (LANCE). Benefits of this production system include customized compositing schedules, near real-time data availability, and minimized re-sampling. FEWS NET has implemented a 10-day compositing scheme every five days to accommodate the need for timely information on vegetation conditions. The data are currently being processed at 250-meter spatial resolution for Central America, Hispaniola, and Africa. Data are further enhanced by the application of a temporal smoothing filter which helps remove contamination due to clouds and other atmospheric effects. The results of this near real-time monitoring capability have been the timely provision of NDVI and NDVI anomaly maps for each of the FEWS NET monitoring regions and the availability of a consistently processed dataset to aid crop assessment missions and to facilitate customized analyses of crop production, drought, and agro-pastoral conditions.

A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest

USGS Publications Warehouse

Liang, Liang; Schwartz, Mark D.; Zhuosen Wang,; Gao, Feng; Schaaf, Crystal B.; Bin Tan,; Morisette, Jeffrey T.; Zhang, Xiaoyang

2014-01-01

Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m × 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring.

A Five-Year Analysis of MODIS NDVI and NDWI for Rangeland Drought Assessment: Preliminary Results

NASA Astrophysics Data System (ADS)

Gu, Y.; Brown, J. F.; Verdin, J. P.; Wardlow, B.

2006-12-01

Drought is one of the most costly natural disasters in the United States. Traditionally, drought monitoring has been based on weather station observations, which lack the continuous spatial coverage needed to adequately characterize and monitor detailed spatial patterns of drought conditions. Satellite remote sensing observations can provide a synoptic view of the land and provide a spatial context for measuring drought. A common satellite-based index, the normalized difference vegetation index (NDVI) has a 30-year history of use for vegetation condition monitoring. NDVI is calculated from the visible red and near infrared channels and measures the changes in chlorophyll absorption and reflection in the spongy mesophyll of the vegetation canopy that are reflected in these respective bands. The normalized difference water index (NDWI) is another index, derived from the near-infrared and short wave infrared channels, and reflects changes in both the water content and spongy mesophyll in the vegetation canopy. As a result, the NDWI is influenced by both desiccation and wilting in the vegetation canopy and may be a more sensitive indicator than the NDVI for large- area drought monitoring. The objective of this study was to process and evaluate a 5-year history of 500-meter NDVI and NDWI data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and to investigate methods for measuring and monitoring drought in rangeland over the southern plains of the United States. This initial study included: (1) the development of a climatological database for MODIS NDVI and NDWI, (2) a study of the relationship between the NDVI, NDWI, and drought condition over rangeland, (3) the development of a method to provide threshold NDVI/NDWI values under drought conditions based on the 5-year NDVI/NDWI/drought condition analysis, and (4) the investigation of additional vegetation drought information provided by the NDWI versus the NDVI in a 5-year comparison of the two indices. The MODIS data were obtained from the Land Processes Distributed Active Archive System. Results show strong relationships among NDVI, NDWI, and drought analyzed over grasslands in the Flint Hills region of Kansas and Oklahoma. During the summer months, the average NDVI and NDWI values were consistently lower (NDVI<0.5 and NDWI<0.3) for the tallgrass prairie under drought conditions than under normal climate conditions (NDVI>0.6 and NDWI>0.4). The distinctions between drought conditions and normal climate conditions are based on the historic U.S. Drought Monitor maps and the historic Palmer index data. To take advantage of information contained in both indices, we calculated the difference between NDVI and NDWI (NDVI-NDWI). The difference between NDVI and NDWI slightly increases during the summer drought condition. Based on these analyses, the NDWI appears to be more sensitive than NDVI to drought conditions. The results of statistical analysis of the relationships among these indices will be presented in the poster.

Analysis of vegetation dynamics and climatic variability impacts on greenness across Canada using remotely sensed data from 2000 to 2009

NASA Astrophysics Data System (ADS)

Fang, Xiuqin; Zhu, Qiuan; Chen, Huai; Ma, Zhihai; Wang, Weifeng; Song, Xinzhang; Zhao, Pengxiang; Peng, Changhui

2014-01-01

Using time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from 2000 to 2009, we assessed decadal vegetation dynamics across Canada and examined the relationship between NDVI and climatic variables (precipitation and temperature). The Palmer drought severity index and vapor pressure difference (VPD) were used to relate the vegetation changes to the climate, especially in cases of drought. Results indicated that MODIS NDVI measurements provided a dynamic picture of interannual variation in Canadian vegetation patterns. Greenness declined in 2000, 2002, and 2009 and increased in 2005, 2006, and 2008. Vegetation dynamics varied across regions during the period. Most forest land shows little change, while vegetation in the ecozone of Pacific Maritime, Prairies, and Taiga Shield shows more dynamics than in the others. Significant correlations were found between NDVI and the climatic variables. The variation of NDVI resulting from climatic variability was more highly correlated to temperature than to precipitation in most ecozones. Vegetation grows better with higher precipitation and temperature in almost all ecozones. However, vegetation grows worse under higher temperature in the Prairies ecozone. The annual changes in NDVI corresponded well with the change in VPD in most ecozones.

Object-Based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Montesano, P. M.; Nelson, R.

2011-01-01

The circumpolar taiga tundra ecotone was delineated using an image-segmentation-based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 to 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation procedure was used to group pixels representing similar tree cover into polygonal features (segmentation objects) that form the map of the transition zone. Each polygon represents an area much larger than the 500 m MODIS pixel and characterizes the patterns of sparse forest patches on a regional scale. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values from5 to 20%, or (2) mean adjusted TCC values greater than 5% but with a standard deviation less than 5% were used to identify the ecotone. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1 degree longitudinal interval in North America and Eurasia, (2) Landsat-derived Canadian proportion of forest cover for Canada, and (3) with canopy cover estimates extracted from airborne profiling lidar data that transected 1238 of the TCC polygons. The adjusted TCC from MODIS VCF shows, on average, less than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. A comparison of the 1238 TCC polygons with profiling lidar measurements yielded an overall accuracy of 67.7%.

An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Wang, Yujie; Frey, R.

2008-01-01

Quality of aerosol retrievals and atmospheric correction depends strongly on accuracy of the cloud mask (CM) algorithm. The heritage CM algorithms developed for AVHRR and MODIS use the latest sensor measurements of spectral reflectance and brightness temperature and perform processing at the pixel level. The algorithms are threshold-based and empirically tuned. They don't explicitly address the classical problem of cloud search, wherein the baseline clear-skies scene is defined for comparison. Here, we report on a new CM algorithm which explicitly builds and maintains a reference clear-skies image of the surface (refcm) using a time series of MODIS measurements. The new algorithm, developed as part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS, relies on fact that clear-skies images of the same surface area have a common textural pattern, defined by the surface topography, boundaries of rivers and lakes, distribution of soils and vegetation etc. This pattern changes slowly given the daily rate of global Earth observations, whereas clouds introduce high-frequency random disturbances. Under clear skies, consecutive gridded images of the same surface area have a high covariance, whereas in presence of clouds covariance is usually low. This idea is central to initialization of refcm which is used to derive cloud mask in combination with spectral and brightness temperature tests. The refcm is continuously updated with the latest clear-skies MODIS measurements, thus adapting to seasonal and rapid surface changes. The algorithm is enhanced by an internal dynamic land-water-snow classification coupled with a surface change mask. An initial comparison shows that the new algorithm offers the potential to perform better than the MODIS MOD35 cloud mask in situations where the land surface is changing rapidly, and over Earth regions covered by snow and ice.

A Drone-based Tropical Forest Experiment to Estimate Vegetation Properties

NASA Astrophysics Data System (ADS)

Henke, D.

2017-12-01

In mid-latitudes, remote sensing technology is intensively used to monitor vegetation properties. However, in the tropics, high cloud-cover and saturation effects of vegetation indices (VI) hamper the reliability of vegetation parameters derived from satellite data. A drone experiment over the Barro Colorado Island (BCI), Panama, with high temporal repetition rates was conducted in spring 2017 to investigate the robustness and stability of remotely sensed vegetation parameters in tropical environments. For this purpose, three 10-day flight windows in February, March and April were selected and drone flights were repeated on daily intervals when weather conditions and equipment allowed it. In total, 18 days were recorded with two different optical cameras on sensefly's eBee drone: one red, green, blue (RGB) camera and one camera with near infra-red (NIR), green and blue channels. When possible, the data were acquired at the same time of day. Pix4D and Agisoft software were used to calculate the Normalized Difference VI (NDVI) and forest structure. In addition, leave samples were collected ones per month from 16 different plant species and the relative water content was measured as ground reference. Further data sources for the analysis are phenocam images (RGB & NIR) on BCI and satellite images of MODIS (NDVI; Enhanced VI EVI) and Sentinel-1 (radar backscatter). The attached figure illustrates the main data collected on BCI. Initial results suggest that the coefficient of determination (R2) is relatively high between ground samples and drone data, Sentinel-1 backscatter and MODIS EVI with R2 values ranging from 0.4 to 0.6; on the contrary, R2 values between ground measurements and MODIS NDVI or phenocam images are below 0.2. As the experiment took place mainly during dry season on BCI, cloud-cover rates are less dominate than during wet season. Under these conditions, MODIS EVI, which is less vulnerable to saturation effects, seems to be more reliable than MODIS NDVI. During wet season, Sentinel-1 backscatter might be the most reliable satellite option to derive vegetation parameters in the tropics. For a more robust conclusion, additional data takes over several years and during dry as well as wet season are needed to confirm initial findings presented here.

Comparing MODIS C6 'Deep Blue' and 'Dark Target' Aerosol Data

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Sayer, A. M.; Bettenhausen, C.; Lee, J.; Levy, R. C.; Mattoo, S.; Munchak, L. A.; Kleidman, R.

2014-01-01

The MODIS Collection 6 Atmospheres product suite includes refined versions of both 'Deep Blue' (DB) and 'Dark Target' (DT) aerosol algorithms, with the DB dataset now expanded to include coverage over vegetated land surfaces. This means that, over much of the global land surface, users will have both DB and DT data to choose from. A 'merged' dataset is also provided, primarily for visualization purposes, which takes retrievals from either or both algorithms based on regional and seasonal climatologies of normalized difference vegetation index (NDVI). This poster present some comparisons of these two C6 aerosol algorithms, focusing on AOD at 550 nm derived from MODIS Aqua measurements, with each other and with Aerosol Robotic Network (AERONET) data, with the intent to facilitate user decisions about the suitability of the two datasets for their desired applications.

Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China

NASA Astrophysics Data System (ADS)

Jin, Huaan; Li, Ainong; Bian, Jinhu; Nan, Xi; Zhao, Wei; Zhang, Zhengjian; Yin, Gaofei

2017-03-01

The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011-2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China. The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r = 0.35, RMSE = 1.72, mean bias = -0.71) and MODIS LAI (r = 0.49, RMSE = 1.75, mean bias = -0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain.

Monitoring Start of Season in Alaska

NASA Astrophysics Data System (ADS)

Robin, J.; Dubayah, R.; Sparrow, E.; Levine, E.

2006-12-01

In biomes that have distinct winter seasons, start of spring phenological events, specifically timing of budburst and green-up of leaves, coincides with transpiration. Seasons leave annual signatures that reflect the dynamic nature of the hydrologic cycle and link the different spheres of the Earth system. This paper evaluates whether continuity between AVHRR and MODIS normalized difference vegetation index (NDVI) is achievable for monitoring land surface phenology, specifically start of season (SOS), in Alaska. Additionally, two thresholds, one based on NDVI and the other on accumulated growing degree-days (GDD), are compared to determine which most accurately predicts SOS for Fairbanks. Ratio of maximum greenness at SOS was computed from biweekly AVHRR and MODIS composites for 2001 through 2004 for Anchorage and Fairbanks regions. SOS dates were determined from annual green-up observations made by GLOBE students. Results showed that different processing as well as spectral characteristics of each sensor restrict continuity between the two datasets. MODIS values were consistently higher and had less inter-annual variability during the height of the growing season than corresponding AVHRR values. Furthermore, a threshold of 131-175 accumulated GDD was a better predictor of SOS for Fairbanks than a NDVI threshold applied to AVHRR and MODIS datasets. The NDVI threshold was developed from biweekly AVHRR composites from 1982 through 2004 and corresponding annual green-up observations at University of Alaska-Fairbanks (UAF). The GDD threshold was developed from 20+ years of historic daily mean air temperature data and the same green-up observations. SOS dates computed with the GDD threshold most closely resembled actual green-up dates observed by GLOBE students and UAF researchers. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska.

Analysis Of The Land Surface Temperature And NDVI Using MODIS Data On The Arctic Tundra During The Last Decade

NASA Astrophysics Data System (ADS)

Mattar, C.; Duran-Alarcon, C.; Jimenez-Munoz, J. C.; Sobrino, J. A.

2013-12-01

The arctic tundra is one of the most sensible biome to climate conditions which has experienced important changes in the spatial distribution of temperature and vegetation in the last decades. In this paper we analyzed the spatio-temporal trend of the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) over the arctic tundra biome during the last decade (2001-2012) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) land products MOD11C3 (LST) and MOD13C2 (NDVI) were used. Anomalies for each variable were analyzed at monthly level, and the magnitude and statistical significance of the trends were computed using the non-parametric tests of Sen's Slope and Mann-Kendal respectively. The results obtained from MODIS LST data showed a significant increase (p-value < 0.05) on surface temperature over the arctic tundra in the last decade. In the case of the NDVI, the trend was positive (increase on NDVI) but statistically not significant (p-value < 0.05). All tundra regions defined in the Circumpolar Arctic Vegetation Map have presented positive and statistically significant trends in NDVI and LST. Values of trends obtained from MODIS data over all the tundra regions were +1.10 [°C/dec] in the case of LST and +0.005 [NDVI value/dec] in the case of NDVI.

Mapping Cropland and Major Crop Types Across the Great Lakes Basin Using MODIS-NDVI Data

EPA Science Inventory

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250-m time-series data to develop a cropland mapping capability throughout the 480 000 km2 Great Lakes Basin (GLB). Cropland mapping was conducted usi...

Assessing the Accuracy of MODIS-NDVI Derived Land-Cover Across the Great Lakes Basin

EPA Science Inventory

This research describes the accuracy assessment process for a land-cover dataset developed for the Great Lakes Basin (GLB). This land-cover dataset was developed from the 2007 MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data. Tr...

The Change in the area of various land covers on the Tibetan Plateau during 1957-2015

NASA Astrophysics Data System (ADS)

Cuo, Lan; Zhang, Yongxin

2017-04-01

With average elevation of 4000 m and area of 2.5×106 km2, Tibetan Plateau hosts various fragile ecosystems such as perennial alpine meadow, perennial alpine steppe, temperate evergreen needleleaf trees, temperate deciduous trees, temperate shrub grassland, and barely vegetated desert. Perennial alpine meadow and steppe are the two dominant vegetation types on the heartland of the plateau. MODIS Leaf Area Index (LAI) ranges from 0 to 2 in most part of the plateau. With climate change, these ecosystems are expected to undergo alteration. This study uses a dynamic vegetation model - Lund-Potsdam-Jena (LPJ) to investigate the change of the barely vegetated area and other vegetation types caused by climate change during 1957-2015 on the Tibetan Plateau. Model simulated foliage projective coverage (FPC) and plant functional types (PFTs) are selected for the investigation. The model is evaluated first using both field surveyed land cover map and MODIS LAI images. Long term trends of vegetation FPC is examined. Decadal variations of vegetated and barely vegetated land are compared. The impacts of extreme precipitation, air temperature and CO2 on the expansion and contraction of barely vegetated and vegetated areas are shown. The study will identify the dominant climate factors in affecting the desert area in the region.

Seasonal Biophysical Dynamics of the Amazon from Space Using MODIS Vegetation Indices

NASA Astrophysics Data System (ADS)

Huete, A. R.; Didan, K.; Ratana, P.; Ferreira, L.

2002-12-01

We utilized the Terra- Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) products to analyze the seasonal and spatial patterns of photosynthetic vegetation activity over the Amazon Basin and surrounding regions of Brazil. The seasonal patterns of vegetation activity were studied along two, eco-climatic transects extending from (1) the cerrado region (Brasilia National Park) to the seasonal tropical forest (Tapajos National Forest) and (2) the caatinga biome to the seasonal and per-humid tropical forests. In addition to the climatic transects, we also investigated the seasonal dynamics of altered, land conversion areas associated with pastures and clearcutting land use activities. Both the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) at 250-m, 500-m, and 1-km were used to extract seasonal profile curves. The quality assurance (QA) information of the output products was used in noise removal and data filtering prior to the generation of the seasonal profiles. Histogram analyses were also performed at coarse (biome) scale and fine, site intensive (flux towers) scale. The seasonal patterns of the cerrado and caatinga were very pronounced with distinct dry and wet seasonal trends. We observed decreasing dry-wet seasonal patterns in the transitional areas near Araguaia National Park. In contrast, the seasonal behavior of the tropical forests were much harder to assess, but indicated slight seasonal trends that ran counter to rainfall activity. This may be attributed to new leaf growth in the dry season. We further found MODIS VI seasonal patterns to vary significantly in land converted and land degraded areas.

Relationships between canopy greenness and CO2 dynamics of a Mediterranean deciduous forest assessed with webcam imagery and MODIS vegetation indices

NASA Astrophysics Data System (ADS)

Balzarolo, M.; Papale, D.; Richardson, A. D.

2009-04-01

Phenological observations of foliar development and senescence are needed to understand the relationship between canopy properties and seasonal productivity dynamics (e.g., carbon uptake) of terrestrial ecosystems. Traditional phenological ground observations based on a visual observation of different vegetation growth phases (from first leaf opening, to first leaf flowering, full bloom until senescence) are laborious and typically limited to observations on just a few individual subjects. On the contrary, remote sensing techniques appear to offer the potential for assessing long-term variability in primary productivity at a global scale (Field et al., 1993). Recent studies have shown that biochemical and biophysical canopy properties can be measured with a quantifiable uncertainty that can be incorporated in the land-biosphere models (Ustin et al., 2004a; Ollinger et al 2008). Canopy greenness can be quantified by the use of vegetation indices (VIs) as, for example, Normalized Difference Vegetation Index (NDVI, Rouse et al., 1974; Deering, 1978), but a disadvantage of this approach is that there are uncertainties associated with these indices (due to the spatial and temporal resolution of the data), and the interpretation of a specific VI value, in the context of on-the-ground phenology, is not clear. Improved ground-based datasets are needed to validate and improve remotely-sensed phenological indices. Continuous monitoring of vegetation canopies with digital webcams (Richardson et al. 2007) may offer a direct link between phenological changes in canopy state and what is "seen" by satellite sensors. The general objective of this study is to analyze the relationship between biosphere-atmosphere CO2 exchange (measured by eddy covariance) and phenological canopy status, or greenness, of a Mediterranean deciduous broadleaf forest in central Italy (Roccarespampani, 42°24' N, 11°55' E). Canopy greenness is quantify using two different approaches: from digital webcam images, using indices derived from red, green and blue (RGB) color channel brightness (RGBi, after Richardson et al. 2007) and with VIs (e.g. NDVI, SR, MSR, GRDI, NCI, CI and SLAVI) derived from MODIS surface reflectance data (MOD09A1). Since MOD09A1 reflectance data represent the maximum surface reflectance of each band for a consecutive 8-day period, webcam imagery, as fluxes data, acquired whit half-hourly temporal resolution have been time averaged on 8 day period. Evaluation of performance of RGBi-VIs, RGBi-CO2flux and MODIS-CO2flux relationships were performed by linear regression analyses using the classical least squares (LS) statistical technique. Among all calculated vegetation indexes, GRDI (Green Red Difference Index: Gitelson et al., 2002) and SLAVI (Specific Leaf Area Vegetation Index: Lymburner et al., 2000) showed best linear fit with webcam RGBi greenness. SLAVI was also one of the vegetation indices best correlated with mean daily CO2 flux (R2=0.79). Finally, the relationship between RGBi and CO2 flux had a R2 of 0.67. Concluding, both webcam and MODIS greenness indices offer potential for assessing seasonal variation in the productivity of terrestrial ecosystems. Future work will focus on reducing the uncertainties inherent in these approaches, and integrating field observations of phenology into this study.

Satellite assessment of early-season forecasts for vegetation conditions of grazing allotments in Nevada, United States

USDA-ARS?s Scientific Manuscript database

Fifteen years of enhanced vegetation index data from the MODIS sensor are examined in conjunction with precipitation and the Palmer drought severity index to assess how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year. ...

A remote sensing protocol for identifying rangelands with degraded productive capacity

Treesearch

Matthew C. Reeves; L. Scott Bagget

2014-01-01

Rangeland degradation is a growing problem throughout the world. An assessment process for com-paring the trend and state of vegetation productivity to objectively derived reference conditions wasdeveloped. Vegetation productivity was estimated from 2000 to 2012 using annual maximum Normalized Difference Vegetation Index (NDVI) from the MODIS satellite platform. Each...

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data*

PubMed Central

Peng, Dai-liang; Huang, Jing-feng; Huete, Alfredo R.; Yang, Tai-ming; Gao, Ping; Chen, Yan-chun; Chen, Hui; Li, Jun; Liu, Zhan-yu

2010-01-01

We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP. PMID:20349524

Progress towards MODIS and VIIRS Cloud Optical Property Data Record Continuity

NASA Astrophysics Data System (ADS)

Meyer, K.; Platnick, S. E.; Wind, G.; Amarasinghe, N.; Holz, R.; Ackerman, S. A.; Heidinger, A. K.

2016-12-01

The launch of Suomi NPP in the fall of 2011 began the next generation of U.S. operational polar orbiting Earth observations, and its VIIRS imager provides an opportunity to extend the 15+ year climate data record of MODIS EOS. Similar to MODIS, VIIRS provides visible through IR observations at moderate spatial resolution with a 1330 LT equatorial crossing consistent with the MODIS on the Aqua platform. However, unlike MODIS, VIIRS lacks key water vapor and CO2 absorbing channels used for high cloud detection and cloud-top property retrievals, and there is a significant change in the spectral location of the 2.1μm shortwave-infrared channel used for cloud optical/microphysical retrievals and cloud thermodynamic phase. Given these instrument differences between MODIS EOS and VIIRS S-NPP/JPSS, we discuss our progress towards merging the MODIS observational record with VIIRS in order to generate cloud optical property climate data record continuity across the observing systems. The MODIS-VIIRS continuity algorithm for cloud optical property retrievals leverages heritage algorithms that produce the existing MODIS cloud optical and microphysical properties product (MOD06); the NOAA AWG/CLAVR-x cloud-top property algorithm and a common MODIS-VIIRS cloud mask feed into the optical property algorithm. To account for the different channel sets of MODIS and VIIRS, each algorithm nominally uses a subset of channels common to both imagers. Data granule and aggregated examples for the current version of the continuity algorithm (MODAWG) will be shown. In addition, efforts to reconcile apparent radiometric biases between analogous channels of the two imagers, a critical consideration for obtaining inter-sensor climate data record continuity, will be discussed.

Satellite and in situ monitoring data used for modeling of forest vegetation reflectance

NASA Astrophysics Data System (ADS)

Zoran, M. A.; Savastru, R. S.; Savastru, D. M.; Miclos, S. I.; Tautan, M. N.; Baschir, L.

2010-10-01

As climatic variability and anthropogenic stressors are growing up continuously, must be defined the proper criteria for forest vegetation assessment. In order to characterize current and future state of forest vegetation satellite imagery is a very useful tool. Vegetation can be distinguished using remote sensing data from most other (mainly inorganic) materials by virtue of its notable absorption in the red and blue segments of the visible spectrum, its higher green reflectance and, especially, its very strong reflectance in the near-IR. Vegetation reflectance has variations with sun zenith angle, view zenith angle, and terrain slope angle. To provide corrections of these effects, for visible and near-infrared light, was used a developed a simple physical model of vegetation reflectance, by assuming homogeneous and closed vegetation canopy with randomly oriented leaves. A simple physical model of forest vegetation reflectance was applied and validated for Cernica forested area, near Bucharest town through two ASTER satellite data , acquired within minutes from one another ,a nadir and off-nadir for band 3 lying in the near infra red, most radiance differences between the two scenes can be attributed to the BRDF effect. Other satellite data MODIS, Landsat TM and ETM as well as, IKONOS have been used for different NDVI and classification analysis.

Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions

NASA Astrophysics Data System (ADS)

Kim, Jongyoun; Hogue, Terri S.

2012-01-01

The current study investigates a method to provide land surface parameters [i.e., land surface temperature (LST) and normalized difference vegetation index (NDVI)] at a high spatial (˜30 and 60 m) and temporal (daily and 8-day) resolution by combining advantages from Landsat and moderate-resolution imaging spectroradiometer (MODIS) satellites. We adopt a previously developed subtraction method that merges the spatial detail of higher-resolution imagery (Landsat) with the temporal change observed in coarser or moderate-resolution imagery (MODIS). Applying the temporal difference between MODIS images observed at two different dates to a higher-resolution Landsat image allows prediction of a combined or fused image (Landsat+MODIS) at a future date. Evaluation of the resultant merged products is undertaken within the Southeastern Arizona region where data is available from a range of flux tower sites. The Landsat+MODIS fused products capture the raw Landsat values and also reflect the MODIS temporal variation. The predicted Landsat+MODIS LST improves mean absolute error around 5°C at the more heterogeneous sites compared to the original satellite products. The fused Landsat+MODIS NDVI product also shows good correlation to ground-based data and is relatively consistent except during the acute (monsoon) growing season. The sensitivity of the fused product relative to temporal gaps in Landsat data appears to be more affected by uncertainty associated with regional precipitation and green-up, than the length of the gap associated with Landsat viewing, suggesting the potential to use a minimal number of original Landsat images during relatively stable land surface and climate conditions. Our extensive validation yields insight on the ability of the proposed method to integrate multiscale platforms and the potential for reducing costs associated with high-resolution satellite systems (e.g., SPOT, QuickBird, IKONOS).

Determination of the single scattering albedo and direct radiative forcing of biomass burning aerosol with data from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite instrument

NASA Astrophysics Data System (ADS)

Zhu, Li

Biomass burning aerosols absorb and scatter solar radiation and therefore affect the energy balance of the Earth-atmosphere system. The single scattering albedo (SSA), the ratio of the scattering coefficient to the extinction coefficient, is an important parameter to describe the optical properties of aerosols and to determine the effect of aerosols on the energy balance of the planet and climate. Aerosol effects on radiation also depend strongly on surface albedo. Large uncertainties remain in current estimates of radiative impacts of biomass burning aerosols, due largely to the lack of reliable measurements of aerosol and surface properties. In this work we investigate how satellite measurements can be used to estimate the direct radiative forcing of biomass burning aerosols. We developed a method using the critical reflectance technique to retrieve SSA from the Moderate Resolution Imaging Spectroradiometer (MODIS) observed reflectance at the top of the atmosphere (TOA). We evaluated MODIS retrieved SSAs with AErosol RObotic NETwork (AERONET) retrievals and found good agreements within the published uncertainty of the AERONET retrievals. We then developed an algorithm, the MODIS Enhanced Vegetation Albedo (MEVA), to improve the representations of spectral variations of vegetation surface albedo based on MODIS observations at the discrete 0.67, 0.86, 0.47, 0.55, 1.24, 1.64, and 2.12 mu-m channels. This algorithm is validated using laboratory measurements of the different vegetation types from the Amazon region, data from the Johns Hopkins University (JHU) spectral library, and data from the U.S. Geological Survey (USGS) digital spectral library. We show that the MEVA method can improve the accuracy of flux and aerosol forcing calculations at the TOA compared to more traditional interpolated approaches. Lastly, we combine the MODIS retrieved biomass burning aerosol SSA and the surface albedo spectrum determined from the MEVA technique to calculate TOA flux and aerosol direct radiative forcing over the Amazon region and compare it with Clouds and the Earth's Radiant Energy System (CERES) satellite results. The results show that MODIS based forcing calculations present similar averaged results compared to CERES, but MODIS shows greater spatial variation of aerosol forcing than CERES. Possible reasons for these differences are explored and discussed in this work. Potential future research based on these results is discussed as well.

Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI.

PubMed

Lobell, D B; Lesch, S M; Corwin, D L; Ulmer, M G; Anderson, K A; Potts, D J; Doolittle, J A; Matos, M R; Baltes, M J

2010-01-01

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

Role of MODIS Vegetation Phenology Products in the ForWarn System for Monitoring of Forest Disturbances in the Conterminous United States

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Norman, Steve; Gasser, Jerry; Smoot, James; Kuper, Philip D,

2012-01-01

This presentation discusses MODIS vegetation phenology products used in the ForWarn Early Warning System (EWS) tool for near real time regional forest disturbance detection and surveillance at regional to national scales. The ForWarn EWS is being developed by the USDA Forest Service NASA, ORNL, and USGS to aid federal and state forest health management activities. ForWarn employs multiple historical land surface phenology products that are derived from MODIS MOD13 Normalized Difference Vegetation Index (NDVI) data. The latter is temporally processed into phenology products with the Time Series Product Tool (TSPT) and the Phenological Parameter Estimation Tool (PPET) software produced at NASA Stennis Space Center. TSPT is used to effectively noise reduce, fuse, and void interpolate MODIS NDVI data. PPET employs TSPT-processed NDVI time series data as an input, outputting multiple vegetation phenology products at a 232 meter resolution for 2000 to 2011, including NDVI magnitude and day of year products for seven key points along the growing season (peak of growing season and the minima, 20%, and 80% of the peak NDVI for both the left and right side of growing season), cumulative NDVI integral products for the most active part of the growing season and sequentially across the growing season at 8 day intervals, and maximum value NDVI products composited at 24 day intervals in which each product date has 8 days of overlap between the previous and following product dates. MODIS NDVI phenology products are also used to compute nationwide NRT forest change products refreshed every 8 days. These include percent change in forest NDVI products that compare the current NDVI from USGS eMODIS products to historical MODIS MOD13 NDVI. For each date, three forest change products are produced using three different maximum value NDVI baselines (from the previous year, three previous years, and all previous years). All change products are output with a rainbow color table in which forests with the most severe NDVI decreases are assigned hot colors (yellow to red) and forests with prominent NDVI increases are assigned cold colors (blue tones). All mentioned products have been integrated as data layers into ForWarn s geospatial data viewer known as the U.S. Forest Change Assessment Viewer (FCAV). The latter is used to view and assess the context of the mentioned forest change products with respect to ancillary data layers, such as land cover, elevation, hydrologic features, climatic data, storm data, aerial disturbance surveys, fire data, and land ownership. The FCAV also includes a temporal NDVI profiler for viewing phenological change in multi-year NDVI associated with known or suspected regionally apparent forest disturbances (e.g., from fire and insects). ForWarn forest change products have been used to detect, track, and assess several biotic and abiotic regional forest disturbance events across the country, including ephemeral and longer lasting damage from storms, drought, and insects. Such change products are most effective for viewing severe disturbances affecting multiple MODIS pixels. MODIS vegetation phenology products contribute vital current information on forest conditions to the ForWarn system and this role is expected to grow as these products are refined and derivative products are added.

Assessment of Vegetation Destruction Due to Wenchuan Earthquake and Its Recovery Process Using MODIS Data

NASA Astrophysics Data System (ADS)

Zou, Z.; Xiao, X.

2015-12-01

With a high temporal resolution and a large covering area, MODIS data are particularly useful in assessing vegetation destruction and recovery of a wide range of areas. In this study, MOD13Q1 data of the growing season (Mar. to Nov.) are used to calculate the Maximum NDVI (NDVImax) of each year. This study calculates each pixel's mean and standard deviation of the NDVImaxs in the 8 years before the earthquake. If the pixel's NDVImax of 2008 is two standard deviation smaller than the mean NDVImax, this pixel is detected as a vegetation destructed pixel. For each vegetation destructed pixel, its similar pixels of the same vegetation type are selected within the latitude difference of 0.5 degrees, altitude difference of 100 meters and slope difference of 3 degrees. Then the NDVImax difference of each vegetation destructed pixel and its similar pixels are calculated. The 5 similar pixels with the smallest NDVImax difference in the 8 years before the earthquake are selected as reference pixels. The mean NDVImaxs of these reference pixels after the earthquake are calculated and serve as the criterion to assess the vegetation recovery process.

IN SITU ESTIMATES OF FOREST LAI FOR MODIS DATA VALIDATION

EPA Science Inventory

Satellite remote sensor data are commonly used to assess ecosystem conditions through synoptic monitoring of terrestrial vegetation extent, biomass, and seasonal dynamics. Two commonly used vegetation indices that can be derived from various remote sensor systems include the Norm...

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product

USDA-ARS?s Scientific Manuscript database

Climate warming over the past half century has led to observable changes in vegetation phenology and growing season length; which can be measured globally using remote sensing derived vegetation indices. Previous studies in mid- and high northern latitude systems show temperature driven earlier spri...

A Changing Number of Alternative States in the Boreal Biome: Reproducibility Risks of Replacing Remote Sensing Products.

PubMed

Xu, Chi; Holmgren, Milena; Van Nes, Egbert H; Hirota, Marina; Chapin, F Stuart; Scheffer, Marten

2015-01-01

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct 'savanna-like' state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available.

Understanding phenology and drought recovery in the Amboseli Basin of Kenya with MODIS Vegetation Indices

NASA Astrophysics Data System (ADS)

Williams, R. T.; Geddes, Q. A.; Baker, A.; Gao, M.; Voelker, E.; Oluwakonyinsola, A.

2013-12-01

The Amboseli Conservation Centre (ACC) has been gathering comprehensive field data for 47 years within the Amboseli Basin near Mount Kilimanjaro in Kenya. Over the past half century, the region has suffered from near complete deforestation and continues to suffer from land degradation due to the increasingly sedentary and segregated human and animal concentrations. Overall, Kenya has experienced a loss of over 50% of biodiversity countrywide since 1977. 30% of remaining biodiversity is found in protected areas, which take up only 8% of the country's total land area. The Amboseli National Park has been identified as having the highest level of habitat irreplaceability of all 33 protected areas in the Somali-Masai Ecoregion. The successional cycles between woody vegetation and grassland affect levels of biodiversity in the region, and are largely driven by the grazing and migratory activities of elephants, as well as human land use and land management practices. Understanding the dynamics of land cover change as a continuum is therefore critical to framing conservation objectives. Currently, few land cover classifications derived from remote sensing are consistent with ACC field data, specifically in terms of differentiation between woody versus grassland vegetation. The ACC is interested in refining these distinctions in the Basin and across the Kenya-Tanzania borderlands and identifying which vegetation types are suffering from degradation and where. Therefore, the goal of this project is to 1) establish phenological profiles of differing vegetation in the Kenya-Tanzania borderlands and 2) characterize trends and drought resilience across the landscape. By analyzing MODIS vegetation indices in 8-day time steps from 2002 to 2012, this study establishes phenological profiles of primary vegetation cover classes including woodlands, bushlands, grasslands, and swamps and identifies areas that are drought-resistant or drought-sensitive at a large scale. By enhancing the ACC's ability to explore and communicate important biological distinctions based on phenology, the project will not only develop a useful tool for the ACC, but further a variety of their conservation and research goals.

Monitoring Tamarisk Defoliation and Scaling Evapotranspiration Using Remote Sensing Data

NASA Astrophysics Data System (ADS)

Dennison, P. E.; Hultine, K. R.; Nagler, P. L.; Miura, T.; Glenn, E. P.; Ehleringer, J. R.

2008-12-01

Non-native tamarisk (Tamarix spp.) has invaded riparian ecosystems throughout the Western United States. Another non-native species, the saltcedar leaf beetle (Diorhabda elongata), has been released in an attempt to control tamarisk infestations. Most efforts directed towards monitoring tamarisk defoliation by Diorhabda have focused on changes in leaf area or sap flux, but these measurements only give a local view of defoliation impacts. We are assessing the ability of remote sensing data for monitoring tamarisk defoliation and measuring resulting changes in evapotranspiration over space and time. Tamarisk defoliation by Diorhabda has taken place during the past two summers along the Colorado River and its tributaries near Moab, Utah. We are using 15 meter spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and 250 meter spatial resolution Moderate Resolution Imaging Spectrometer (MODIS) data to monitor tamarisk defoliation. An ASTER normalized difference vegetation index (NDVI) time series has revealed large drops in index values associated with loss of leaf area due to defoliation. MODIS data have superior temporal monitoring abilities, but at the sacrifice of much lower spatial resolution. A MODIS enhanced vegetation index time series has revealed that for pixels where the percentage of riparian cover is moderate or high, defoliation is detectable even at 250 meter spatial resolution. We are comparing MODIS vegetation index time series to site measurements of leaf area and sap flux. We are also using an evapotranspiration model to scale potential water savings resulting from the biocontrol of tamarisk.

Transitioning from MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for agricultural monitoring.

PubMed

Skakun, Sergii; Justice, Christopher O; Vermote, Eric; Roger, Jean-Claude

2018-01-01

The Visible/Infrared Imager/Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite was launched in 2011, in part to provide continuity with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard National Aeronautics and Space Administration's (NASA) Terra and Aqua remote sensing satellites. The VIIRS will eventually replace MODIS for both land science and applications and add to the coarse-resolution, long term data record. It is, therefore, important to provide the user community with an assessment of the consistency of equivalent products from the two sensors. For this study, we do this in the context of example agricultural monitoring applications. Surface reflectance that is routinely delivered within the M{O,Y}D09 and VNP09 series of products provide critical input for generating downstream products. Given the range of applications utilizing the normalized difference vegetation index (NDVI) generated from M{O,Y}D09 and VNP09 products and the inherent differences between MODIS and VIIRS sensors in calibration, spatial sampling, and spectral bands, the main objective of this study is to quantify uncertainties related the transitioning from using MODIS to VIIRS-based NDVI's. In particular, we compare NDVI's derived from two sets of Level 3 MYD09 and VNP09 products with various spatial-temporal characteristics, namely 8-day composites at 500 m spatial resolution and daily Climate Modelling Grid (CMG) images at 0.05° spatial resolution. Spectral adjustment of VIIRS I1 (red) and I2 (near infra-red - NIR) bands to match MODIS/Aqua b1 (red) and b2 (NIR) bands is performed to remove a bias between MODIS and VIIRS-based red, NIR, and NDVI estimates. Overall, red reflectance, NIR reflectance, NDVI uncertainties were 0.014, 0.029 and 0.056 respectively for the 500 m product and 0.013, 0.016 and 0.032 for the 0.05° product. The study shows that MODIS and VIIRS NDVI data can be used interchangeably for applications with an uncertainty of less than 0.02 to 0.05, depending on the scale of spatial aggregation, which is typically the uncertainty of the individual dataset.

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

USGS Publications Warehouse

Reed, Bradley C.; Budde, Michael E.; Spencer, Page; Miller, Amy E.

2009-01-01

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions. Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3??months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Ni??o winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.

VIP Data Explorer: A Tool for Exploring 30 years of Vegetation Index and Phenology Observations

NASA Astrophysics Data System (ADS)

Barreto-munoz, A.; Didan, K.; Rivera-Camacho, J.; Yitayew, M.; Miura, T.; Tsend-Ayush, J.

2011-12-01

Continuous acquisition of global satellite imagery over the years has contributed to the creation of long term data records from AVHRR, MODIS, TM, SPOT-VGT and other sensors. These records account for 30+ years, as these archives grow, they become invaluable tools for environmental, resources management, and climate studies dealing with trends and changes from local, regional to global scale. In this project, the Vegetation Index and Phenology Lab (VIPLab) is processing 30 years of daily global surface reflectance data into an Earth Science Data Record of Vegetation Index and Phenology metrics. Data from AVHRR (N07,N09,N11 and N14) and MODIS (AQUA and TERRA collection 5) for the periods 1981-1999 and 2000-2010, at CMG resolution were processed into one seamless and sensor independent data record using various filtering, continuity and gap filling techniques (Tsend-Ayush et al., AGU 2011, Rivera-Camacho et al, AGU 2011). An interactive online tool (VIP Data Explorer) was developed to support the visualization, qualitative and quantitative exploration, distribution, and documentation of these records using a simple web 2.0 interface. The VIP Data explorer (http://vip.arizona.edu/viplab_data_explorer) can display any combination of multi temporal and multi source data, enable the quickly exploration and cross comparison of the various levels of processing of this data. It uses the Google Earth (GE) model and was developed using the GE API for images rendering, manipulation and geolocation. These ESDRs records can be quickly animated in this environment and explored for visual trends and anomalies detection. Additionally the tool enables extracting and visualizing any land pixel time series while showing the different levels of processing it went through. User can explore this ESDR database within this data explorer GUI environment, and any desired data can be placed into a dynamic "cart" to be ordered and downloaded later. More functionalities are planned and will be added to this data explorer tool as the project progresses.

[Study on the relationship between Terra-MODIS image and the snail distribution in marshland of Jiangning county, Jiangsu province].

PubMed

Zhang, Bo; Zhang, Zhi-ying; Xu, De-zhong; Sun, Zhi-dong; Zhou, Xiao-nong; Gong, Zi-li; Liu, Shi-jun; Liu, Cheng; Xu, Bin; Zhou, Yun

2003-04-01

To analyze the relationship between the normalized difference vegetation index (NDVI) and the snail distribution in marshland of Jiangning county in Jiangsu province, and to explore the utility of Terra-MODIS image map in the small scale snail habitats surveillance. NDVI were extracted from MODIS image by vector chart of the snail distribution using ArcView 8.1 and ERDAS 8.5 software. The relationship between NDVI and the snail distribution were Investigated using Bivariate correlations and stepwise linear regression. The snail density on marshland was positively correlated with the mean NDVI in the first ten-day of May and the maximum NDVI (N(20max)) in the last ten-day of May. Incidence of pixel with the live snail on marshland was positively correlated with the mean NDVI (N(2mean)) in the first ten-day of May. An equation Y(1) = 0.009 47 x N(20max) (R(2) = 0.73), Y(2) = 0.018 6 x N(2mean) (R(2) = 0.906) was established. This study showed that the Terra-MODIS satellite images reflecting the status of the vegetation on marshland in Jiangning county could be applied to the study to supervise the snail habitat. The results suggested that MODIS images could be used to survey the small scale snail habitats on marshland.

Multitemporal MODIS-EVI relationships with precipitation and temperature at the Santa Rita Experimental Range

Treesearch

Jorry Z. U. Kaurivi; Alfredo R. Huete; Kamel Didan

2003-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides temporal enhanced vegetation index (EVI) data at 250, 500, and 1,000 m spatial resolutions that can be compared to daily, weekly, monthly, and annual weather parameters. A study was conducted at the grassland site (less than 10 percent velvet mesquite [Prosopis juliflora, var. velutina]) and the...

GEOLAND2 global LAI, FAPAR Essential Climate Variables for terrestrial carbon modeling: principles and validation

NASA Astrophysics Data System (ADS)

Baret, F.; Weiss, M.; Lacaze, R.; Camacho, F.; Smets, B.; Pacholczyk, P.; Makhmara, H.

2010-12-01

LAI and fAPAR are recognized as Essential Climate Variables providing key information for the understanding and modeling of canopy functioning. Global remote sensing observations at medium resolution are routinely acquired since the 80’s mainly with AVHRR, SEAWIFS, VEGETATION, MODIS and MERIS sensors. Several operational products have been derived and provide global maps of LAI and fAPAR at daily to monthly time steps. Inter-comparison between MODIS, CYCLOPES, GLOBCARBON and JRC-FAPAR products showed generally consistent seasonality, while large differences in magnitude and smoothness may be observed. One of the objectives of the GEOLAND2 European project is to develop such core products to be used in a range of application services including the carbon monitoring. Rather than generating an additional product from scratch, the version 1 of GEOLAND2 products was capitalizing on the existing products by combining them to retain their pros and limit their cons. For these reasons, MODIS and CYCLOPES products were selected since they both include LAI and fAPAR while having relatively close temporal sampling intervals (8 to 10 days). GLOBCARBON products were not used here because of the too long monthly time step inducing large uncertainties in the seasonality description. JRC-FAPAR was not selected as well to preserve better consistency between LAI and fAPAR products. MODIS and CYCLOPES products were then linearly combined to take advantage of the good performances of CYCLOPES products for low to medium values of LAI and fAPAR while benefiting from the better MODIS performances for the highest LAI values. A training database representative of the global variability of vegetation type and conditions was thus built. A back-propagation neural network was then calibrated to estimate the new LAI and fAPAR products from VEGETATION preprocessed observations. Similarly, the vegetation cover fraction (fCover) was also derived by scaling the original CYCLOPES fCover products. Validation results achieved following the principles proposed by CEOS-LPV show that the new product called GEOV1 behaves as expected with good performances over the whole range of LAI and fAPAR in a temporally smooth and spatially consistent manner. These products will be processed and delivered by VITO in near real time at 1 km spatial resolution and 10 days frequency using a pre-operational production quality tracking system. The entire VEGETATION archive, from 1999 will be processed to provide a consistent time series over both VEGETATION sensors at the same spatial and temporal sampling. A climatology of products computed over the VEGETATION period will be also delivered at the same spatial and temporal sampling, showing average values, between year variability and possible trends over the decade. Finally, the VEGETATION derived time series starting back to 1999 will be completed with consistent products at 4 km spatial resolution derived from the NOAA/AVHRR series to cover the 1981-2010 period.

MODIS Measures Total U.S. Leaf Area

NASA Technical Reports Server (NTRS)

2002-01-01

This composite image over the continental United States was produced with data acquired by the Moderate-resolution Imaging Spectroradiometer (MODIS) during the period March 24 - April 8, 2000. The image is a map of the density of the plant canopy covering the ground. It is the first in a series of images over the continental U.S. produced by the MODIS Land Discipline Group (refer to this site June 2 and 5 for the next two images in the series). The image is a MODIS data product called 'Leaf Area Index,' which is produced by radiometrically measuring the visible and near infrared energy reflected by vegetation. The Leaf Area Index provides information on the structure of plant canopy, showing how much surface area is covered by green foliage relative to total land surface area. In this image, dark green pixels indicate areas where more than 80 percent of the land surface is covered by green vegetation, light green pixels show where leaves cover about 10 to 50 percent of the land surface, and brown pixels show virtually no leaf coverage. The more leaf area a plant has, the more sunlight it can absorb for photosynthesis. Leaf Area Index is one of a new suite of measurements that scientists use to understand how the Earth's land surfaces are changing over time. Their goal is to use these measurements to refine computer models well enough to simulate how the land biosphere influences the natural cycles of water, carbon, and energy throughout the Earth system. This image is the first of its kind from the MODIS instrument, which launched in December 1999 aboard the Terra spacecraft. MODIS began acquiring scientific data on February 24, 2000, when it first opened its aperture door. The MODIS instrument and Terra spacecraft are both managed by NASA's Goddard Space Flight Center, Greenbelt, MD. Image courtesy Steven Running, MODIS Land Group Member, University of Montana

A MODIS-based vegetation index climatology

USDA-ARS?s Scientific Manuscript database

Our motivation here is to provide information for the NASA Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval algorithms (launch in 2014). Vegetation attenuates the signal and the algorithms must correct for this effect. One approach is to use data that describes the canopy water ...

Estimation of photosynthetic capacity using MODIS polarization: 1988 proposal to NASA Headquarters

NASA Technical Reports Server (NTRS)

Vanderbilt, Vern C.

1992-01-01

The remote sensing community has clearly identified the utility of NDVI (normalized difference vegetation index) and SR (simple ratio) and other vegetation indices for estimating such metrics of landscape ecology as green foliar biomass, photosynthetic capacity, and net primary production. Both theoretical and empirical investigations have established cause and effect relationships between the photosynthetic process in plant canopies and these combinations of remotely sensed data. Yet it has also been established that the relationships exhibit considerable variability that appears to be ecosystem-dependent and may represent a source of ecologically important information. The overall hypothesis of this proposal is that the ecosystem-dependent variability in the various vegetation indices is in part attributable to the effects of specular reflection. The polarization channels on MODIS provide the potential to estimate this specularly reflected light and allow the modification of the vegetation indices to better measure the photosynthetic process in plant canopies. In addition, these polarization channels potentially provide additional ecologically important information about the plant canopy.

The SMAP Level-4 ECO Project: Linking the Terrestrial Water and Carbon Cycles

NASA Technical Reports Server (NTRS)

Kolassa, J.; Reichle, R. H.; Liu, Qing; Koster, Randal D.

2017-01-01

The SMAP (Soil Moisture Active Passive) Level-4 projects aims to develop a fully coupled hydrology-vegetation data assimilation algorithm to generate improved estimates of modeled hydrological fields and carbon fluxes. This includes using the new NASA Catchment-CN (Catchment-Carbon-Nitrogen) model, which combines the Catchment land surface hydrology model with dynamic vegetation components from the Community Land Model version 4 (CLM4). As such, Catchment-CN allows a more realistic, fully coupled feedback between the land hydrology and the biosphere. The L4 ECO project further aims to inform the model through the assimilation of Soil Moisture Active Passive (SMAP) brightness temperature observations as well as observations of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of absorbed photosynthetically active radiation (FPAR). Preliminary results show that the assimilation of SMAP observations leads to consistent improvements in the model soil moisture skill. An evaluation of the Catchment-CN modeled vegetation characteristics showed that a calibration of the model's vegetation parameters is required before an assimilation of MODIS FPAR observations is feasible.

Physically-based Canopy Reflectance Model Inversion of Vegetation Biophysical-Structural Information from Terra-MODIS Imagery in Boreal and Mountainous Terrain for Ecosystem, Climate and Carbon Models using the BIOPHYS-MFM Algorithm

NASA Astrophysics Data System (ADS)

Peddle, D. R.; Hall, F.

2009-12-01

The BIOPHYS algorithm provides innovative and flexible methods for the inversion of canopy reflectance models (CRM) to derive essential biophysical structural information (BSI) for quantifying vegetation state and disturbance, and for input to ecosystem, climate and carbon models. Based on spectral, angular, temporal and scene geometry inputs that can be provided or automatically derived, the BIOPHYS Multiple-Forward Mode (MFM) approach generates look-up tables (LUTs) that comprise reflectance data, structural inputs over specified or computed ranges, and the associated CRM output from forward mode runs. Image pixel and model LUT spectral values are then matched. The corresponding BSI retrieved from the LUT matches is output as the BSI results. BIOPHYS-MFM has been extensively used with agencies in Canada and the USA over the past decade (Peddle et al 2000-09; Soenen et al 2005-09; Gamon et al 2004; Cihlar et al 2003), such as CCRS, CFS, AICWR, NASA LEDAPS, BOREAS and MODIS Science Teams, and for the North American Carbon Program. The algorithm generates BSI products such as land cover, biomass, stand volume, stem density, height, crown closure, leaf area index (LAI) and branch area, crown dimension, productivity, topographic correction, structural change from harvest, forest fires and mountain pine beetle damage, and water / hydrology applications. BIOPHYS-MFM has been applied in different locations in Canada (six provinces from Newfoundland to British Columbia) and USA (NASA COVER, MODIS and LEDAPS sites) using 7 different CRM models and a variety of imagery (e.g. MODIS, Landsat, SPOT, IKONOS, airborne MSV, MMR, casi, Probe-1, AISA). In this paper we summarise the BIOPHYS-MFM algorithm and results from Terra-MODIS imagery from MODIS validation sites at Kananaskis Alberta in the Canadian Rocky Mountains, and from the Boreal Ecosystem Atmosphere Study (BOREAS) in Saskatchewan Canada. At the montane Rocky Mountain site, BIOPHYS-MFM density estimates were within ±380 stems/hectare (ha), with horizontal crown radius (HCR) ±0.4m, vertical crown radius (VCR) ±0.6m, and height (HGT) ±0.8m. At the BOREAS site, analysis of single-date MODIS imagery yielded density estimates within ±210 stems/ha, HCR ±0.3m, VCR ±0.5m, and HGT ±0.9m. Higher accuracies at BOREAS compared to the Rocky Mountain site were attributed primarily to the more complex terrain in the mountains, for which good results were obtained given the steep environmental, ecosystem, terrain and biophysical gradients. Further analysis of multiple BOREAS MODIS scenes with different view zenith angles provided convergence of BSI results, with improvements found for density (±42 stems/ha) and HCR (±0.2m). We concluded that good results from MODIS were obtained for both boreal and montane ecosystems. From this and other studies, BIOPHYS-MFM is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying vegetation dynamics for carbon and other models. It is also suitable for integration with forest inventory, monitoring and update needs, and other programs.

MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland

NASA Astrophysics Data System (ADS)

Gong, Z.; Kawamura, K.; Ishikawa, N.; Goto, M.; Wulan, T.; Alateng, D.; Yin, T.; Ito, Y.

2015-11-01

The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002-2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002-2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days, respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation and increasing temperature at the early growing period because of global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.

Validation of the ANOCOVA model for regional scale ECa-ECe calibration

USDA-ARS?s Scientific Manuscript database

Over the past decade two approaches have emerged as the preferred means for assessing salinity at regional scale: (1) vegetative indices from satellite imagery (e.g., MODIS enhanced vegetative index, NDVI, etc.) and (2) analysis of covariance (ANOCOVA) calibration of apparent soil electrical conduct...

Land product validation of MODIS derived FPAR product over the tropical dry-forest of Santa Rosa National Park, Guanacaste, Costa Rica.

NASA Astrophysics Data System (ADS)

Sharp, Iain; Sanchez, Arturo

2017-04-01

Land-product validation of the MODIS derived FPAR product over the tropical dry-forest of Santa Rosa National Park, Guanacaste, Costa Rica. By Iain Sharp & Dr. Arturo Sanchez-Azofeifa In remote sensing, being able to ensure the accuracy of the satellite data being produced remains an issue; this is especially true for phenological variables such as the Fraction of Photosynthetically Active Radiation (FPAR). FPAR, which is considered an essential climate variable by the Global Terrestrial Observation System (GTOS), utilizes the 400-700 nm wavelength range to quantify the total amount of solar radiation available for photosynthetic use. It is a variable that is strongly influenced by the seasonal, diurnal, and optic properties of vegetation making it an accurate representation of vegetation health. Measurements of ground level FPAR can be completed using flux towers along with a limited number of wireless ground sensors, but due to the finite number and location of these towers, many research initiatives instead use the Moderate resolution Imaging Spectroradiometer (MODIS) FPAR product, which converts Leaf Area Index (LAI) to a FPAR value using Beer's Law. This is done despite there being little consensus on whether this is the best method to use for all ecosystems and vegetation types. One particular ecosystem that has had limited study to determine the accuracy of the MODIS derived FPAR products are the Tropical Dry Forests (TDFs) of Latin America. This ecosystem undergoes drastic seasonal changes from leaf off during the dry season to green-up during the wet seasons. This study aims to test the congruency between the MODIS derived FPAR values and ground-based FPAR values in relation to growing season length, growing season start and end dates, the peak and mean of FPAR values, and overall growth/phenological trends at the Santa Rosa National Park Environmental Monitoring Super Site (SR-EMSS) in Costa Rica and FPAR MODIS products. We derive our FPAR from a Wireless Sensor Network (WSN) consisting of more than 50 nodes measuring transmitted PAR, temperature, relative humidity, and soil moisture over custom time intervals ranging from 2-Hz to 15 min since 2013. Our fundamental goal is to demonstrate how accurate and reflective the MODIS derived FPAR product is of TDF phenology. This will be the first step taken in identifying potential problems with the MODIS derived FPAR products over TDFs in the Americas.

FLUXNET: A Global Network of Eddy-Covariance Flux Towers

NASA Astrophysics Data System (ADS)

Cook, R. B.; Holladay, S. K.; Margle, S. M.; Olsen, L. M.; Gu, L.; Heinsch, F.; Baldocchi, D.

2003-12-01

The FLUXNET global network was established to aid in understanding the mechanisms controlling the exchanges of carbon dioxide, water vapor, and energy across a variety of terrestrial ecosystems. Flux tower data are also being used to validate ecosystem model outputs and to provide information for validating remote sensing based products, including surface temperature, reflectance, albedo, vegetation indices, leaf area index, photosynthetically active radiation, and photosynthesis derived from MODIS sensors on the Terra and Aqua satellites. The global FLUXNET database provides consistent and complete flux data to support global carbon cycle science. Currently FLUXNET consists of over 210 sites, with most flux towers operating continuously for 4 years or longer. Gap-filled data are available for 53 sites. The FLUXNET database contains carbon, water vapor, sensible heat, momentum, and radiation flux measurements with associated ancillary and value-added data products. Towers are located in temperate conifer and broadleaf forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra on five continents. Selected MODIS Land products in the immediate vicinity of the flux tower are subsetted and posted on the FLUXNET Web site for 169 flux-towers. The MODIS subsets are prepared in ASCII format for 8-day periods for an area 7 x 7 km around the tower.

Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI.

PubMed

Liu, Shiliang; Cheng, Fangyan; Dong, Shikui; Zhao, Haidi; Hou, Xiaoyun; Wu, Xue

2017-06-23

Spatiotemporal dynamics of aboveground biomass (AGB) is a fundamental problem for grassland environmental management on the Qinghai-Tibet Plateau (QTP). Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data can feasibly be used to estimate AGB at large scales, and their precise validation is necessary to utilize them effectively. In our study, the clip-harvest method was used at 64 plots in QTP grasslands to obtain actual AGB values, and a handheld hyperspectral spectrometer was used to calculate field-measured NDVI to validate MODIS NDVI. Based on the models between NDVI and AGB, AGB dynamics trends during 2000-2012 were analyzed. The results showed that the AGB in QTP grasslands increased during the study period, with 70% of the grasslands undergoing increases mainly in the Qinghai Province. Also, the meadow showed a larger increasing trend than steppe. Future AGB dynamic trends were also investigated using a combined analysis of the slope values and the Hurst exponent. The results showed high sustainability of AGB dynamics trends after the study period. Predictions indicate 60% of the steppe and meadow grasslands would continue to increase in AGB, while 25% of the grasslands would remain in degradation, with most of them distributing in Tibet.

Role of MODIS Vegetation Phenology Products in the ForWarn System for Monitoring of Forest Disturbances in the Conterminous United States

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Norman, S.; Gasser, J.; Smoot, J.; Kuper, P.

2012-12-01

This presentation discusses MODIS vegetation phenology products used in the ForWarn Early Warning System (EWS) tool for near real time regional forest disturbance detection and surveillance at regional to national scales. The ForWarn EWS is being developed by the USDA Forest Service NASA, ORNL, and USGS to aid federal and state forest health management activities. ForWarn employs multiple historical land surface phenology products that are derived from MODIS MOD13 Normalized Difference Vegetation Index (NDVI) data. The latter is temporally processed into phenology products with the Time Series Product Tool (TSPT) and the Phenological Parameter Estimation Tool (PPET) software produced at NASA Stennis Space Center. TSPT is used to effectively noise reduce, fuse, and void interpolate MODIS NDVI data. PPET employs TSPT-processed NDVI time series data as an input, outputting multiple vegetation phenology products at a 232 meter resolution for 2000 to 2011, including NDVI magnitude and day of year products for seven key points along the growing season (peak of growing season and the minima, 20%, and 80% of the peak NDVI for both the left and right side of growing season), cumulative NDVI integral products for the most active part of the growing season and sequentially across the growing season at 8 day intervals, and maximum value NDVI products composited at 24 day intervals in which each product date has 8 days of overlap between the previous and following product dates. MODIS NDVI phenology products are also used to compute nationwide near real time forest change products every 8 days. These include percent change in forest NDVI products that compare the current NDVI from USGS eMODIS products to historical MODIS MOD13 NDVI. For each date, three forest change products are produced using three different maximum value NDVI baselines (from the previous year, three previous years, and all previous years). All change products are output with a rainbow color table in which forests with the most severe NDVI decreases are assigned hot colors (yellow to red) and forests with prominent NDVI increases are assigned cold colors (blue tones). All mentioned products have been integrated as data layers into ForWarn's geospatial data viewer known as the U.S. Forest Change Assessment Viewer (FCAV). The latter is used to view and assess the context of the mentioned forest change products with respect to ancillary data layers, such as land cover, elevation, hydrologic features, climatic data, storm data, aerial disturbance surveys, fire data, and land ownership. The FCAV also includes a temporal NDVI profiler for viewing phenological change in multi-year NDVI associated with known or suspected regionally apparent forest disturbances (e.g., from fire and insects). ForWarn forest change products have been used to detect, track, and assess several biotic and abiotic regional forest disturbance events across the country, including ephemeral and longer lasting damage from storms, drought, and insects. Such change products are most effective for viewing severe disturbance patches of multiple pixels. MODIS vegetation phenology products contribute vital current information on forest conditions to the ForWarn system and this role is expected to grow as these products are refined and derivative products are added.

Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument

NASA Technical Reports Server (NTRS)

Tan, Bin; Hu, Jiannan; Huang, Dong; Yang, Wenze; Zhang, Ping; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2005-01-01

The first significant processing of Terra MODIS data, called Collection 3, covered the period from November 2000 to December 2002. The Collection 3 leaf area index (LAI) and fraction vegetation absorbed photosynthetically active radiation (FPAR) products for broadleaf crops exhibited three anomalies (a) high LAI values during the peak growing season, (b) differences in LAI seasonality between the radiative transfer-based main algorithm and the vegetation index based back-up algorithm, and (c) too few retrievals from the main algorithm during the summer period when the crops are at full flush. The cause of these anomalies is a mismatch between reflectances modeled by the algorithm and MODIS measurements. Therefore, the Look-Up-Tables accompanying the algorithm were revised and implemented in Collection 4 processing. The main algorithm with the revised Look-Up-Tables generated retrievals for over 80% of the pixels with valid data. Retrievals from the back-up algorithm, although few, should be used with caution as they are generated from surface reflectances with high uncertainties.

Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods

Treesearch

Zhuosen Wang; Crystal B. Schaaf; Alan H. Strahler; Mark J. Chopping; Miguel O. Román; Yanmin Shuai; Curtis E. Woodcock; David Y. Hollinger; David R. Fitzjarrald

2014-01-01

This study assesses the Moderate-resolution Imaging Spectroradiometer (MODIS) BRDF/albedo 8 day standard product and products from the daily Direct Broadcast BRDF/albedo algorithm, and shows that these products agree well with ground-based albedo measurements during the more difficult periods of vegetation dormancy and snow cover. Cropland, grassland, deciduous and...

Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: Land use/land cover, vegetation cover changes estimated using multi-source satellite data

NASA Astrophysics Data System (ADS)

Zhang, Jixian; Zhengjun, Liu; Xiaoxia, Sun

2009-12-01

The eco-environment in the Three Gorges Reservoir Area (TGRA) in China has received much attention due to the construction of the Three Gorges Hydropower Station. Land use/land cover changes (LUCC) are a major cause of ecological environmental changes. In this paper, the spatial landscape dynamics from 1978 to 2005 in this area are monitored and recent changes are analyzed, using the Landsat TM (MSS) images of 1978, 1988, 1995, 2000 and 2005. Vegetation cover fractions for a vegetation cover analysis are retrieved from MODIS/Terra imagery from 2000 to 2006, being the period before and after the rising water level of the reservoir. Several analytical indices have been used to analyze spatial and temporal changes. Results indicate that cropland, woodland, and grassland areas reduced continuously over the past 30 years, while river and built-up area increased by 2.79% and 4.45% from 2000 to 2005, respectively. The built-up area increased at the cost of decreased cropland, woodland and grassland. The vegetation cover fraction increased slightly. We conclude that significant changes in land use/land cover have occurred in the Three Gorges Reservoir Area. The main cause is a continuous economic and urban/rural development, followed by environmental management policies after construction of the Three Gorges Dam.

Remote Sensing Time Series Product Tool

NASA Technical Reports Server (NTRS)

Predos, Don; Ryan, Robert E.; Ross, Kenton W.

2006-01-01

The TSPT (Time Series Product Tool) software was custom-designed for NASA to rapidly create and display single-band and band-combination time series, such as NDVI (Normalized Difference Vegetation Index) images, for wide-area crop surveillance and for other time-critical applications. The TSPT, developed in MATLAB, allows users to create and display various MODIS (Moderate Resolution Imaging Spectroradiometer) or simulated VIIRS (Visible/Infrared Imager Radiometer Suite) products as single images, as time series plots at a selected location, or as temporally processed image videos. Manually creating these types of products is extremely labor intensive; however, the TSPT development tool makes the process simplified and efficient. MODIS is ideal for monitoring large crop areas because of its wide swath (2330 km), its relatively small ground sample distance (250 m), and its high temporal revisit time (twice daily). Furthermore, because MODIS imagery is acquired daily, rapid changes in vegetative health can potentially be detected. The new TSPT technology provides users with the ability to temporally process high-revisit-rate satellite imagery, such as that acquired from MODIS and from its successor, the VIIRS. The TSPT features the important capability of fusing data from both MODIS instruments onboard the Terra and Aqua satellites, which drastically improves cloud statistics. With the TSPT, MODIS metadata is used to find and optionally remove bad and suspect data. Noise removal and temporal processing techniques allow users to create low-noise time series plots and image videos and to select settings and thresholds that tailor particular output products. The TSPT GUI (graphical user interface) provides an interactive environment for crafting what-if scenarios by enabling a user to repeat product generation using different settings and thresholds. The TSPT Application Programming Interface provides more fine-tuned control of product generation, allowing experienced programmers to bypass the GUI and to create more user-specific output products, such as comparison time plots or images. This type of time series analysis tool for remotely sensed imagery could be the basis of a large-area vegetation surveillance system. The TSPT has been used to generate NDVI time series over growing seasons in California and Argentina and for hurricane events, such as Hurricane Katrina.

Producing Global Science Products for the Moderate Resolution Imaging Spectroradiometer (MODIS) in MODAPS

NASA Technical Reports Server (NTRS)

Masuoka, Edward J.; Tilmes, Curt A.; Ye, Gang; Devine, Neal; Smith, David E. (Technical Monitor)

2000-01-01

The MODerate resolution Imaging Spectroradiometer (MODIS) was launched on NASA's EOS-Terra spacecraft December 1999. With 36 spectral bands covering the visible, near wave and short wave infrared. MODIS produces over 40 global science data products, including sea surface temperature, ocean color, cloud properties, vegetation indices land surface temperature and land cover change. The MODIS Data Processing System (MODAPS) produces 400 GB/day of global MODIS science products from calibrated radiances generated in the Earth Observing System Data and Information System (EOSDIS). The science products are shipped to the EOSDIS for archiving and distribution to the public. An additional 200 GB of products are shipped each day to MODIS team members for quality assurance and validation of their products. In the sections that follow, we will describe the architecture of the MODAPS, identify processing bottlenecks encountered in scaling MODAPS from 50 GB/day backup system to a 400 GB/day production system and discuss how these were handled.

Comparative Analysis of Aerosol Retrievals from MODIS, OMI and MISR Over Sahara Region

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Hsu, C.; Terres, O.; Leptoukh, G.; Kalashnikova, O.; Korkin, S.

2011-01-01

MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.

Estimating Per-Pixel GPP of the Contiguous USA Directly from MODIS EVI Data

NASA Astrophysics Data System (ADS)

Rahman, A. F.; Sims, D. A.; El-Masri, B. Z.; Cordova, V. D.

2005-12-01

We estimated gross primary production (GPP) of the contiguous USA using enhanced vegetation index (EVI) data from NASA's moderate resolution imaging spectroradiometer (MODIS). Based on recently published values of correlation coefficients between EVI and GPP of North American vegetations, we derived GPP maps of the contiguous USA for 2001-2004, which included one La Nina year and three moderately El Nino years. The product was a truly per-pixel GPP estimate (named E-GPP), in contrast to the pseudo-continuous MOD17, the standard MODIS GPP product. We compared E-GPP with fine-scale experimental GPP data and MOD17 estimates from three Bigfoot experimental sites, and also with MOD17 estimates from the whole contiguous USA for the above-mentioned four years. For each of the '7 by 7' km Bigfoot experimental sites, E-GPP was able to track the primary production activity during the green-up period while MOD17 failed to do so. The E-GPP estimates during peak production season were similar to those from Bigfoot and MOD17 for most vegetation types except for the deciduous types, where it was lower. Annual E-GPP of the Bigfoot sites compared well with Bigfoot experimental GPP (r = 0.71) and MOD17 (r = 0.78). But for the contiguous USA for 2001-2004, annual E-GPP showed disagreement with MOD17 in both magnitude and seasonal trends for deciduous forests and grass lands. In this study we explored the reasons for this mismatch between E-GPP and MOD17 and also analyzed the uncertainties in E-GPP across multiple spatial scales. Our results show that the E-GPP, based on a simple regression model, can work as a robust alternative to MOD17 for large-area annual GPP estimation. The relative advantages of E-GPP are that it is truly per-pixel, solely dependent on remotely sensed data that is routinely available from NASA, easy to compute and has the potential of being used as an operational product.

View Angle Effects on MODIS Snow Mapping in Forests

NASA Technical Reports Server (NTRS)

Xin, Qinchuan; Woodcock, Curtis E.; Liu, Jicheng; Tan, Bin; Melloh, Rae A.; Davis, Robert E.

2012-01-01

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

Advances in regional crop yield estimation over the United States using satellite remote sensing data

NASA Astrophysics Data System (ADS)

Johnson, D. M.; Dorn, M. F.; Crawford, C.

2015-12-01

Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a variety of data mining and modeling options and results strongly lean toward solutions of ensemble decision trees like Cubist and Random Forest. Those comparisons of what are seen as best will be also be shown. And finally, important model refinements accounting for temporal and spatial trends have also been considered and results will be presented.

MODIS land data at the EROS data center DAAC

USGS Publications Warehouse

Jenkerson, Calli B.; Reed, B.C.

2001-01-01

The US Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center (EDC) in Sioux Falls, SD, USA, is the primary national archive for land processes data and one of the National Aeronautics and Space Administration's (NASA) Distributed Active Archive Centers (DAAC) for the Earth Observing System (EOS). One of EDC's functions as a DAAC is the archival and distribution of Moderate Resolution Spectroradiometer (MODIS) Land Data collected from the Earth Observing System (EOS) satellite Terra. More than 500,000 publicly available MODIS land data granules totaling 25 Terabytes (Tb) are currently stored in the EDC archive. This collection is managed, archived, and distributed by EOS Data and Information System (EOSDIS) Core System (ECS) at EDC. EDC User Services support the use of MODIS Land data, which include land surface reflectance/albedo, temperature/emissivity, vegetation characteristics, and land cover, by responding to user inquiries, constructing user information sites on the EDC web page, and presenting MODIS materials worldwide.

Verification and Validation of NASA-Supported Enhancements to Decision Support Tools of PECAD

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; McKellip, Rodney; Moore, Roxzana F.; Fendley, Debbie

2005-01-01

This section of the evaluation report summarizes the verification and validation (V&V) of recently implemented, NASA-supported enhancements to the decision support tools of the Production Estimates and Crop Assessment Division (PECAD). The implemented enhancements include operationally tailored Moderate Resolution Imaging Spectroradiometer (MODIS) products and products of the Global Reservoir and Lake Monitor (GRLM). The MODIS products are currently made available through two separate decision support tools: the MODIS Image Gallery and the U.S. Department of Agriculture (USDA) Foreign Agricultural Service (FAS) MODIS Normalized Difference Vegetation Index (NDVI) Database. Both the Global Reservoir and Lake Monitor and MODIS Image Gallery provide near-real-time products through PECAD's CropExplorer. This discussion addresses two areas: 1. Assessments of the standard NASA products on which these enhancements are based. 2. Characterizations of the performance of the new operational products.

Biospheric Monitoring and Ecological Forecasting using EOS/MODIS data, ecosystem modeling, planning and scheduling technologies

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Votava, P.; Golden, K.; Hashimoto, H.; Jolly, M.; White, M.; Running, S.; Coughlan, J.

2003-12-01

The latest generation of NASA Earth Observing System satellites has brought a new dimension to continuous monitoring of the living part of the Earth System, the Biosphere. EOS data can now provide weekly global measures of vegetation productivity and ocean chlorophyll, and many related biophysical factors such as land cover changes or snowmelt rates. However, information with the highest economic value would be forecasting impending conditions of the biosphere that would allow advanced decision-making to mitigate dangers, or exploit positive trends. We have developed a software system called the Terrestrial Observation and Prediction System (TOPS) to facilitate rapid analysis of ecosystem states/functions by integrating EOS data with ecosystem models, surface weather observations and weather/climate forecasts. Land products from MODIS (Moderate Resolution Imaging Spectroradiometer) including land cover, albedo, snow, surface temperature, leaf area index are ingested into TOPS for parameterization of models and for verifying model outputs such as snow cover and vegetation phenology. TOPS is programmed to gather data from observing networks such as USDA soil moisture, AMERIFLUX, SNOWTEL to further enhance model predictions. Key technologies enabling TOPS implementation include the ability to understand and process heterogeneous-distributed data sets, automated planning and execution of ecosystem models, causation analysis for understanding model outputs. Current TOPS implementations at local (vineyard) to global scales (global net primary production) can be found at http://www.ntsg.umt.edu/tops.

Modeling gross primary production of an evergreen needleleaf forest using MODIS and climate data

Treesearch

Xiangming Xiao; Qingyuan Zhang; David Hollinger; John Aber; Berrien, III Moore

2005-01-01

Forest canopies are composed of photosynthetically active vegetation (PAV, chloroplasts) and nonphotosynthetic vegetation (NPV, e.g., cell wall, vein, branch). The fraction of photosynthetically active radiation (PAR) absorbed by the canopy (FAPAR) should be partitioned into FAPARPAV and FAPARNPV. Gross primary production (...

Toward daily monitoring of vegetation conditions at field scale through fusing data from multiple sensors

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires remote sensing data at fine spatial and temporal resolution. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for crop and rangeland monitoring. The Landsat satellite s...

ANALYSIS OF YEAR 2002 SEASONAL FOREST DYNAMICS USING TIME SERIES IN SITU LAI MEASUREMENTS AND MODIS LAI SATELLITE PRODUCTS

EPA Science Inventory

Multitemporal satellite images are the standard basis for regional-scale land-cover (LC) change detection. However, embedded in the data are the confounding effects of vegetation dynamics (phenology). As photosynthetic vegetation progresses through its annual cycle, the spectral ...

Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia

USGS Publications Warehouse

Wakie, Tewodros; Evangelista, Paul H.; Jarnevich, Catherine S.; Laituri, Melinda

2014-01-01

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species-occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

Pre-Launch Tasks Proposed in our Contract of December 1991

NASA Technical Reports Server (NTRS)

1998-01-01

We propose, during the pre-EOS phase to: (1) develop, with other MODIS Team Members, a means of discriminating different major biome types with NDVI and other AVHRR-based data; (2) develop a simple ecosystem process model for each of these biomes, BIOME-BGC; (3) relate the seasonal trend of weekly composite NDVI to vegetation phenology and temperature limits to develop a satellite defined growing season for vegetation; and (4) define physiologically based energy to mass conversion factors for carbon and water for each biome. Our final core at-launch product will be simplified, completely satellite driven biome specific models for net primary production. We will build these biome specific satellite driven algorithms using a family of simple ecosystem process models as calibration models, collectively called BIOME-BGC, and establish coordination with an existing network of ecological study sites in order to test and validate these products. Field datasets will then be available for both BIOME-BGC development and testing, use for algorithm developments of other MODIS Team Members, and ultimately be our first test point for MODIS land vegetation products upon launch. We will use field sites from the National Science Foundation Long-Term Ecological Research network, and develop Glacier National Park as a major site for intensive validation.

Pre-Launch Tasks Proposed in our Contract of December 1991

NASA Technical Reports Server (NTRS)

Running, Steven W.; Nemani, Ramakrishna R.; Glassy, Joseph

1997-01-01

We propose, during the pre-EOS phase to: (1) develop, with other MODIS Team Members, a means of discriminating different major biome types with NDVI and other AVHRR-based data. (2) develop a simple ecosystem process model for each of these biomes, BIOME-BGC (3) relate the seasonal trend of weekly composite NDVI to vegetation phenology and temperature limits to develop a satellite defined growing season for vegetation; and (4) define physiologically based energy to mass conversion factors for carbon and water for each biome. Our final core at-launch product will be simplified, completely satellite driven biome specific models for net primary production. We will build these biome specific satellite driven algorithms using a family of simple ecosystem process models as calibration models, collectively called BIOME-BGC, and establish coordination with an existing network of ecological study sites in order to test and validate these products. Field datasets will then be available for both BIOME-BGC development and testing, use for algorithm developments of other MODIS Team Members, and ultimately be our first test point for MODIS land vegetation products upon launch. We will use field sites from the National Science Foundation Long-Term Ecological Research network, and develop Glacier National Park as a major site for intensive validation.

Estimating of gross primary production in an Amazon-Cerrado transitional forest using MODIS and Landsat imagery.

PubMed

Danelichen, Victor H M; Biudes, Marcelo S; Velasque, Maísa C S; Machado, Nadja G; Gomes, Raphael S R; Vourlitis, George L; Nogueira, José S

2015-09-01

The acceleration of the anthropogenic activity has increased the atmospheric carbon concentration, which causes changes in regional climate. The Gross Primary Production (GPP) is an important variable in the global carbon cycle studies, since it defines the atmospheric carbon extraction rate from terrestrial ecosystems. The objective of this study was to estimate the GPP of the Amazon-Cerrado Transitional Forest by the Vegetation Photosynthesis Model (VPM) using local meteorological data and remote sensing data from MODIS and Landsat 5 TM reflectance from 2005 to 2008. The GPP was estimated using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) calculated by MODIS and Landsat 5 TM images. The GPP estimates were compared with measurements in a flux tower by eddy covariance. The GPP measured in the tower was consistent with higher values during the wet season and there was a trend to increase from 2005 to 2008. The GPP estimated by VPM showed the same increasing trend observed in measured GPP and had high correlation and Willmott's coefficient and low error metrics in comparison to measured GPP. These results indicated high potential of the Landsat 5 TM images to estimate the GPP of Amazon-Cerrado Transitional Forest by VPM.

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia.

PubMed

Hüttich, Christian; Herold, Martin; Strohbach, Ben J; Dech, Stefan

2011-05-01

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Evaluation of the Event Driven Phenology Model Coupled with the VegET Evapotranspiration Model Through Comparisons with Reference Datasets in a Spatially Explicit Manner

NASA Technical Reports Server (NTRS)

Kovalskyy, V.; Henebry, G. M.; Adusei, B.; Hansen, M.; Roy, D. P.; Senay, G.; Mocko, D. M.

2011-01-01

A new model coupling scheme with remote sensing data assimilation was developed for estimation of daily actual evapotranspiration (ET). The scheme represents a mix of the VegET, a physically based model to estimate ET from a water balance, and an event driven phenology model (EDPM), where the EDPM is an empirically derived crop specific model capable of producing seasonal trajectories of canopy attributes. In this experiment, the scheme was deployed in a spatially explicit manner within the croplands of the Northern Great Plains. The evaluation was carried out using 2007-2009 land surface forcing data from the North American Land Data Assimilation System (NLDAS) and crop maps derived from remotely sensed data of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compared the canopy parameters produced by the phenology model with normalized difference vegetation index (NDVI) data derived from the MODIS nadir bi-directional reflectance distribution function (BRDF) adjusted reflectance (NBAR) product. The expectations of the EDPM performance in prognostic mode were met, producing determination coefficient (r2) of 0.8 +/-.0.15. Model estimates of NDVI yielded root mean square error (RMSE) of 0.1 +/-.0.035 for the entire study area. Retrospective correction of canopy dynamics with MODIS NDVI brought the errors down to just below 10% of observed data range. The ET estimates produced by the coupled scheme were compared with ones from the MODIS land product suite. The expected r2=0.7 +/-.15 and RMSE = 11.2 +/-.4 mm per 8 days were met and even exceeded by the coupling scheme0 functioning in both prognostic and retrospective modes. Minor setbacks of the EDPM and VegET performance (r2 about 0.5 and additional 30 % of RMSR) were found on the peripheries of the study area and attributed to the insufficient EDPM training and to spatially varying accuracy of crop maps. Overall the experiment provided sufficient evidence of soundness and robustness of the EDPM and VegET coupling scheme, assuring its potential for spatially explicit applications.

Detecting Soil Moisture Related Impacts on Gross Primary Productivity using the MODIS-based Photochemical Reflectance Index

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Running, S. W.; Ballantyne, A.; Guan, K.; Huemmrich, K. F.

2016-12-01

Satellite remote sensing provides continuous observations of vegetation properties that can be used to estimate ecosystem gross primary production (GPP). The Photochemical Reflectance Index (PRI) has been shown to be sensitive to photosynthetic light use efficiency (LUE), GPP and canopy water-stress. The NASA EOS MODIS (Moderate Resolution Imaging Spectroradiometer) sensor provides potential PRI estimation globally at daily time step and 1-km spatial resolution for more than 10 years. Here, we use the MODIS based PRI with eddy covariance CO2 flux measurements and meteorological observations from 20 tower sites representing 5 major plant functional types (PFT) within the continental USA (CONUS) to assess GPP sensitivity to seasonal water supply variability. The sPRI (scaled PRI) derived using MODIS band 13 as a reference band (sPRI13) generally shows higher correspondence with tower GPP observations than other potential MODIS reference bands (MODIS band 1, 4, 10 and 12). The sPRI13 was used to represent soil moisture related water supply constraints to LUE within a terrestrial carbon flux model to estimate GPP (GPPPRI). The GPPPRI calculations show generally strong relationships with tower GPP observations (0.457 ≤ R2 ≤ 0.818), except for lower GPPPRI performance over evergreen needleleaf forest (ENF) sites. A regional model sensitivity analysis using the sPRI13 as a proxy for soil moisture related water supply limits indicated that water restrictions limit GPP over more than 21% of the CONUS domain, particularly in northwest and southwest CONUS subregions, and drier climate areas where atmospheric moisture deficits (VPD) alone are insufficient to represent both atmosphere demand and soil water supply controls affecting productivity. Our results indicate strong potential of the MODIS sPRI13 to represent GPP sensitivity to seasonal soil moisture related water supply variability, with enhanced (1-km resolution) delineation of these processes closer to the scale of in situ tower observations, providing an effective tool to characterize sub-grid spatial heterogeneity in soil moisture related water supply controls that inform coarser scale observations and estimates determined from other satellite observations and global carbon, and climate models.

New Features of the Collection 4 MODIS LAI and FPAR Product

NASA Astrophysics Data System (ADS)

Bin, T.; Yang, W.; Dong, H.; Shabanov, N.; Knyazikhin, Y.; Myneni, R.

2003-12-01

An algorithm based on physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from MODIS surface reflectance data was developed, prototyped and is in operational production at NASA computing facilities since June 2000. This poster highlights recent changes in the operational MODIS LAI and FPAR algorithm introduced for collection 4 data reprocessing. The changes to the algorithm are targeted to improve agreement of retrieved LAI and FPAR with corresponding field measurements, improve consistency of Quality Control (QC) definitions and miscellaneous bug fixes as summarized below. * Improvement of LUTs for the main and back-up algorithms for biomes 1 and 3. Benefits: a) increase in quality of retrievals; b) non-physical peaks in the global LAI distribution have been removed; c) improved agreement with field measurements * Improved QA scheme. Benefits: a) consistency between MODLAND and SCF quality flags has been achieved; b)ambiguity in QA has been resolved * New 8-day compositing scheme. Benefits: a) compositing over best quality retrievals, instead of all retrievals; b) lowers LAI values, decreases saturation and number of pixels generated by the back-up * At-launch static IGBP land cover, input to the LAI/FPAR algorithm, was replaced with the MODIS land cover map. Benefits: a) crosswalking of 17 classes IGBP scheme to 6-biome LC has been eliminated; b) uncertainties in the MODIS LAI/FPAR product due to uncertainties in land cover map have been reduced

Downscaling of Seasonal Landsat-8 and MODIS Land Surface Temperature (LST) in Kolkata, India

NASA Astrophysics Data System (ADS)

Garg, R. D.; Guha, S.; Mondal, A.; Lakshmi, V.; Kundu, S.

2017-12-01

The quality of life of urban people is affected by urban heat environment. The urban heat studies can be carried out using remotely sensed thermal infrared imagery for retrieving Land Surface Temperature (LST). Currently, high spatial resolution (<200 m) thermal images are limited and their temporal resolution is low (e.g., 17 days of Landsat-8). Coarse spatial resolution (1000 m) and high temporal resolution (daily) thermal images of MODIS (Moderate Resolution Imaging Spectroradiometer) are frequently available. The present study is to downscale spatially coarser resolution of the thermal image to fine resolution thermal image using regression based downscaling technique. This method is based on the relationship between (LST) and vegetation indices (e.g., Normalized Difference Vegetation Index or NDVI) over a heterogeneous landscape. The Kolkata metropolitan city, which experiences a tropical wet-and-dry type of climate has been selected for the study. This study applied different seasonal open source satellite images viz., Landsat-8 and Terra MODIS. The Landsat-8 images are aggregated at 960 m resolution and downscaled into 480, 240 120 and 60 m. Optical and thermal resolution of Landsat-8 and MODIS are 30 m and 60 m; 250 m and 1000 m respectively. The homogeneous land cover areas have shown better accuracy than heterogeneous land cover areas. The downscaling method plays a crucial role while the spatial resolution of thermal band renders it unable for advanced study. Key words: Land Surface Temperature (LST), Downscale, MODIS, Landsat, Kolkata

MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests

NASA Astrophysics Data System (ADS)

Testa, S.; Soudani, K.; Boschetti, L.; Borgogno Mondino, E.

2018-02-01

Monitoring forest phenology allows us to study the effects of climate change on vegetated land surfaces. Daily and composite time series (TS) of several vegetation indices (VIs) from MODerate resolution Imaging Spectroradiometer (MODIS) data have been widely used in scientific works for phenological studies since the beginning of the MODIS mission. The objective of this work was to use MODIS data to find the best VI/TS combination to estimate start-of-season (SOS) and end-of-season (EOS) dates across 50 temperate deciduous forests. Our research used as inputs 2001-2012 daily reflectance from MOD09GQ/MOD09GA products and 16-day composite VIs from the MOD13Q1 dataset. The 50 pixels centered on the 50 forest plots were extracted from the above-mentioned MODIS imagery; we then generated 5 different types of TS (1 daily from MOD09 and 4 composite from MOD13Q1) and used all of them to implement 6 VIs, obtaining 30 VI/TS combinations. SOS and EOS estimates were determined for each pixel/year and each VI/TS combination. SOS/EOS estimations were then validated against ground phenological observations. Results showed that, in our test areas, composite TS, if actual acquisition date is considered, performed mostly better than daily TS. EVI, WDRVI0.20 and NDVI were more suitable to SOS estimation, while WDRVI0.05 and EVI were more convenient in estimating early and advanced EOS, respectively.

An annual plant growth proxy in the Mojave Desert using MODIS-EVI data

USGS Publications Warehouse

Wallace, C.S.A.; Thomas, K.A.

2008-01-01

In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth models as proxies for annual plant cover using metrics that captured phenological variability in Moderate-Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite images. We used landscape phenologies revealed in MODIS data together with ecological knowledge of annual plant seasonality to develop a suite of metrics to describe annual growth on a yearly basis. Each of these metrics was applied to temporally-composited MODIS-EVI images to develop a relative model of annual growth. Each model was evaluated by testing how well it predicted field estimates of annual cover collected during 2003 and 2005 at the Mojave National Preserve. The best performing metric was the spring difference metric, which compared the average of three spring MODIS-EVI composites of a given year to that of 2002, a year of record drought. The spring difference metric showed correlations with annual plant cover of R2 = 0.61 for 2005 and R 2 = 0.47 for 2003. Although the correlation is moderate, we consider it supportive given the characteristics of the field data, which were collected for a different study in a localized area and are not ideal for calibration to MODIS pixels. A proxy for annual growth potential was developed from the spring difference metric of 2005 for use as an environmental data layer in desert tortoise habitat modeling. The application of the spring difference metric to other imagery years presents potential for other applications such as fuels, invasive species, and dust-emission monitoring in the Mojave Desert.

Use of Moderate-Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos

NASA Astrophysics Data System (ADS)

Roesch, Andreas; Schaaf, Crystal; Gao, Feng

2004-06-01

Moderate-Resolution Imaging Spectroradiometer (MODIS) surface albedo at high spatial and spectral resolution is compared with other remotely sensed climatologies, ground-based data, and albedos simulated with the European Center/Hamburg 4 (ECHAM4) global climate model at T42 resolution. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterizations in weather forecast and climate models. The remotely sensed PINKER surface albedo climatology follows the MODIS estimates fairly well in both the visible and near-infrared spectra, whereas ECHAM4 simulates high positive albedo biases over snow-covered boreal forests and the Himalayas. In contrast, the ECHAM4 albedo is probably too low over the Sahara sand desert and adjacent steppes. The study clearly indicates that neglecting albedo variations within T42 grid boxes leads to significant errors in the simulated regional climate and horizontal fluxes, mainly in mountainous and/or snow-covered regions. MODIS surface albedo at 0.05 resolution agrees quite well with in situ field measurements collected at Baseline Surface Radiation Network (BSRN) sites during snow-free periods, while significant positive biases are found under snow-covered conditions, mainly due to differences in the vegetation cover at the BSRN site (short grass) and the vegetation within the larger MODIS grid box. Black sky (direct beam) albedo from the MODIS bidirectional reflectance distribution function model captures the diurnal albedo cycle at BSRN sites with sufficient accuracy. The greatest negative biases are generally found when the Sun is low. A realistic approach for relating albedo and zenith angle has been proposed. Detailed evaluations have demonstrated that ignoring the zenith angle dependence may lead to significant errors in the surface energy balance.

An Annual Plant Growth Proxy in the Mojave Desert Using MODIS-EVI Data.

PubMed

Wallace, Cynthia S A; Thomas, Kathryn A

2008-12-03

In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth models as proxies for annual plant cover using metrics that captured phenological variability in Moderate-Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite images. We used landscape phenologies revealed in MODIS data together with ecological knowledge of annual plant seasonality to develop a suite of metrics to describe annual growth on a yearly basis. Each of these metrics was applied to temporally-composited MODIS-EVI images to develop a relative model of annual growth. Each model was evaluated by testing how well it predicted field estimates of annual cover collected during 2003 and 2005 at the Mojave National Preserve. The best performing metric was the spring difference metric, which compared the average of three spring MODIS-EVI composites of a given year to that of 2002, a year of record drought. The spring difference metric showed correlations with annual plant cover of R² = 0.61 for 2005 and R² = 0.47 for 2003. Although the correlation is moderate, we consider it supportive given the characteristics of the field data, which were collected for a different study in a localized area and are not ideal for calibration to MODIS pixels. A proxy for annual growth potential was developed from the spring difference metric of 2005 for use as an environmental data layer in desert tortoise habitat modeling. The application of the spring difference metric to other imagery years presents potential for other applications such as fuels, invasive species, and dust-emission monitoring in the Mojave Desert.

Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates

NASA Astrophysics Data System (ADS)

Sánchez-Ruiz, Sergio; Piles, María; Sánchez, Nilda; Martínez-Fernández, José; Vall-llossera, Mercè; Camps, Adriano

2014-08-01

Sensors in the range of visible and near-shortwave-thermal infrared regions can be used in combination with passive microwave observations to provide soil moisture maps at much higher spatial resolution than the original resolution of current radiometers. To do so, a new downscaling algorithm ultimately based on the land surface temperature (LST) - Normalized Difference Vegetation Index (NDVI) - Brightness Temperature (TB) relationship is used, in which shortwave infrared indices are used as vegetation descriptors, instead of the more common near infrared ones. The theoretical basis of those indices, calculated as the normalized ratio of the 1240, 1640 and 2130 nm shortwave infrared (SWIR) bands and the 858 nm near infrared (NIR) band indicate that they are able to provide estimates of the vegetation water content. These so-called water indices extracted from MODIS products, have been used together with MODIS LST, and SMOS TB to improve the spatial resolution of ∼40 km SMOS soil moisture estimates. The aim was to retrieve soil moisture maps with the same accuracy as SMOS, but at the same resolution of the MODIS dataset, i.e., 500 m, which were then compared against in situ measurements from the REMEDHUS network in Spain. Results using two years of SMOS and MODIS data showed a similar performance for the four indices, with slightly better results when using the index derived from the first SWIR band. For the areal-average, a coefficient of correlation (R) of ∼0.61 and ∼0.72 for the morning and afternoon orbits, respectively, and a centered root mean square difference (cRMSD) of ∼0.04 m3 m-3 for both orbits was obtained. A twofold improvement of the current versions of this downscaling approach has been achieved by using more frequent and higher spatial resolution water indexes as vegetation descriptors: (1) the spatial resolution of the resulting soil moisture maps can be enhanced from ∼40 km up to 500 m, and (2) more accurate soil moisture maps (in terms of R and cRMSD) can be obtained, especially in periods of high vegetation activity. The results of this study support the use of high resolution LST and SWIR-based vegetation indices to disaggregate SMOS observations down to 500 m soil moisture maps, meeting the needs of fine-scale hydrological applications.

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements

PubMed Central

Fontana, Fabio; Rixen, Christian; Jonas, Tobias; Aberegg, Gabriel; Wunderle, Stefan

2008-01-01

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG). We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand large-scale vegetation growth dynamics above the tree line in the European Alps. PMID:27879852

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

PubMed

Fontana, Fabio; Rixen, Christian; Jonas, Tobias; Aberegg, Gabriel; Wunderle, Stefan

2008-04-23

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

MODIS NDVI and vegetation phenology dynamics in the Inner Mongolia grassland

NASA Astrophysics Data System (ADS)

Gong, Z.; Kawamura, K.; Ishikawa, N.; Goto, M.; Wulan, T.; Alateng, D.; Yin, T.; Ito, Y.

2015-08-01

The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002-2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002-2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation (February-May) and increasing temperature during the growing period because of the global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.

Sensitivity analysis of the Commonly Used Drought Indices on the different land use Types - Case Study over Turkey

NASA Astrophysics Data System (ADS)

Ersoy, E. N.; Hüsami Afşar, M.; Bulut, B.; Onen, A.; Yilmaz, M. T.

2017-12-01

Droughts are climatic phenomenon that may impact large and small regions alike for long or short time periods and influence society in terms of industrial, agricultural, domestic and many more aspects. The characteristics of the droughts are commonly investigated using indices like Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI). On the other hand, these indices may not necessarily yield similar performance over different vegetation types. The aim is to analyze the sensitivity of drought indices (SPI, SPEI, PDSI) to vegetation types over different climatic regions in Turkey. Here the magnitude of the drought severity is measured using MODIS NDVI data, while the vegetation type (e.g., non-irrigated arable lands, vineyards, fruit trees and berry plantations, olive groves, pastures, land principally occupied by agriculture) information is obtained using CORINE land cover classification. This study has compared the drought characteristics and vegetation conditions on different land use types using remotely sensed datasets (e.g., CORINE land use data, MODIS NDVI), and commonly used drought indices between 2000 and 2016 using gauge based precipitation and temperature measurements.

Progress towards MODIS and VIIRS Cloud Fraction Data Record Continuity

NASA Astrophysics Data System (ADS)

Ackerman, S. A.; Frey, R.; Holz, R.; Platnick, S. E.; Heidinger, A. K.

2016-12-01

Satellite-derived clear-sky vs. cloudy-sky discrimination at the pixel scale is an important input parameter used in many real-time applications. Cloud fractions, resulting from integrating over time and space, are also critical to the study of recent decadal climate changes. The NASA NPOESS Preparatory Project (NPP) has funded a science team to develop and study the ability to make continuous climate records from MODIS (2000-2020) and VIIRS (2012-2030). The MODAWG project, led by Dr. Steve Platnick of NASA/GSFC, combines elements of the MODIS processing system and the NOAA Algorithm Working Group (AWG) to achieve this goal. This presentation will focus on the cloud masking aspects of MODAWG, derived primarily from the MODIS cloud mask (MOD35). Challenges to continuity of cloud detection due to differences in instrument characteristics will be discussed. Cloud mask results from use of the same (continuity) algorithm will be shown for both MODIS and VIIRS, including comparisons to collocated CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud data.

Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index

Treesearch

Taehee Hwang; Conghe Song; James Vose; Lawrence Band

2011-01-01

Forest canopy phenology is an important constraint on annual water and carbon budgets, and responds to regional interannual climate variation. In steep terrain, there are complex spatial variations in phenology due to topographic influences on microclimate, community composition, and available soil moisture. In this study, we investigate spatial patterns of phenology...

The influence of burn severity on post-fire vegetation recovery and albedo change during early succession in North American boreal forests

NASA Astrophysics Data System (ADS)

Jin, Y.; Randerson, J. T.; Goetz, S. J.; Beck, P. S.; Loranty, M. M.; Goulden, M.

2011-12-01

Severity of burning can influence multiple aspects of forest composition, carbon cycling, and climate forcing. We quantified how burn severity affected vegetation recovery and albedo change during early succession in Canadian boreal regions by combining satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Canadian Large Fire Data Base (LFDB). We used the difference Normalized Burn Ratio (dNBR) and changes in spring albedo derived from MODIS 500m albedo product as measures of burn severity. We found that the most severe burns had the greatest reduction in summer EVI in first year after fire, indicating greater loss of vegetation cover immediately following fire. By 5-7 years after fire, summer EVI for all severity classes had recovered to within 90-110% of pre-fire levels. Burn severity had a positive effect on the increase of post-fire spring albedo during the first 7 years after fire, and a shift from low to moderate or moderate to severe fires led to amplification of the post-fire albedo increase by approximately 30%. Fire-induced increases in both spring and summer albedo became progressively larger with stand age from years 1-7, with the trend in spring albedo likely driven by continued losses of needles and branches from trees killed by the fire (and concurrent losses of black carbon coatings on remaining debris), and the summer trend associated with increases in leaf area of short-stature herbs and shrubs. Our results suggest that increases in burn severity and carbon losses observed in some areas of boreal forests (e.g., Turetsky et al., 2011) may be at least partly offset by increases in negative forcing associated with changes in surface albedo.

Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data

NASA Astrophysics Data System (ADS)

Huang, Huabing; Liu, Caixia; Wang, Xiaoyi; Biging, Gregory S.; Chen, Yanlei; Yang, Jun; Gong, Peng

2017-07-01

Vegetation height is an important parameter for biomass assessment and vegetation classification. However, vegetation height data over large areas are difficult to obtain. The existing vegetation height data derived from the Ice, Cloud and land Elevation Satellite (ICESat) data only include laser footprints in relatively flat forest regions (<5°). Thus, a large portion of ICESat data over sloping areas has not been used. In this study, we used a new slope correction method to improve the accuracy of estimates of vegetation heights for regions where slopes fall between 5° and 15°. The new method enabled us to use more than 20% additional laser data compared with the existing vegetation height data which only uses ICESat data in relatively flat areas (slope < 5°) in China. With the vegetation height data extracted from ICESat footprints and ancillary data including Moderate Resolution Imaging Spectroradiometer (MODIS) derived data (canopy cover, reflectances and leaf area index), climate data, and topographic data, we developed a wall to wall vegetation height map of China using the Random Forest algorithm. We used the data from 416 field measurements to validate the new vegetation height product. The coefficient of determination (R2) and RMSE of the new vegetation height product were 0.89 and 4.73 m respectively. The accuracy of the product is significantly better than that of the two existing global forest height products produced by Lefsky (2010) and Simard et al. (2011), when compared with the data from 227 field measurements in our study area. The new vegetation height data demonstrated clear distinctions among forest, shrub and grassland, which is promising for improving the classification of vegetation and above-ground forest biomass assessment in China.

Assessing the Three-North Shelter Forest Program in China by a novel framework for characterizing vegetation changes

NASA Astrophysics Data System (ADS)

Qiu, Bingwen; Chen, Gong; Tang, Zhenghong; Lu, Difei; Wang, Zhuangzhuang; Chen, Chongchen

2017-11-01

The Three-North Shelter Forest Program (TNSFP) in China has been intensely invested for approximately 40 years. However, the efficacy of the TNSFP has been debatable due to the spatiotemporal complexity of vegetation changes. A novel framework was proposed for characterizing vegetation changes in the TNSFP region through Combining Trend and Temporal Similarity trajectory (COTTS). This framework could automatically and continuously address the fundamental questions on where, what, how and when vegetation changes have occurred. Vegetation trend was measured by a non-parametric method. The temporal similarity trajectory was tracked by the Jeffries-Matusita (JM) distance of the inter-annual vegetation indices temporal profiles and modeled using the logistic function. The COTTS approach was applied to examine the afforestation efforts of the TNSFP using 500 m 8-day composites MODIS datasets from 2001 to 2015. Accuracy assessment from the 1109 reference sites reveals that the COTTS is capable of automatically determining vegetation dynamic patterns, with an overall accuracy of 90.08% and a kappa coefficient of 0.8688. The efficacy of the TNSFP was evaluated through comprehensive considerations of vegetation, soil and wetness. Around 45.78% areas obtained increasing vegetation trend, 2.96% areas achieved bare soil decline and 4.50% areas exhibited increasing surface wetness. There were 4.49% areas under vegetation degradation & desertification. Spatiotemporal heterogeneity of efficacy of the TNSFP was revealed: great vegetation gain through the abrupt dynamic pattern in the semi-humid and humid regions, bare soil decline & potential efficacy in the semi-arid region and remarkable efficacy in functional region of Eastern Ordos.

Monitoring post-fire recovery of shrublands in Mediterranean-type ecosystems using MODIS and TM/ETM+ data

NASA Astrophysics Data System (ADS)

Hope, Allen; Albers, Noah; Bart, Ryan

2010-05-01

Wildland fires in Mediterranean-Type Ecosystems (MTEs) are episodic events that dramatically alter land-cover conditions. Monitoring post-fire vegetation recovery is important for land management applications such as the scheduling of prescribed burns, post-fire resource management and soil erosion control. Full recovery of MTE shrublands may take many years and have a prolonged effect on water, energy and carbon fluxes in these ecosystems. Comparative studies of fynbos ecosystems in the Cape Floristic Region of South Africa (Western Cape Region) and chaparral ecosystems of California have demonstrated that there is a considerable degree of convergence in some aspects of post-fire vegetation regeneration and marked differences in other aspects. Since these MTEs have contrasting rainfall and soil nutrient conditions, an obvious question arises as to the similarity or dissimilarity in remotely sensed post-fire recovery pathways of vegetation stands in these two regions and the extent to which fire severity and drought impact the rate of vegetation recovery. Post-fire recovery pathways of chaparral and fynbos vegetation stands were characterized using the normalized difference vegetation index (NDVI) based on TM/ETM+ and MODIS (250 m) data. Procedures based on stands of unburned vegetation (control) were implemented to normalize the NDVI for variations associated with inter-annual differences in rainfall. Only vegetation stands that had not burned for 20 years were examined in this study to eliminate potential effects of variable fire histories on the recovery pathways. Post-fire recovery patterns of vegetation in both regions and across different vegetation types were found to be very similar. Post-fire stand age was the primary control over vegetation recovery and the NDVI returned to pre-fire values within seven to 10 years of the fires. Droughts were shown to cause slight interruptions in recovery rates while fire severity had no discernable effect. Intra-stand variability in the NDVI (pixel-scale) also returned to pre-fire values within the same time frame but increased with water stress associated with droughts. While these studies indicated that the NDVI of fynbos and chaparral stands recovered to pre-fire values within 10 years, it is recognized that other ecosystem characteristics may take considerably longer to recover. Despite the larger pixel size, MODIS data were found to be more suitable for monitoring vegetation post-fire recovery than TM/ETM+ data, requiring considerably less pre-processing and providing substantially more information regarding phenological characteristics of recovery pathways. Future studies will include consideration of fire history in the post-fire recovery characteristics of vegetation in these two MTEs.

Consistency of vegetation index seasonality across the Amazon rainforest

NASA Astrophysics Data System (ADS)

Maeda, Eduardo Eiji; Moura, Yhasmin Mendes; Wagner, Fabien; Hilker, Thomas; Lyapustin, Alexei I.; Wang, Yujie; Chave, Jérôme; Mõttus, Matti; Aragão, Luiz E. O. C.; Shimabukuro, Yosio

2016-10-01

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

Consistency of Vegetation Index Seasonality Across the Amazon Rainforest

NASA Technical Reports Server (NTRS)

Maeda, Eduardo Eiji; Moura, Yhasmin Mendes; Wagner, Fabien; Hilker, Thomas; Lyapustin, Alexei I.; Wang, Yujie; Chave, Jerome; Mottus, Matti; Aragao, Luiz E.O.C.; Shimabukuro, Yosio

2016-01-01

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

Modelling evapotranspiration using the modified Penman-Monteith equation and MODIS data over the Albany Thicket in South Africa

NASA Astrophysics Data System (ADS)

Gwate, O.; Mantel, Sukhmani K.; Palmer, Anthony R.; Gibson, Lesley A.

2016-10-01

Evapotranspiration (ET) is one of the least understood components of the water cycle, particularly in data scarce areas. In a context of climate change, evaluating water vapour fluxes of a particular area is crucial to help understand dynamics in water balance. In data scarce areas, ET modelling becomes vital. The study modelled ET using the Penman-Monteith- Leuning (PML) equation forced by Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) and MODIS albedo with ancillary meteorological data from an automatic weather station. The study area is located on the Albany Thicket (AT) biome of South Africa and the dominant plant species is Portulacaria afra. The biggest challenge to the implementation of the PML is the parameterisation of surface and stomatal conductance. We tested the use of volumetric soil water content (fswc), precipitation and equilibrium evaporation ratio (fzhang) and soil drying after precipitation (f) approaches to account for the fraction (f) of evaporation from the soil. ET from the model was validated using an eddy covariance system (EC). Post processing of eddy covariance data was implement using EddyPro software. The fdrying method performed better with a root mean square observations standard deviation ratio (RSR) of 0.97. The results suggest that modelling ET over the AT vegetation is delicate owing to strong vegetation phenological control of the ET process. The convergent evolution of the vegetation has resulted in high plant available water than the model can detect. It is vital to quantify plant available water in order to improve ET modelling in thicket vegetation.

Water and vegetation indices by using MODIS products for eucalyptus, pasture, and natural ecosystems in the eastern São Paulo state, Southeast Brazil

NASA Astrophysics Data System (ADS)

de C. Teixeira, Antônio H.; Leivas, Janice F.; Ronquim, Carlos C.; Garçon, Edlene A. M.; Bayma-Silva, Gustavo

2017-10-01

Eucalyptus (Ec) and pasture (Pt) are expanding while natural vegetation (Nv) are losing space in the Paraíba Valley, eastern side of the São Paulo state, Southeast Brazil. For quantification of water and vegetation conditions, the MODIS product MOD13Q1 was used together with a net of weather stations and vegetation land masks during the year 2015. The SAFER algorithm was applied to retrieve the actual evapotranspiration (ET), which was combined with the Monteith's radiation use efficiency (RUE) model to estimate the biomass production (BIO). Three moisture indices were applied, the climatic water balance ratio (WBr), the ratio of precipitation (P) to ET, the water balance deficit (WBd), the difference between P and ET, and the evapotranspiration ratio (ETr), the ratio of ET to the reference evapotranspiration (ET0). On the one hand, the highest ET rates for the Ec ecosystem should be a negative aspect under water scarcity conditions; however, it presented the best water productivity. Although the Ec ecosystem presenting the lowest WBr and WBd values, it had the highest ETr, averaging 0.92, when comparing to those for Nv (0.88) and Pt (0.79). These results indicated that eucalyptus plants have greater ability of conserving soil moisture in their root zones, increasing WP, when comparing with Pt and Nv ecosystems. These water relationships are relevant issues under the land-use change conditions in the Paraiba Valley, confirming the suitability of using the MODIS products together with weather stations to study the ecosystem dynamics.

Cumulative drought and land-use impacts on perennial vegetation across a North American dryland region

USGS Publications Warehouse

Munson, Seth M.; Long, A. Lexine; Wallace, Cynthia; Webb, Robert H.

2016-01-01

Question The decline and loss of perennial vegetation in dryland ecosystems due to global change pressures can alter ecosystem properties and initiate land degradation processes. We tracked changes of perennial vegetation using remote sensing to address the question of how prolonged drought and land-use intensification have affected perennial vegetation cover across a desert region in the early 21st century? Location Mojave Desert, southeastern California, southern Nevada, southwestern Utah and northwestern Arizona, USA. Methods We coupled the Moderate-Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) with ground-based measurements of perennial vegetation cover taken in about 2000 and about 2010. Using the difference between these years, we determined perennial vegetation changes in the early 21st century and related these shifts to climate, soil and landscape properties, and patterns of land use. Results We found a good fit between MODIS-EVI and perennial vegetation cover (2000: R2 = 0.83 and 2010: R2 = 0.74). The southwestern, far southeastern and central Mojave Desert had large declines in perennial vegetation cover in the early 21st century, while the northeastern and southeastern portions of the desert had increases. These changes were explained by 10-yr precipitation anomalies, particularly in the cool season and during extreme dry or wet years. Areas heavily impacted by visitor use or wildfire lost perennial vegetation cover, and vegetation in protected areas increased to a greater degree than in unprotected areas. Conclusions We find that we can extrapolate previously documented declines of perennial plant cover to an entire desert, and demonstrate that prolonged water shortages coupled with land-use intensification create identifiable patterns of vegetation change in dryland regions.

Recent Weather Extremes and Impacts on Agricultural Production and Vector-Borne Disease Outbreak Patterns

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Small, Jennifer L.; Britch, Seth C.; Tucker, Compton J.; Pak, Edwin W.; Reynolds, Curt A.; Crutchfield, James; Linthicum, Kenneth J.

2014-01-01

We document significant worldwide weather anomalies that affected agriculture and vector-borne disease outbreaks during the 2010-2012 period. We utilized 2000-2012 vegetation index and land surface temperature data from NASA's satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to map the magnitude and extent of these anomalies for diverse regions including the continental United States, Russia, East Africa, Southern Africa, and Australia. We demonstrate that shifts in temperature and/or precipitation have significant impacts on vegetation patterns with attendant consequences for agriculture and public health. Weather extremes resulted in excessive rainfall and flooding as well as severe drought, which caused,10 to 80% variation in major agricultural commodity production (including wheat, corn, cotton, sorghum) and created exceptional conditions for extensive mosquito-borne disease outbreaks of dengue, Rift Valley fever, Murray Valley encephalitis, and West Nile virus disease. Analysis of MODIS data provided a standardized method for quantifying the extreme weather anomalies observed during this period. Assessments of land surface conditions from satellite-based systems such as MODIS can be a valuable tool in national, regional, and global weather impact determinations.

Retrieval of aerosol optical depth over bare soil surfaces using time series of MODIS imagery

NASA Astrophysics Data System (ADS)

Yuan, Zhengwu; Yuan, Ranyin; Zhong, Bo

2014-11-01

Aerosol Optical Depth (AOD) is one of the key parameters which can not only reflect the characterization of atmospheric turbidity, but also identify the climate effects of aerosol. The current MODIS aerosol estimation algorithm over land is based on the "dark-target" approach which works only over densely vegetated surfaces. For non-densely vegetated surfaces (such as snow/ice, desert, and bare soil surfaces), this method will be failed. In this study, we develop an algorithm to derive AOD over the bare soil surfaces. Firstly, this method uses the time series of MODIS imagery to detect the " clearest" observations during the non-growing season in multiple years for each pixel. Secondly, the "clearest" observations after suitable atmospheric correction are used to fit the bare soil's bidirectional reflectance distribution function (BRDF) using Kernel model. As long as the bare soil's BRDF is established, the surface reflectance of "hazy" observations can be simulated. Eventually, the AOD over the bare soil surfaces are derived. Preliminary validation results by comparing with the ground measurements from AERONET at Xianghe sites show a good agreement.

Recent weather extremes and impacts on agricultural production and vector-borne disease outbreak patterns.

PubMed

Anyamba, Assaf; Small, Jennifer L; Britch, Seth C; Tucker, Compton J; Pak, Edwin W; Reynolds, Curt A; Crutchfield, James; Linthicum, Kenneth J

2014-01-01

We document significant worldwide weather anomalies that affected agriculture and vector-borne disease outbreaks during the 2010-2012 period. We utilized 2000-2012 vegetation index and land surface temperature data from NASA's satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to map the magnitude and extent of these anomalies for diverse regions including the continental United States, Russia, East Africa, Southern Africa, and Australia. We demonstrate that shifts in temperature and/or precipitation have significant impacts on vegetation patterns with attendant consequences for agriculture and public health. Weather extremes resulted in excessive rainfall and flooding as well as severe drought, which caused ∼10 to 80% variation in major agricultural commodity production (including wheat, corn, cotton, sorghum) and created exceptional conditions for extensive mosquito-borne disease outbreaks of dengue, Rift Valley fever, Murray Valley encephalitis, and West Nile virus disease. Analysis of MODIS data provided a standardized method for quantifying the extreme weather anomalies observed during this period. Assessments of land surface conditions from satellite-based systems such as MODIS can be a valuable tool in national, regional, and global weather impact determinations.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors.

PubMed

Lange, Maximilian; Dechant, Benjamin; Rebmann, Corinna; Vohland, Michael; Cuntz, Matthias; Doktor, Daniel

2017-08-11

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors

PubMed Central

Lange, Maximilian; Rebmann, Corinna; Cuntz, Matthias; Doktor, Daniel

2017-01-01

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure. PMID:28800065

An evaluation of MODIS 250-m data for green LAI estimation in crops

NASA Astrophysics Data System (ADS)

Gitelson, Anatoly A.; Wardlow, Brian D.; Keydan, Galina P.; Leavitt, Bryan

2007-10-01

Green leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. Remotely sensed data provide considerable potential for estimating LAI at local, regional, and global scales. The goal of this study was to retrieve green LAI from MODIS 250-m vegetation index (VI) data for irrigated and rainfed maize and soybeans. The performance of both MODIS-derived NDVI and Wide Dynamic Range Vegetation Index (WDRVI) were evaluated across three growing seasons (2002 through 2004) over a wide range of LAI and also compared to the performance of NDVI and WDRVI derived from reflectance data collected at close-range across the same field locations. The NDVI vs. LAI relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the NDVI to LAI exceeding 2 m2/m2 for both crops. WDRVI vs. LAI relation was linear across the entire range of LAI variation with determination coefficients above 0.93. Importantly, the coefficients of the close-range WDRVI vs. LAI equation and the MODIS-retrieved WDRVI vs. LAI equation were very close. The WDRVI was found to be capable of accurately estimating LAI across a much greater LAI range than the NDVI and can be used for assessing even slight variations in LAI, which are indicative of the early stages of plant stress. These results demonstrate the new possibilities for analyzing the spatio-temporal variation of the LAI of crops using multi-temporal MODIS 250-m imagery.

Estimation of Canopy Clumping Index From MISR and MODIS Sensors Using the Normalized Difference Hotspot and Darkspot (NDHD) Method: The Influence of BRDF Models and Solar Zenith Angle

NASA Astrophysics Data System (ADS)

Wei, S.; Fang, H.

2016-12-01

The Clumping index (CI) describes the spatial distribution pattern of foliage, and is a critical parameter used to characterize the terrestrial ecosystem and model land-surface processes. Global and regional scale CI maps have been generated from POLDER, MODIS, and MISR sensors based on an empirical relationship with the normalized difference between hotspot and darkspot (NDHD) index by previous studies. However, the hotspot and darkspot values and CI values can be considerably different from different bidirectional reflectance distribution function (BRDF) models and solar zenith angles (SZA). In this study, we evaluated the effects of different configurations of BRDF models and SZA values on CI estimation using the NDHD method. CI maps estimated from MISR and MODIS were compared with reference data at the VALERI sites. Results show that for moderate to least clumped vegetation (CI > 0.5), CIs retrieved with the observational SZA agree well with field values, while SZA =0° results in underestimates, and SZA = 60° results in overestimates. For highly clumped (CI < 0.5) and sparsely vegetated areas (FCOVER<25%), the Ross-Li model with 60° SZA is recommended for CI estimation. The suitable NDHD configuration was further used to estimate a 15-year time series CI from MODIS BRDF data. The time series CI shows a reasonable seasonal trajectory, and varies consistently with the MODIS leaf area index (LAI). This study enables better usage of the NDHD method for CI estimation, and can be a useful reference for research on CI validation.

Assessing the Success of Postfire Reseeding in Semiarid Rangelands Using Terra MODIS

NASA Technical Reports Server (NTRS)

Chen, Fang; Weber, Keith T.; Scbnase, John L.

2012-01-01

Successful postfire reseeding efforts can aid rangeland ecosystem recovery by rapidly establishing a desired plant community and thereby reducing the likelihood of infestation by invasive plants. Although the success of postfire remediation is critical, few efforts have been made to leverage existing geospatial technologies to develop methodologies to assess reseeding success following a fire. In this study, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were used to improve the capacity to assess postfire reseeding rehabilitation efforts, with particular emphasis on the semiarid rangelands of Idaho. Analysis of MODIS data demonstrated a positive effect of reseeding on rangeland ecosystem recovery, as well as differences in vegetation between reseeded areas and burned areas where no reseeding had occurred (P,0.05). We conclude that MODIS provides useful data to assess the success of postfire reseeding.

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

USGS Publications Warehouse

Running, S. W.; Justice, C.O.; Salomonson, V.V.; Hall, D.; Barker, J.; Kaufmann, Y. J.; Strahler, Alan H.; Huete, A.R.; Muller, Jan-Peter; Vanderbilt, V.; Wan, Z.; Teillet, P.; Carneggie, David M. Geological Survey (U.S.) Ohlen

1994-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.

Amazon Forests Response to Droughts: A Perspective from the MAIAC Product

NASA Technical Reports Server (NTRS)

Bi, Jian; Myneni, Ranga; Lyapustin, Alexei; Wang, Yujie; Park, Taejin; Chi, Chen; Yan, Kai; Knyazikhin, Yuri

2016-01-01

Amazon forests experienced two severe droughts at the beginning of the 21st century: one in 2005 and the other in 2010. How Amazon forests responded to these droughts is critical for the future of the Earth's climate system. It is only possible to assess Amazon forests' response to the droughts in large areal extent through satellite remote sensing. Here, we used the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) data to assess Amazon forests' response to droughts, and compared the results with those from the standard (Collection 5 and Collection 6) MODIS VI data. Overall, the MAIAC data reveal more realistic Amazon forests inter-annual greenness dynamics than the standard MODIS data. Our results from the MAIAC data suggest that: (1) the droughts decreased the greenness (i.e., photosynthetic activity) of Amazon forests; (2) the Amazon wet season precipitation reduction induced by El Niño events could also lead to reduced photosynthetic activity of Amazon forests; and (3) in the subsequent year after the water stresses, the greenness of Amazon forests recovered from the preceding decreases. However, as previous research shows droughts cause Amazon forests to reduce investment in tissue maintenance and defense, it is not clear whether the photosynthesis of Amazon forests will continue to recover after future water stresses, because of the accumulated damages caused by the droughts.

Use of MODIS Data in Dynamic SPARROW Analysis of Watershed Loading Reductions

NASA Astrophysics Data System (ADS)

Smith, R. A.; Schwarz, G. E.; Brakebill, J. W.; Hoos, A.; Moore, R. B.; Nolin, A. W.; Shih, J. S.; Journey, C. A.; Macauley, M.

2014-12-01

Predicting the temporal response of stream water quality to a proposed reduction in contaminant loading is a major watershed management problem due to temporary storage of contaminants in groundwater, vegetation, snowpack, etc. We describe the response of dynamically calibrated SPARROW models of total nitrogen (TN) flux to hypothetical reductions in reactive nitrogen inputs in three sub-regional watersheds: Potomac River Basin (Chesapeake Bay drainage), Long Island Sound drainage, and South Carolina coastal drainage. The models are based on seasonal water quality and watershed input data from 170 monitoring stations for the period 2002 to 2008.The spatial reference frames of the three models are stream networks containing an average 38,000 catchments and the time step is seasonal. We use MODIS Enhanced Vegetation Index (EVI) and snow/ice cover data to parameterize seasonal uptake and release of nitrogen from vegetation and snowpack. The model accounts for storage of total nitrogen inputs from fertilized cropland, pasture, urban land, and atmospheric deposition. Model calibration is by non-linear regression. Model source terms based on previous season export allow for recursive simulation of stream flux and can be used to estimate the approximate residence times of TN in the watersheds. Catchment residence times in the Long Island Sound Basin are shorter (typically < 1 year) than in the Potomac or South Carolina Basins (typically > 1 year), in part, because a significant fraction of nitrogen flux derives from snowmelt and occurs within one season of snowfall. We use the calibrated models to examine the response of TN flux to hypothetical step reductions in source inputs at the beginning of the 2002-2008 period and the influence of observed fluctuations in precipitation, temperature, vegetation growth and snow melt over the period. Following non-point source reductions of up to 100%, stream flux was found to continue to vary greatly for several years as a function of seasonal conditions, with high values in both winter (January, February, March) and spring due to high precipitation and snow melt, but much lower summer yields due to low precipitation and nitrogen retention in growing vegetation (EVI). Temporal variations in stream flux are large enough to potentially mask water quality improvements for several years.

Monitoring Functional Traits of Alpine Vegetation using Remote Sensing

NASA Astrophysics Data System (ADS)

Li, C.; Wulf, H.; Schaepman, M. E.; Schmid, B.

2016-12-01

Plant functional traits can be used to study the interactions between plants and ecosystem functioning as well as the response of plants to various environmental pressures. Continuous monitoring of plant functional traits dynamics on a large spatial scale is important to understand the mechanisms of ecosystem function degradation, especially on the Qinghai-Tibet Plateau. In this study, we investigated spatiotemporal trends of functional traits (i.e., chlorophyll content, phenology, leaf area index proxy of leaf size and above ground biomass proxy of leaf mass) in the eastern part of the Qinghai-Tibet Plateau based on the combined analysis of multi-sensor satellite data and field observations at three spatial scales (ground-truth data at 1 m, Landsat at 30 m, MODIS at 500 m), and analyzed potential factors contribute to their spatiotemporal trends. Chlorophyll content (Chl) and biomass was retrieved based on 94 field plots measurements. LAI was analyzed using MCD15A3H product and estimated values using digital hemispherical photographs in the field. Plant phenology will be processed based on MODIS NDVI time series and hourly Phenocam observations. The preliminary results show that (1) Chl, LAI and biomass show high spatial heterogeneity trends and increase in 2001 - 2015. (2) Elevation played an important role in the spatial pattern of LAI and Chl variation in 15 years. A dividing line of approximately 3800 m exists and shows that below this line, LAI and Chl changes more complicated, showing significantly positive and negative linear trend. While above this altitude, the change rate of two variables keeps relatively stable. Vegetation in low elevation is exposed to high habitat diversity by showing high Chl, LAI and biomass spatial heterogeneity. The vegetation in high habitat diversity may be more sensitive to climatic variables and human activities than higher elevation since warming contribute to the positive trend of traits while human factors like urbanization might be explain negative trend in relative low altitude (below 3800 m). (3) Temperature contribute to the above functional traits variation than precipitation, especially temperature is more correlated to the functional traits of widely distributed vegetation type than narrow-ranging vegetation type.

An evaluation of time-series MODIS 250-meter vegetation index data for crop mapping in the United States Central Great Plains

NASA Astrophysics Data System (ADS)

Wardlow, Brian Douglas

The objectives of this research were to: (1) investigate time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-meter EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) data for regional-scale crop-related land use/land cover characterization in the U.S. Central Great Plains and (2) develop and test a MODIS-based crop mapping protocol. A pixel-level analysis of the time-series MODIS 250-m VIs for 2,000+ field sites across Kansas found that unique spectral-temporal signatures were detected for the region's major crop types, consistent with the crops' phenology. Intra-class variations were detected in VI data associated with different land use practices, climatic conditions, and planting dates for the crops. The VIs depicted similar seasonal variations and were highly correlated. A pilot study in southwest Kansas found that accurate and detailed cropping patterns could be mapped using the MODIS 250-m VI data. Overall and class-specific accuracies were generally greater than 90% for mapping crop/non-crop, general crops (alfalfa, summer crops, winter wheat, and fallow), summer crops (corn, sorghum, and soybeans), and irrigated/non-irrigated crops using either VI dataset. The classified crop areas also had a high level of agreement (<5% difference) with the USDA reported crop areas. Both VIs produced comparable crop maps with only a 1-2% difference between their classification accuracies and a high level of pixel-level agreement (>90%) between their classified crop patterns. This hierarchical crop mapping protocol was tested for Kansas and produced similar classification results over a larger and more diverse area. Overall and class-specific accuracies were typically between 85% and 95% for the crop maps. At the state level, the maps had a high level of areal agreement (<5% difference) with the USDA crop area figures and their classified patterns were consistent with the state's cropping practices. In general, the protocol's performance was relatively consistent across the state's range of environmental conditions, landscape patterns, and cropping practices. The largest areal differences occurred in eastern Kansas due to the omission of many small cropland areas that were not resolvable at MODIS' 250-m resolution. Notable regional deviations in classified areas also occurred for selected classes due to localized precipitation patterns and specific cropping practices.

MODIS EVI-based net primary production in the Sahel 2000-2014

NASA Astrophysics Data System (ADS)

Ardö, Jonas; Tagesson, Torbern; Jamali, Sadegh; Khatir, Abdelrahman

2018-03-01

Africa is facing resource problems due to increasing demand combined with potential climate-induced changes in supply. Here we aim to quantify resources in terms of net primary production (NPP [g C m-2 yr-1]) of vegetation in the Sahel region for 2000-2014. Using time series of the enhanced vegetation index (EVI) from MODIS, NPP was estimated for the Sahel region with a 500 × 500 m spatial resolution and 8-day temporal resolution. The estimates were based on local eddy covariance flux measurements from six sites in the Sahel region and the carbon use efficiency originating from a dynamic vegetation model. No significant NPP change was found for the Sahel as a region but, for sub-regions, significant changes, both increasing and decreasing, were observed. Substantial uncertainties related to NPP estimates and the small availability of evaluation data makes verification difficult. The simplicity of the methodology used, dependent on earth observation only, is considered an advantage.

Continuity of MODIS and VIIRS Snow-Cover Maps during Snowmelt in the Catskill Mountains in New York

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; Roman, M. O.; DiGirolamo, N. E.

2015-12-01

We investigate the local and regional differences and possible biases between the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible-Infrared Imager Radiometer Suite (VIIRS) snow-cover maps in the winter of 2012 during snowmelt conditions in the Catskill Mountains in New York using a time series of cloud-gap filled daily snow-cover maps. The MODIS Terra instrument has been providing daily global snow-cover maps since February 2000 (Riggs and Hall, 2015). Using the VIIRS instrument, launched in 2011, NASA snow products are being developed based on the heritage MODIS snow-mapping algorithms, and will soon be available to the science community. Continuity of the standard NASA MODIS and VIIRS snow-cover maps is essential to enable environmental-data records (EDR) to be developed for analysis of snow-cover trends using a consistent data record. For this work, we compare daily MODIS and VIIRS snow-cover maps of the Catskill Mountains from 29 February through 14 March 2012. The entire region was snow covered on 29 February and by 14 March the snow had melted; we therefore have a daily time series available to compare normalized difference snow index (NDSI), as an indicator of snow-cover fraction. The MODIS and VIIRS snow-cover maps have different spatial resolutions (500 m for MODIS and 375 m for VIIRS) and different nominal overpass times (10:30 AM for MODIS Terra and 2:30 PM for VIIRS) as well as different cloud masks. The results of this work will provide a quantitative assessment of the continuity of the snow-cover data records for use in development of an EDR of snow cover.http://modis-snow-ice.gsfc.nasa.gov/Riggs, G.A. and D.K. Hall, 2015: MODIS Snow Products User Guide to Collection 6, http://modis-snow-ice.gsfc.nasa.gov/?c=userguides

Mapping national scale land cover disturbance for the continental United States, 2006 to 2010

NASA Astrophysics Data System (ADS)

Hansen, M. C.; Potapov, P. V.; Egorov, A.; Roy, D. P.; Loveland, T. R.

2011-12-01

Data from the Web-Enabled Landsat Data (WELD) project were used to quantify forest cover loss and bare ground gain dynamics for the continental United States at a 30 meter spatial resolution from 2006 to 2010. Results illustrate the land cover dynamics associated with forestry, urbanization and other medium to long-term cover conversion processes. Ephemeral changes, such as crop rotations and fallows or inundation, were not quantified. Forest disturbance is pervasive at the national-scale, while increasing bare ground is found in growing urban areas as well as in mining regions. The methods applied are an outgrowth of the Vegetation Continuous Field (VCF) method, initially employed with MODIS data and then WELD data to map percent cover variables. As in our previous work with MODIS in mapping forest change, we applied the VCF method to characterize forest cover loss and bare ground gain probability per pixel. Additional themes will be added to provide a more comprehensive picture of national-scale land dynamics based on these initial results using WELD.

Eruption of Eyjafjallajökull Volcano, Iceland

NASA Image and Video Library

2017-12-08

March 31, 2010..The volcanic eruption near Eyjafjallajökull persists into its second week, with continued lava fountaining and lava flows spilling into nearby canyons. The eruption is located at the Fimmvörduháls Pass between the Eyjafjallajökull ice field to the west (left) and the Mýrdalsjökull ice field to the east (right). This natural-color satellite image was acquired on March 26, 2010, by the MODIS aboard NASA’s Terra satellite. Dark ash and scoria cover the northern half of the Fimmvörduháls Pass. White snow covers the rest of the pass, sandwiched between white glaciers. Snow-free land is tan, brown, or dark gray, devoid of vegetation in early spring. To download a high res version of this image go to: modis.gsfc.nasa.gov/gallery/individual.php?db_date=2010-0... NASA Goddard Space Flight Center is home to the nation's largest organization of combined scientists, engineers and technologists that build spacecraft, instruments and new technology to study the Earth, the sun, our solar system, and the universe.

Examining the Influence of Teleconnection Patterns on CO2 Fluxes at an Old-Growth Forest Scaling from Stand to Region Using MODIS

NASA Astrophysics Data System (ADS)

Wharton, S.; Chasmer, L.; Falk, M.; Paw U, K.

2007-12-01

In this study, year-to-year variability in three of the major Pacific teleconnection patterns were examined to determine if CO2 and H2O fluxes at an old-growth forest in the Pacific Northwest were affected by climatic changes associated with these patterns. The three cycles examined are the Pacific Decadal Oscillation, Pacific/North American Oscillation and El Niño-Southern Oscillation. We centered our study on the Wind River Canopy Crane, an AmeriFlux tower located in a 500 year old conifer forest in southern Washington State. CO2 and H2O fluxes have been measured continuously for six years using the eddy covariance method. The objectives of this study are to: 1. determine to what extent teleconnection patterns influence measured CO2 and H2O fluxes through mechanistic anomalies; 2. ascertain if climatic shifts affect annual vegetation canopy characteristics; and 3. make comparisons at the local and regional scales using MODIS. The ecosystem was a significant sink of carbon (-207 gC m-2 year-1) in 1999 but turned into a large carbon source (+ 100 gC m-2 year-1) in 2003. NEE significantly (above the 95th CI) lags the PNA, ENSO and PDO indicating that these patterns affect the forest carbon budget across overlapping time scales. To ascertain the influence of atmospheric patterns on fluxes, we categorized the flux measurement years based on in-phase climate events (1999 = La Niña/cool PDO, 2003 = El Niño/warm PDO, 2000-2002, 2004 = neutral ENSO years). The results of this analysis indicate that the Pacific Ocean/atmospheric oscillation anomalies explain much of variance in annual NEE (R2 = 0.78 between NEE and the PDO, R2 = 0.87 for the PNA, and R2 = 0.56 for ENSO). Teleconnection patterns are found to be associated mostly with air temperature, precipitation, and incoming light radiation (cloudy vs. sunny conditions). Important meteorological driving mechanisms of fluxes include: water- use efficiency (WUE), light-use efficiency (LUE) and canopy structure parameters (e.g., fPAR). Tower-based fPAR was strongly related to NEE (R2 = 0.78) and climatic patterns (R2 = 0.84 with ENSO and R2 = 0.76 with PDO). Variability in fluxes may be a result of changes in the canopy structural characteristics; for example higher, fPAR (e.g., 2003) correlated well with increased respiration fluxes. MODIS data (200 km X 200 km area) were obtained to determine if anomalies in vegetation indices and canopy structure could be linked to teleconnection patterns at the site level and across the region. The MODIS-derived Enhanced Vegetation Index (EVI) correlated well with yearly cumulative NEE at the tower and regional EVI anomalies were strongly negatively correlated with the annual PDO index (R2 = 0.9). MODIS-derived fPAR product correlated with yearly variability in the PDO (R2 = 0.34) at the site level. Therefore, there is reasonable expectation that structural changes, as a result of climate variability during strongly positive or negative teleconnection patterns, will be observed in other parts of the Pacific Northwest. MODIS data is useful for identifying the effects of teleconnections across a regional scale.

Validation of MODIS aerosol optical depth product over China using CARSNET measurements

NASA Astrophysics Data System (ADS)

Xie, Yong; Zhang, Yan; Xiong, Xiaoxiong; Qu, John J.; Che, Huizheng

2011-10-01

This study evaluates Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) retrievals with ground measurements collected by the China Aerosol Remote Sensing NETwork (CARSNET). In current stage, the MODIS Collection 5 (C5) AODs are retrieved by two distinct algorithms: the Dark Target (DT) and the Deep Blue (DB). The CARSNET AODs are derived with measurements of Cimel Electronique CE-318, the same instrument deployed by the AEROsol Robotic Network (AEROENT). The collocation is performed by matching each MODIS AOD pixel (10 × 10 km 2) to CARSNET AOD mean within 7.5 min of satellite overpass. Four-year comparisons (2005-2008) of MODIS/CARSNET at ten sites show the performance of MODIS AOD retrieval is highly dependent on the underlying land surface. The MODIS DT AODs are on average lower than the CARSNET AODs by 6-30% over forest and grassland areas, but can be higher by up to 54% over urban area and 95% over desert-like area. More than 50% of the MODIS DT AODs fall within the expected error envelope over forest and grassland areas. The MODIS DT tends to overestimate for small AOD at urban area. At high vegetated area it underestimates for small AOD and overestimates for large AOD. Generally, its quality reduces with the decreasing AOD value. The MODIS DB is capable of retrieving AOD over desert but with a significant underestimation at CARSNET sites. The best retrieval of the MODIS DB is over grassland area with about 70% retrievals within the expected error. The uncertainties of MODIS AOD retrieval from spatial-temporal collocation and instrument calibration are discussed briefly.

A Model-based Approach to Scaling GPP and NPP in Support of MODIS Land Product Validation

NASA Astrophysics Data System (ADS)

Turner, D. P.; Cohen, W. B.; Gower, S. T.; Ritts, W. D.

2003-12-01

Global products from the Earth-orbiting MODIS sensor include land cover, leaf area index (LAI), FPAR, 8-day gross primary production (GPP), and annual net primary production (NPP) at the 1 km spatial resolution. The BigFoot Project was designed specifically to validate MODIS land products, and has initiated ground measurements at 9 sites representing a wide array of vegetation types. An ecosystem process model (Biome-BGC) is used to generate estimates of GPP and NPP for each 5 km x 5 km BigFoot site. Model inputs include land cover and LAI (from Landsat ETM+), daily meteorological data (from a centrally located eddy covariance flux tower), and soil characteristics. Model derived outputs are validated against field-measured NPP and flux tower-derived GPP. The resulting GPP and NPP estimates are then aggregated to the 1 km resolution for direct spatial comparison with corresponding MODIS products. At the high latitude sites (tundra and boreal forest), the MODIS GPP phenology closely tracks the BigFoot GPP, but there is a high bias in the MODIS GPP. In the temperate zone sites, problems with the timing and magnitude of the MODIS FPAR introduce differences in MODIS GPP compared to the validation data at some sites. However, the MODIS LAI/FPAR data are currently being reprocessed (=Collection 4) and new comparisons will be made for 2002. The BigFoot scaling approach permits precise overlap in spatial and temporal resolution between the MODIS products and BigFoot products, and thus permits the evaluation of specific components of the MODIS NPP algorithm. These components include meteorological inputs from the NASA Data Assimilation Office, LAI and FPAR from other MODIS algorithms, and biome-specific parameters for base respiration rate and light use efficiency.

Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Berndt, Emily B.; Srikishen, Jayanthi; Zavodsky, Bradley T.

2014-01-01

The NASA SPoRT Center is working to incorporate Suomi-NPP products into its research and transition activities to improve regional numerical weather prediction (NWP). Specifically, SPoRT seeks to utilize two data products from NOAA/NESDIS: (1) daily global VIIRS green vegetation fraction (GVF), and (2) NOAA Unique CrIS and ATMS Processing System (NUCAPS) temperature and moisture retrieved profiles. The goal of (1) is to improve the representation of vegetation in the Noah land surface model (LSM) over existing climatological GVF datasets in order to improve the land-atmosphere energy exchanges in NWP models and produce better temperature, moisture, and precipitation forecasts. The goal of (2) is to assimilate NUCAPS retrieved profiles into the Gridpoint Statistical Interpolation (GSI) data assimilation system to assess the impact on a summer pre-frontal convection case. Most regional NWP applications make use of a monthly GVF climatology for use in the Noah LSM within the Weather Research and Forecasting (WRF) model. The GVF partitions incoming energy into direct surface heating/evaporation over bare soil versus evapotranspiration processes over vegetated surfaces. Misrepresentations of the fractional coverage of vegetation during anomalous weather/climate regimes (e.g., early/late bloom or freeze; drought) can lead to poor NWP model results when land-atmosphere feedback is important. SPoRT has been producing a daily MODIS GVF product based on the University of Wisconsin Direct Broadcast swaths of Normalized Difference Vegetation Index (NDVI). While positive impacts have been demonstrated in the WRF model for some cases, the reflectances composing these NDVI do not correct for atmospheric aerosols nor satellite view angle, resulting in temporal noisiness at certain locations (especially heavy vegetation). The method behind the NESDIS VIIRS GVF is expected to alleviate the issues seen in the MODIS GVF real-time product, thereby offering a higher-quality dataset for modeling applications. SPoRT is evaluating the VIIRS GVF data against the MODIS real-time and climatology GVF in both WRF and the NASA Land Information System. SPoRT has a history of assimilating hyperspectral infrared retrieved profiles

Evaluation of eco-physiological indicators in Northeast Asia dryland regions based on MODIS products and ecological models

NASA Astrophysics Data System (ADS)

Kang, W.

2017-12-01

Ecosystem carbon-energy-water circles have significant effect on function and structure and vice verse. Based on these circles mechanism, some eco-physiological indicators, like Transpiration (T), gross primary productivity (GPP), light use efficiency (LUE) and water use efficiency (WUE), are commonly applied to assess terrestrial ecosystem function and structure dynamics. The ecosystem weakened function and simple structure in Northeast dryland regions resulted from land degradation or desertification, which could be demonstrated by above-mentioned indicators. In this study, based on MODIS atmosphere (MYD07, MYD04, MYD06 data) and land products (MYD13A2 NDVI, MYD11A1 LST, MYD15A2 LAI and land cover data), we first retrieved transpiration and LUE via Penman-Monteith Model and modified Vegetation Photosynthesis Model (VPM), respectively; and then evaluated dynamics of these eco-physiological indicators (Tair, VPD, T, LUE, GPP and WUE) and some hotspots were found for next land degradation assessment. The results showed: (1) LUE and WUE are lower in barren or sparsely vegetated area and grasslands than in forest and croplands. (2) Whereas, all indicators presented higher variability in grassland area, particularly in east Mongolia. (3) GPP and transpiration have larger variability than other indicators due to fraction of absorbed Photosynthetically active radiation (FPAR). These eco-physiological indicators are expected to continue to change under future climate change and to help to assess land degradation from ecosystem energy-water-carbon perspectives.

Fires in Kamchatka Peninsula, Russia

NASA Technical Reports Server (NTRS)

2002-01-01

Numerous thermal anomalies were detected on the Kamchatka Peninsula in northeastern Russia in late June and early July by the Moderate Resolution Imaging Spectroradiometer (MODIS). Some of the anomalies (red dots) were fires, but at least one was the result of ongoing volcanic activity at one of the Peninsula's numerous active volcanoes. The erupting volcano, called Sheveluch, can be seen most clearly in the image from July 8, 2002. It is located in the upper right quadrant of the image, and appears as a grayish circular patch amid the surrounding green vegetation. In its center is a red dot indicating that MODIS detected a thermal signature coming from the restless volcano. Credit: Jacques Descloitres, MODIS Land Rapid Response Team, NASA/GSFC

Assessment of Burned Area and Atmospheric Gases from Multi- temporal MODIS Images (2000- 2017) in Nainital District, Uttarakhand

NASA Astrophysics Data System (ADS)

Aggarwal, R.; K V, S. B.; Dhakate, P. M.

2017-12-01

Recent times have observed a significant rate of deforestation and forest degradation. One of the major causes of forest degradation is forest fires. Forest fires though have shaped the current forest ecosystem but also have continued to degrade the system by causing loss of flora and fauna. In addition to that, forest fire leads to emission of carbon and other trace gases which contributes to global warming. The hill states in India, particularly Uttarakhand witnesses annual forest fires; which are primarily anthropogenic caused, occurring from March to June. Nainital one of the thirteen districts in Uttarakhand, has been selected as the study site. The region has diverse endemic species of vegetation, ranging from Alpine in North to moist deciduous in South. The increasing forest fire incidents in the region and limited studies on the subject, calls for landscape assessment of the complex Human Environment System (HES). It is in this context, that a greater need for monitoring forest fire incidents has been felt. Remote Sensing and GIS which are robust tool, provides continuous information of an area at various spatial and temporal resolutions. The goal of this study is to map burned area, burned severity and estimate atmospheric gas emissions in forested areas of Nainital by utilizing cloud free MODIS images from 2000- 2017. Multiple spectral indices were generated from pre and post burn dataset of MODIS to conclude the most sensitive band combination. Inter- comparison of results obtained from different spectral indices and the global MODIS MCD45A1 was carried out using linear regression analysis. Additionally, burned area estimation from satellite was compared to figures reported by forest department. There were considerable differences amongst the two which could be primarily due to differences in spatial resolution, and timings of forest fire occurrence and image acquisition. Further, estimation of various atmospheric gases was carried out based on the IPCC guidelines. Such an analysis is critically important for designing of relevant forest fire mitigation strategies. The study signifies that long term MODIS data and the rationing method is an effective technique to map and monitor the burned area, burned severity and atmospheric gas emission in the forested regions of Himalayan.

Global Distributions of Mineral Dust Properties from SeaWiFS and MODIS: From Sources to Sinks

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Bettenhausen, C.; Sayer, A.

2011-01-01

The impact of natural and anthropogenic sources of mineral dust has gained increasing attention from scientific communities in recent years. Indeed, these airborne dust particles, once lifted over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the oceans resulting in important biogeochemical impacts on the ecosystem. Due to the relatively short lifetime (a few hours to about a week), the distributions of these mineral dust particles vary extensively in both space and time. Consequently, satellite observations are needed over both source and sink regions for continuous temporal and spatial sampling of aerosol properties. With the launch of SeaWiFS in 1997, Terra/MODIS in 1999, and Aqua/MODIS in 2002, high quality comprehensive aerosol climatology is becoming feasible for the first time. As a result of these unprecedented satellite data records, studies of the radiative and biogeochemical effects due to dust aerosols are now possible. In this study, we will show the comparisons of satellite retrieved aerosol optical thickness using Deep Blue algorithm with data from AERONET sunphotometers over desert and semi-desert regions as well as vegetated areas. Our results indicate reasonable agreements between these two. These new satellite products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from Sea WiFS and MODIS-like instruments. The multiyear satellite measurements since 1997 from Sea WiFS will be compared with those retrieved from MODIS and MISR, and will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with the dust outbreaks over the entire globe. Finally, the trends observed over the last decade based upon the SeaWiFS time series in the amounts of tropospheric aerosols due to natural and anthropogenic sources (such as changes in the frequency of dust storms) will be discussed.

Wetland Restoration Response Analysis using MODIS and Groundwater Data

PubMed Central

Melesse, Assefa M.; Nangia, Vijay; Wang, Xixi; McClain, Michael

2007-01-01

Vegetation cover and groundwater level changes over the period of restoration are the two most important indicators of the level of success in wetland ecohydrological restoration. As a result of the regular presence of water and dense vegetation, the highest evapotranspiration (latent heat) rates usually occur within wetlands. Vegetation cover and evapotranspiration of large areas of restoration like that of Kissimmee River basin, South Florida will be best estimated using remote sensing technique than point measurements. Kissimmee River basin has been the area of ecological restoration for some years. The current ecohydrological restoration activities were evaluated through fractional vegetation cover (FVC) changes and latent heat flux using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Groundwater level data were also analyzed for selected eight groundwater monitoring wells in the basin. Results have shown that the average fractional vegetation cover and latent heat along 10 km buffer of Kissimmee River between Lake Kissimmee and Lake Okeechobee was higher in 2004 than in 2000. It is evident that over the 5-year period of time, vegetated and areas covered with wetlands have increased significantly especially along the restoration corridor. Analysis of groundwater level data (2000-2004) from eight monitoring wells showed that, the average monthly level of groundwater was increased by 20 cm and 34 cm between 2000 and 2004, and 2000 and 2003, respectively. This change was more evident for wells along the river. PMID:28903205

A Satellite-Derived Climate-Quality Data Record of the Clear-Sky Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Comiso, Josefino C.; DiGirolamo, Nicolo E.; Shuman, Christopher A.; Key, Jeffrey R.; Koenig, Lora S.

2011-01-01

We have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra ice-surface temperature (1ST) algorithm. A climate-data record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. We present daily and monthly Terra MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 at 6.25-km spatial resolution on a polar stereographic grid within +/-3 hours of 17:00Z or 2:00 PM Local Solar Time. Preliminary validation of the ISTs at Summit Camp, Greenland, during the 2008-09 winter, shows that there is a cold bias using the MODIS IST which underestimates the measured surface temperature by approximately 3 C when temperatures range from approximately -50 C to approximately -35 C. The ultimate goal is to develop a CDR that starts in 1981 with the Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present. Differences in the APP and MODIS cloud masks have so far precluded the current IST records from spanning both the APP and MODIS IST time series in a seamless manner though this will be revisited when the APP dataset has been reprocessed. The Greenland IST climate-quality data record is suitable for continuation using future Visible Infrared Imager Radiometer Suite (VIIRS) data and will be elevated in status to a CDR when at least 9 more years of climate-quality data become available either from MODIS Terra or Aqua, or from the VIIRS. The complete MODIS IST data record will be available online in the summer of 2011.

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

NASA Technical Reports Server (NTRS)

Wang, Aihui; Barlage, Michael; Zeng, Xubin; Draper, Clara Sophie

2014-01-01

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

Land and atmosphere interactions using satellite remote sensing and a coupled mesoscale/land surface model

NASA Astrophysics Data System (ADS)

Hong, Seungbum

Land and atmosphere interactions have long been recognized for playing a key role in climate and weather modeling. However their quantification has been challenging due to the complex nature of the land surface amongst various other reasons. One of the difficult parts in the quantification is the effect of vegetation which are related to land surface processes such soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examined the effects of vegetation and its relationship with soil moisture on the simulated land-atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Then, this study evaluates the model improvements for each simulation method.

Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series

NASA Astrophysics Data System (ADS)

Adami, Marcos; Bernardes, Sérgio; Arai, Egidio; Freitas, Ramon M.; Shimabukuro, Yosio E.; Espírito-Santo, Fernando D. B.; Rudorff, Bernardo F. T.; Anderson, Liana O.

2018-07-01

The development, implementation and enforcement of policies involving the rational use of the land and the conservation of natural resources depend on an adequate characterization and understanding of the land cover, including its dynamics. This paper presents an approach for monitoring vegetation dynamics using high-quality time series of MODIS surface reflectance data by generating fraction images using Linear Spectral Mixing Model (LSMM) over South America continent. The approach uses physically-based fraction images, which highlight target information and reduce data dimensionality. Further dimensionality was also reduced by using the vegetation fraction images as input to a Principal Component Analysis (PCA). The RGB composite of the first three PCA components, accounting for 92.9% of the dataset variability, showed good agreement with the main ecological regions of South America continent. The analysis of 21 temporal profiles of vegetation fraction values and precipitation data over South America showed the ability of vegetation fractions to represent phenological cycles over a variety of environments. Comparisons between vegetation fractions and precipitation data indicated the close relationship between water availability and leaf mass/chlorophyll content for several vegetation types. In addition, phenological changes and disturbance resulting from anthropogenic pressure were identified, particularly those associated with agricultural practices and forest removal. Therefore the proposed method supports the management of natural and non-natural ecosystems, and can contribute to the understanding of key conservation issues in South America, including deforestation, disturbance and fire occurrence and management.

Daily MODIS Data Trends of Hurricane-Induced Forest Impact and Early Recovery

NASA Technical Reports Server (NTRS)

Ramsey, Elijah, III; Spruce, Joseph; Rangoonwala, Amina; Suzuoki, Yukihiro; Smoot, James; Gasser, Jerry; Bannister, Terri

2011-01-01

We studied the use of daily satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors to assess wetland forest damage and recovery from Hurricane Katrina (29 August 2005 landfall). Processed MODIS daily vegetation index (VI) trends were consistent with previously determined impact and recovery patterns provided by the "snapshot" 25 m Landsat Thematic Mapper optical and RADARSAT-1 synthetic aperture radar satellite data. Phenological trends showed high 2004 and 2005 pre-hurricane temporal correspondence within bottomland hardwood forest communities, except during spring green-up, and temporal dissimilarity between these hardwoods and nearby cypress-tupelo swamp forests (Taxodium distichum [baldcypress] and Nyssa aquatica [water tupelo]). MODIS VI trend analyses established that one year after impact, cypress-tupelo and lightly impacted hardwood forests had recovered to near prehurricane conditions. In contrast, canopy recovery lagged in the moderately and severely damaged hardwood forests, possibly reflecting regeneration of pre-hurricane species and stand-level replacement by invasive trees.

Monitoring and modeling agricultural drought for famine early warning (Invited)

NASA Astrophysics Data System (ADS)

Verdin, J. P.; Funk, C.; Budde, M. E.; Lietzow, R.; Senay, G. B.; Smith, R.; Pedreros, D.; Rowland, J.; Artan, G. A.; Husak, G. J.; Michaelsen, J.; Adoum, A.; Galu, G.; Magadzire, T.; Rodriguez, M.

2009-12-01

The Famine Early Warning Systems Network (FEWS NET) makes quantitative estimates of food insecure populations, and identifies the places and periods during which action must be taken to assist them. Subsistence agriculture and pastoralism are the predominant livelihood systems being monitored, and they are especially drought-sensitive. At the same time, conventional climate observation networks in developing countries are often sparse and late in reporting. Consequently, remote sensing has played a significant role since FEWS NET began in 1985. Initially there was heavy reliance on vegetation index imagery from AVHRR to identify anomalies in landscape greenness indicative of drought. In the latter part of the 1990s, satellite rainfall estimates added a second, independent basis for identification of drought. They are used to force crop water balance models for the principal rainfed staple crops in twenty FEWS NET countries. Such models reveal seasonal moisture deficits associated with yield reduction on a spatially continuous basis. In 2002, irrigated crops in southwest Asia became a concern, and prompted the implementation of a gridded energy balance model to simulate the seasonal mountain snow pack, the main source of irrigation water. MODIS land surface temperature data are also applied in these areas to directly estimate actual seasonal evapotranspiration on the irrigated lands. The approach reveals situations of reduced irrigation water supply and crop production due to drought. The availability of MODIS data after 2000 also brought renewed interest in vegetation index imagery. MODIS NDVI data have proven to be of high quality, thanks to significant spectral and spatial resolution improvements over AVHRR. They are vital to producing rapid harvest assessments for drought-impacted countries in Africa and Asia. The global food crisis that emerged in 2008 has led to expansion of FEWS NET monitoring to over 50 additional countries. Unlike previous practice, these new countries have no local FEWS NET analysts, requiring increased reliance on remote sensing for detection of agricultural drought and potential food insecurity. USGS is increasing its cooperation with NASA, NOAA, and university partners to meet this challenge. New servers for near real time delivery of MODIS NDVI, satellite rainfall estimates, and gridded snow pack estimates are being established. A custom instance of NASA's Land Information System software is also being developed to create a land data assimilation system specifically for FEWS NET domains, data streams, and monitoring and forecast requirements. The system will take better advantage of remote sensing data, including promising new products from the Soil Moisture Active-Passive (SMAP) mission, by integrating them with surface observations for simulation of land surface processes. In this way, the continuous improvement of monitoring and modeling for famine early warning will advance to a new level of sophistication and effectiveness.

Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral

Treesearch

Kellie A. Uyeda; Douglas A. Stow; Dar A. Roberts; Philip J. Riggan

2017-01-01

Multi-temporal satellite imagery can provide valuable information on the patterns of vegetation growth over large spatial extents and long time periods, but corresponding ground-referenced biomass information is often difficult to acquire, especially at an annual scale. In this study, we test the relationship between annual biomass estimated using shrub growth rings...

Spring and autumn phenological variability across environmental gradients of Great Smoky Mountains National Park, USA

Treesearch

Steven P. Norman; William W. Hargrove; William M. Christie

2017-01-01

Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for...

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Treesearch

Linda B. Phillips; Andrew J. Hansen; Curtis H. Flather

2008-01-01

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is...

Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981-2012

NASA Astrophysics Data System (ADS)

Cao, Xiaoming; Feng, Yiming; Wang, Juanle

2017-06-01

This paper has developed a general Ts-NDVI triangle space with vegetation index time-series data from AVHRR and MODIS to monitor soil moisture in the Mongolian Plateau during 1981-2012, and studied the spatio-temporal variations of drought based on the temperature vegetation dryness index (TVDI). The results indicated that (1) the developed general Ts-NDVI space extracted from the AVHRR and MODIS remote sensing data would be an effective method to monitor regional drought, moreover, it would be more meaningful if the single time Ts-NDVI space showed an unstable condition; (2) the inverted TVDI was expected to reflect the water deficit in the study area. It was found to be in close negative agreement with precipitation and 10 cm soil moisture; (3) in the Mongolian Plateau, TVDI presented a zonal distribution with changes in land use/land cover types, vegetation cover and latitude. The soil moisture is low in bare land, construction land and grassland. During 1981-2012, drought was widely spread throughout the plateau, and aridification was obvious in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.

A VARI-Based Relative Greenness from MODIS Data for Computing the Fire Potential Index

NASA Technical Reports Server (NTRS)

Schneider, P.; Roberts, D. A.; Kyriakidis, P. C.

2008-01-01

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate the VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FP outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.

Verification and Validation of NASA-Supported Enhancements to PECAD's Decision Support Tools

NASA Technical Reports Server (NTRS)

McKellipo, Rodney; Ross, Kenton W.

2006-01-01

The NASA Applied Sciences Directorate (ASD), part of the Earth-Sun System Division of NASA's Science Mission Directorate, has partnered with the U.S. Department of Agriculture (USDA) to enhance decision support in the area of agricultural efficiency-an application of national importance. The ASD integrated the results of NASA Earth science research into USDA decision support tools employed by the USDA Foreign Agricultural Service (FAS) Production Estimates and Crop Assessment Division (PECAD), which supports national decision making by gathering, analyzing, and disseminating global crop intelligence. Verification and validation of the following enhancements are summarized: 1) Near-real-time Moderate Resolution Imaging Spectroradiometer (MODIS) products through PECAD's MODIS Image Gallery; 2) MODIS Normalized Difference Vegetation Index (NDVI) time series data through the USDA-FAS MODIS NDVI Database; and 3) Jason-1 and TOPEX/Poseidon lake level estimates through PECAD's Global Reservoir and Lake Monitor. Where possible, each enhanced product was characterized for accuracy, timeliness, and coverage, and the characterized performance was compared to PECAD operational requirements. The MODIS Image Gallery and the GRLM are more mature and have achieved a semi-operational status, whereas the USDA-FAS MODIS NDVI Database is still evolving and should be considered

Short-term and long-term evapotranspiration rates at ecological restoration sites along a large river receiving rare flow events

USGS Publications Warehouse

Shanafield, Margaret; Jurado, Hugo Gutierrez; Burgueño, Jesús Eliana Rodríguez; Hernández, Jorge Ramírez; Jarchow, Christopher; Nagler, Pamela L.

2017-01-01

Many large rivers around the world no longer flow to their deltas, due to ever greater water withdrawals and diversions for human needs. However, the importance of riparian ecosystems is drawing increasing recognition, leading to the allocation of environmental flows to restore river processes. Accurate estimates of riparian plant evapotranspiration (ET) are needed to understand how the riverine system responds to these rare events and achieve the goals of environmental flows. In 2014, historic environmental flows were released into the Lower Colorado River at Morelos Dam (Mexico); this once perennial but now dry reach is the final stretch to the mighty Colorado River Delta. One of the primary goals was to supply native vegetation restoration sites along the reach with water to help seedlings establish and boost groundwater levels to foster the planted saplings. Patterns in ET before, during, and after the flows are useful for evaluating whether this goal was met and understanding the role that ET plays in this now ephemeral river system. Here, diurnal fluctuations in groundwater levels and MODIS data were used to compare estimates of ET specifically at three native vegetation restoration sites during 2014 planned flow events, while MODIS data was used to evaluate long-term (2002 – 2016) ET responses to restoration efforts at these sites. Overall, ET was generally 0 - 10 mm d-1 across sites and although daily ET values from groundwater data were highly variable, weekly averaged estimates were highly correlated with MODIS-derived estimates at most sites. The influence of the 2014 flow events was not immediately apparent in the results, although the process of clearing vegetation and planting native vegetation at the restoration sites was clearly visible in the results.

Simulating the effects of fire disturbance and vegetation recovery on boreal ecosystem carbon fluxes

NASA Astrophysics Data System (ADS)

Yi, Y.; Kimball, J. S.; Jones, L. A.; Zhao, M.

2011-12-01

Fire related disturbance and subsequent vegetation recovery has a major influence on carbon storage and land-atmosphere CO2 fluxes in boreal ecosystems. We applied a synthetic approach combining tower eddy covariance flux measurements, satellite remote sensing and model reanalysis surface meteorology within a terrestrial carbon model framework to estimate fire disturbance and recovery effects on boreal ecosystem carbon fluxes including gross primary production (GPP), ecosystem respiration and net CO2 exchange (NEE). A disturbance index based on MODIS land surface temperature and NDVI was found to coincide with vegetation recovery status inferred from tower chronosequence sites. An empirical algorithm was developed to track ecosystem recovery status based on the disturbance index and used to nudge modeled net primary production (NPP) and surface soil organic carbon stocks from baseline steady-state conditions. The simulations were conducted using a satellite based terrestrial carbon flux model driven by MODIS NDVI and MERRA reanalysis daily surface meteorology inputs. The MODIS (MCD45) burned area product was then applied for mapping recent (post 2000) regional disturbance history, and used with the disturbance index to define vegetation disturbance and recovery status. The model was then applied to estimate regional patterns and temporal changes in terrestrial carbon fluxes across the entire northern boreal forest and tundra domain. A sensitivity analysis was conducted to assess the relative importance of fire disturbance and recovery on regional carbon fluxes relative to assumed steady-state conditions. The explicit representation of disturbance and recovery effects produces more accurate NEE predictions than the baseline steady-state simulations and reduces uncertainty regarding the purported missing carbon sink in the high latitudes.

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Using MODIS and GRACE to assess water storage in regional Wetlands: Iraqi and Sudd Marsh systems

NASA Astrophysics Data System (ADS)

Becker, R.

2015-12-01

Both The Iraqi (Mesopotamian) Marshes, an extensive wetlands system in Iraq, and the Sudd Marshlands, located in Sudan have been heavily impacted by both human and climate forces over the past decades. The Sudd wetlands are highly variable in size, averaging roughly 30,000 km2, but extending to as large as ~130,000 km2 during the wet seasons, while the Iraqi marshes are smaller, at ~15,000 km2, without the same extent of intra-annual variability. A combination of MODIS and GRACE images from 2003-2015 for the study areas were used to determine the time dependent change in surface water area (SWA) in the marshes, marshland extent and variability in total water storage. Combined open water area and vegetation abundance and cover, as determined by MODIS (NDVI and MNDWI), is highly correlated with total mass variability observed by GRACE (RL05 Tellus land grid). Annual variability in the Iraqi marshes correlates well with combined SWA and vegetation extent. Variability of vegetation in the Sudd marshes is seen to correlate well on an annual basis with water storage variation, and with a 2 month lag (water mass increases and decreases lead vegetation increases and decreases) when examined on a monthly basis. As a result, in both systems, the overall wetlands extent and health is observed to be water limited. Predictions for precipitation variability and human diversions of water through either dam storage or navigation modifications are predicted to lower water availability and lower variability in these systems. These two regional wetlands systems will shrink, with resulting loss in habitat and other ecosystem services.

Understanding the relationship between vegetation phenology and productivity across key dryland ecosystem types through the integration of PhenoCam, satellite, and eddy covariance data

NASA Astrophysics Data System (ADS)

Yan, D.; Scott, R. L.; Moore, D. J.; Biederman, J. A.; Smith, W. K.

2017-12-01

Land surface phenology (LSP) - defined as remotely sensed seasonal variations in vegetation greenness - is intrinsically linked to seasonal carbon uptake, and is thus commonly used as a proxy for vegetation productivity (gross primary productivity; GPP). Yet, the relationship between LSP and GPP remains uncertain, particularly for understudied dryland ecosystems characterized by relatively large spatial and temporal variability. Here, we explored the relationship between LSP and the phenology of GPP for three dominant dryland ecosystem types, and we evaluated how these relationships change as a function of spatial and temporal scale. We focused on three long-term dryland eddy covariance flux tower sites: Walnut Gulch Lucky Hills Shrubland (WHS), Walnut Gulch Kendall Grassland (WKG), and Santa Rita Mesquite (SRM). We analyzed daily canopy-level, 16-day 30m, and 8-day 500m time series of greenness indices from PhenoCam, Landsat 7 ETM+/Landsat 8 OLI, and MODIS, respectively. We first quantified the impact of spatial scale by temporally resampling canopy-level PhenoCam, 30m Landsat, and 500m MODIS to 16-day intervals and then comparing against flux tower GPP estimates. We next quantified the impact of temporal scale by spatially resampling daily PhenoCam, 16-day Landsat, and 8-day MODIS to 500m time series and then comparing against flux tower GPP estimates. We find evidence of critical periods of decoupling between LSP and the phenology of GPP that vary according to the spatial and temporal scale, and as a function of ecosystem type. Our results provide key insight into dryland LSP and GPP dynamics that can be used in future efforts to improve ecosystem process models and satellite-based vegetation productivity algorithms.

The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests

NASA Astrophysics Data System (ADS)

Jin, Yufang; Randerson, James T.; Goetz, Scott J.; Beck, Pieter S. A.; Loranty, Michael M.; Goulden, Michael L.

2012-03-01

Severity of burning can influence multiple aspects of forest composition, carbon cycling, and climate forcing. We quantified how burn severity affected vegetation recovery and albedo change during early succession in Canadian boreal regions by combining satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Canadian Large Fire Database. We used the MODIS-derived difference Normalized Burn Ratio (dNBR) and initial changes in spring albedo as measures of burn severity. We found that the most severe burns had the greatest reduction in summer MODIS Enhanced Vegetation Index (EVI) in the first year after fire, indicating greater loss of vegetation cover. By 5-8 years after fire, summer EVI for all severity classes had recovered to within 90%-108% of prefire levels. Spring and summer albedo progressively increased during the first 7 years after fire, with more severely burned areas showing considerably larger postfire albedo increases during spring and more rapid increases during summer as compared with moderate- and low-severity burns. After 5-7 years, increases in spring albedo above prefire levels were considerably larger in high-severity burns (0.20 ± 0.06; defined by dNBR percentiles greater than 75%) as compared to changes observed in moderate- (0.16 ± 0.06; for dNBR percentiles between 45% and 75%) or low-severity burns (0.13 ± 0.06; for dNBR percentiles between 20% and 45%). The sensitivity of spring albedo to dNBR was similar in all ecozones and for all vegetation types along gradients of burn severity. These results suggest carbon losses associated with increases in burn severity observed in some areas of boreal forests may be at least partly offset, in terms of climate impacts, by increases in negative forcing associated with changes in surface albedo.

Recent Update on MODIS/VIIRS Deep Blue Data Continuity and New Aerosol Products

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Sayer, Andrew M.; Lee, Jaehwa; Bettenhausen, Corey; Carletta, N.; Tsay, Si-Chee

2016-01-01

The MODIS VIIRS 2016 Science Team Meeting was held June 6-10, 2016 at the Sheraton in Silver Spring, MD. The organizers plan to post the presentations and posters here: http:modis.gsfc.nasa.govsci_teammeetings201606.

Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2010-05-01

The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the range 2.0-2.6, 2.5-3.7, and 3.5-4.9°C respectively. The dataset of remote sensing products has been compiled on the base of special technology using Internet resources, that includes MODIS-based estimates of land surface temperature (LST) Tsg, E, NDVI, LAI for the region of interest and the same vegetation seasons. Two types of MODIS-based Тsg and E estimates have been extracted from LP DAAC web-site (for separate dates of 2003-2009 time period): LST/E Daily L3 product (MOD11В1) with spatial resolution ~ 4.8 km and LST/E 5-Min L2 product (MOD11_L2) with spatial resolution ~ 1 km. The verification of Tsg estimates has been performed via comparison with analogous and collocated AVHRR-based ones. Along with this the sample of SEVIRI-based Tsg and E estimates has been accumulated for the Kursk area and surrounding territories for the time interval of several days during 2009 vegetation season. To retrieve Тsg and Е from SEVIRI/Meteosat-8, -9 data the new method has been developed. Being designed as the combination of well-known Split Window Technique and Two Temperature Method algorithms it provides the derivation of Тsg from SEVIRI/Meteosat-9 measurements carried out at three successive times (classified as 100% cloud-free) and covering the region under consideration without accurate a priory knowledge of E. Comparison of the SEVIRI-based Tsg retrievals with the independent collocated Tsg estimates gives the values of RMS deviation in the range of 0.9-1.4°C and it proves (indirectly) the efficiency of proposed approach. To assimilate satellite-derived estimates of vegetation characteristics and LST in the SVAT model some procedures have been developed. These procedures have included: 1) the replacement of LAI and B ground and point-wise estimates by their AVHRR- or MODIS-based analogues. The efficiency of such approach has been proved through comparison between satellite-derived and ground-based seasonal time behaviors of LAI and B, between satellite-derived, modeled, and in-situ measured temperatures as well as through comparison the modeled and actual values of evapotranspiration Ev and soil water content W for one meter soil layer. The discrepancies between mentioned temperatures do not exceed the RMS errors of satellite-derived estimates Ta, Ts.eff and Tsg while the modeled and measured values of Ev and W have been found close to each other within their standard estimation error; 2) the treating AVHRR- or MODIS-based LST as the input model variable within the SVAT model instead their standard ground-based estimates if the satisfactory time-matching of satellite and ground-based observations takes place. The SEVIRI-derived Tsg can be also used for these aims. Permissibility of such replacement has been verified while comparing remote sensed, modeled and ground-based temperatures as well as calculated and measured values of W and Ev. The SEVIRI-based Tsg estimates were found to be very informative and useful due to their high temporal resolution. Moreover the approach has been developed to account for space variability of vegetation cover parameters and meteorological characteristics. This approach includes the development of algorithms and programs for entering AVHRR- and MODIS-derived LAI and B, all named satellite-based LSTs as well as ground-based precipitation, air temperature and humidity data prepared by Inverse Distance Weighted Average Method into the model in each calculation grid unit. The calculations of vertical water and heat fluxes, soil water and heat contents and other water and heat balance components for Kursk region have been carried out with the help of the SVAT model using fields of AVHRR/3- and MODIS-derived LAI and B and AVHRR/3-, MODIS, and SEVIRI-derived LST for various vegetation seasons of 2003-2009. The acceptable accuracy levels of above values assessment have been achieved under all scenarios of parameter and input model variable specification. Thus, the results of this study confirm the opportunity of using area distributed satellite-derived estimates of land surface characteristics for the model calculations of water and heat balance components for large territories especially under the lack of ground observation data. The present study was carried out with support of the Russian Foundation of Basic Researches - grant N 10-05-00807.

A Satellite-Derived Climate-Quality Data Record of the Clear-Sky Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Comiso, Josefino C.; DiGirolamo, Nikolo E.; Shuman, Christopher A.; Key, Jeffrey R.; Koenig, Lora S.

2012-01-01

We have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (1ST) algorithm. A climate-data record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. We present daily and monthly MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 at 6.25-km spatial resolution on a polar stereographic grid. This record will be elevated in status to a CDR when at least nine more years of data become available either from MODIS Terra or Aqua, or from the Visible Infrared Imager Radiometer Suite (VIIRS) to be launched in October 2011. Our ultimate goal is to develop a CDR that starts in 1981 with the Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present, and into the VIIRS era. Differences in the APP and MODIS cloud masks have so far precluded the current 1ST records from spanning both the APP and MODIS time series in a seamless manner though this will be revisited when the APP dataset has been reprocessed. The complete MODIS 1ST daily and monthly data record is available online.

Relation of MODIS EVI and LAI across time, vegetation types and hydrological regimes

NASA Astrophysics Data System (ADS)

Alexandridis, Thomas; Ovakoglou, George

2015-04-01

Estimation of the Leaf Area Index (LAI) of a landscape is considered important to describe the ecosystems activity and is used as an important input parameter in hydrological and biogeochemical models related to water and carbon cycle, desertification risk, etc. The measurement of LAI in the field is a laborious and costly process and is mainly done by indirect methods, such as hemispherical photographs that are processed by specialized software. For this reason there have been several attempts to estimate LAI with multispectral satellite images, using theoretical biomass development models, or empirical equations using vegetation indices and land cover maps. The aim of this work is to study the relation of MODIS EVI and LAI across time, vegetation type, and hydrological regime. This was achieved by studying 120 maps of EVI and LAI which cover a hydrological year and five hydrologically diverse areas: river Nestos in Greece, Queimados catchment in Brazil, Rijnland catchment in The Netherlands, river Tamega in Portugal, and river Umbeluzi in Mozambique. The following Terra MODIS composite datasets were downloaded for the hydrological year 2012-2013: MOD13A2 "Vegetation Indices" and MCD15A2 "LAI and FPAR", as well as the equivalent quality information layers (QA). All the pixels that fall in a vegetation land cover (according to the MERIS GLOBCOVER map) were sampled for the analysis, with the exception of those that fell at the border between two vegetation or other land cover categories, to avoid the influence of mixed pixels. Using linear regression analysis, the relationship between EVI and LAI was identified per date, vegetation type and study area. Results show that vegetation type has the highest influence in the variation of the relationship between EVI and LAI in each study area. The coefficient of determination (R2) is high and statistically significant (ranging from 0.41 to 0.83 in 90% of the cases). When plotting the EVI factor from the regression equation across time, there is an evident temporal change in all test sites. The sensitivity of EVI to LAI is smaller in periods of high biomass production. The range of fluctuation is different across sites, and is related to biomass quantity and type. Higher fluctuation is noted in the winter season in Tamega, possibly due to cloud infected pixels that the QA and compositing algorithms did not successfully detect. Finally, there was no significant difference in the R2 and EVI factor when including in the analyses pixels indicated as "low and marginal quality" by the QA layers, thus suggesting that the use of low quality pixels can be justified when good quality pixels are not enough. Future work will study the transferability of these relations across scales and sensors. This study is supported by the Research Committee of Aristotle University of Thessaloniki project "Improvement of the estimation of Leaf Area Index (LAI) at basin scale using satellite images". MODIS data are provided by USGS.

Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data

USGS Publications Warehouse

Maiersperger, Tom; Scaramuzza, Pat; Leigh, Larry; Shrestha, S.; Gallo, Kevin; Jenkerson, Calli B.; Dwyer, John L.

2013-01-01

This study provides a baseline quality check on provisional Landsat Surface Reflectance (SR) products as generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software. Characterization of the Landsat SR products leveraged comparisons between aerosol optical thickness derived from LEDAPS and measured by Aerosol Robotic Network (AERONET), as well as reflectance correlations with field spectrometer and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Results consistently indicated similarity between LEDAPS and alternative data products in longer wavelengths over vegetated areas with no adjacent water, while less reliable performance was observed in shorter wavelengths and sparsely vegetated areas. This study demonstrates the strengths and weaknesses of the atmospheric correction methodology used in LEDAPS, confirming its successful implementation to generate Landsat SR products.

Modeling the effects of anthropogenic habitat change on savanna snake invasions into African rainforest.

PubMed

Freedman, Adam H; Buermann, Wolfgang; Lebreton, Matthew; Chirio, Laurent; Smith, Thomas B

2009-02-01

We used a species-distribution modeling approach, ground-based climate data sets, and newly available remote-sensing data on vegetation from the MODIS and Quick Scatterometer sensors to investigate the combined effects of human-caused habitat alterations and climate on potential invasions of rainforest by 3 savanna snake species in Cameroon, Central Africa: the night adder (Causus maculatus), olympic lined snake (Dromophis lineatus), and African house snake (Lamprophis fuliginosus). Models with contemporary climate variables and localities from native savanna habitats showed that the current climate in undisturbed rainforest was unsuitable for any of the snake species due to high precipitation. Limited availability of thermally suitable nest sites and mismatches between important life-history events and prey availability are a likely explanation for the predicted exclusion from undisturbed rainforest. Models with only MODIS-derived vegetation variables and savanna localities predicted invasion in disturbed areas within the rainforest zone, which suggests that human removal of forest cover creates suitable microhabitats that facilitate invasions into rainforest. Models with a combination of contemporary climate, MODIS- and Quick Scatterometer-derived vegetation variables, and forest and savanna localities predicted extensive invasion into rainforest caused by rainforest loss. In contrast, a projection of the present-day species-climate envelope on future climate suggested a reduction in invasion potential within the rainforest zone as a consequence of predicted increases in precipitation. These results emphasize that the combined responses of deforestation and climate change will likely be complex in tropical rainforest systems.

An Evaluation of MODIS-Retrieved Aerosol Optical Depth over a Mountainous AERONET Site in the Southeastern US

NASA Technical Reports Server (NTRS)

Sherman, James P.; Gupta, Pawan; Levy, Robert C.; Sherman, Peter J.

2016-01-01

The literature shows that aerosol optical depth (AOD) derived from the MODIS Collection 5 (C5) dark target algorithm has been extensively validated by spatiotemporal collocation with AERONET sites on both global and regional scales.Although generally comparing well over the eastern US region, poor performance over mountains in other regions indicate the need to evaluate the MODIS product over a mountain site. This study compares MODIS C5 AOD at 550nm to AOD measured at the Appalachian State University AERONET site in Boone, NC over 30 months between August 2010 and September 2013. For the combined Aqua and Terra datasets, although more than 70% of the 500 MODIS AOD measurements agree with collocated AERONET AOD to within error envelope of +/- (0.05 + 15%), MODIS tends to have a low bias (0.02-0.03). The agreement between MODIS and AERONET AOD does not depend on MODIS quality assurance confidence (QAC) value. However, when stratified by satellite, MODIS-Terra data does not perform as well as Aqua, with especially poor correlation (r = 0.39) for low aerosol loading conditions (AERONET AOD less than 0.15).Linear regressions between Terra and AERONET possess statistically-different slopes for AOD < 0.15 and AOD > or = 0.15. AERONET AOD measured only during MODIS overpass hours is highly correlated with daily-averaged AERONET AOD. MODIS monthly-averaged AOD also tracks that of AERONET over the study period. These results indicate that MODIS is sensitive to the day-to-day variability, as well as the annual cycle of AOD over the Appalachian State AERONET site. The complex topography and high seasonality in AOD and vegetation indices allow us to specifically evaluate MODIS dark target algorithm surface albedo and aerosol model assumptions at a regionally-representative SE US mountain site.

Recent Weather Extremes and Impacts on Agricultural Production and Vector-Borne Disease Outbreak Patterns

PubMed Central

Anyamba, Assaf; Small, Jennifer L.; Britch, Seth C.; Tucker, Compton J.; Pak, Edwin W.; Reynolds, Curt A.; Crutchfield, James; Linthicum, Kenneth J.

2014-01-01

We document significant worldwide weather anomalies that affected agriculture and vector-borne disease outbreaks during the 2010–2012 period. We utilized 2000–2012 vegetation index and land surface temperature data from NASA's satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to map the magnitude and extent of these anomalies for diverse regions including the continental United States, Russia, East Africa, Southern Africa, and Australia. We demonstrate that shifts in temperature and/or precipitation have significant impacts on vegetation patterns with attendant consequences for agriculture and public health. Weather extremes resulted in excessive rainfall and flooding as well as severe drought, which caused ∼10 to 80% variation in major agricultural commodity production (including wheat, corn, cotton, sorghum) and created exceptional conditions for extensive mosquito-borne disease outbreaks of dengue, Rift Valley fever, Murray Valley encephalitis, and West Nile virus disease. Analysis of MODIS data provided a standardized method for quantifying the extreme weather anomalies observed during this period. Assessments of land surface conditions from satellite-based systems such as MODIS can be a valuable tool in national, regional, and global weather impact determinations. PMID:24658301

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

NASA Astrophysics Data System (ADS)

Nasanbat, E.; Sharav, S.; Sanjaa, T.; Lkhamjav, O.; Magsar, E.; Tuvdendorj, B.

2018-04-01

This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin

USGS Publications Warehouse

Tan, Zhengxi; Liu, Shu-Guang; Jenkerson, Calli B.; Oeding, Jennifer; Wylie, Bruce K.; Rover, Jennifer R.; Young, Claudia J.

2012-01-01

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components

a New Algorithm for the Aod Inversion from Noaa/avhrr Data

NASA Astrophysics Data System (ADS)

Sun, L.; Li, R.; Yu, H.

2018-04-01

The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74 % of the retrieved AOD are within the expected error range of ±(0.05 + 0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.

Estimating Irrigation Water Requirements using MODIS Vegetation Indices and Inverse Biophysical Modeling

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Bounoua, Lahouari; Harriss, Robert; Harriss, Robert; Wells, Gordon; Glantz, Michael; Dukhovny, Victor A.; Orlovsky, Leah

2007-01-01

An inverse process approach using satellite-driven (MODIS) biophysical modeling was used to quantitatively assess water resource demand in semi-arid and arid agricultural lands by comparing the carbon and water flux modeled under both equilibrium (in balance with prevailing climate) and non-equilibrium (irrigated) conditions. Since satellite observations of irrigated areas show higher leaf area indices (LAI) than is supportable by local precipitation, we postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. For an observation year we used MODIS vegetation indices, local climate data, and the SiB2 photosynthesis-conductance model to examine the relationship between climate and the water stress function for a given grid-cell and observed leaf area. To estimate the minimum amount of supplemental water required for an observed cell, we added enough precipitation to the prevailing climatology at each time step to minimize the water stress function and bring the soil to field capacity. The experiment was conducted on irrigated lands on the U.S. Mexico border and Central Asia and compared to estimates of irrigation water used.

Modeling gross primary production in semi-arid Inner Mongolia using MODIS imagery and eddy covariance data

Treesearch

Ranjeet John; Jiquan Chen; Asko Noormets; Xiangming Xiao; Jianye Xu; Nan Lu; Shiping Chen

2013-01-01

We evaluate the modelling of carbon fluxes from eddy covariance (EC) tower observations in different water-limited land-cover/land-use (LCLU) and biome types in semi-arid Inner Mongolia, China. The vegetation photosynthesis model (VPM) and modified VPM (MVPM), driven by the enhanced vegetation index (EVI) and land-surface water index (LSWI), which were derived from the...

Estimation of Crop Gross Primary Production (GPP): I. Impact of MODIS Observation Footprint and Impact of Vegetation BRDF Characteristics

NASA Technical Reports Server (NTRS)

Zhang, Qingyuan; Cheng, Yen-Ben; Lyapustin, Alexei I.; Wang, Yujie; Xiao, Xiangming; Suyker, Andrew; Verma, Shashi; Tan, Bin; Middleton, Elizabeth M.

2014-01-01

Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) = 35? to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA = 35?. The fourth experiment included only backscatter observations with VZA = 35?. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. Changes in R2, RMSE,CV and changes in model coefficients "a" and "b" (experiment 2 vs. experiment 1; and experiment 4 vs. experiment 3) were indicators of the impacts. The second experiment produced better performance than the first experiment, increasing R2 (?0.13) and reducing RMSE (?0.68 g C m-2 d-1) and CV (?9%). For each VI, the slope of GPP = a × VI × PAR in the second experiment for each crop type changed little while the slope and intercept of GPP = a × VI × PAR + b varied field by field. The CIgreen was least affected by the MODIS observation footprint in estimating crop daily GPP (R2, ?0.08; RMSE, ?0.42 g C m-2 d-1; and CV, ?7%). Footprint most affected the NDVI (R2, ?0.15; CV, ?10%) and the EVI (RMSE, ?0.84 g C m-2 d-1). The vegetation BRDF impact also caused variation of model performance and change of model coefficients. Significantly different slopes were obtained for forward vs. backscatter observations, especially for the CIgreen and the NDVI. Both the footprint impact and the BRDF impact varied with crop types, irrigation options, model options and VI options.

Monitoring vegetation recovery in fire-affected areas using temporal profiles of spectral signal from time series MODIS and LANDSAT satellite images

NASA Astrophysics Data System (ADS)

Georgopoulou, Danai; Koutsias, Nikos

2015-04-01

Vegetation phenology is an important element of vegetation characteristics that can be useful in vegetation monitoring especially when satellite remote sensing observations are used. In that sense temporal profiles extracted from spectral signal of time series MODIS and LANDSAT satellite images can be used to characterize vegetation phenology and thus to be helpful for monitoring vegetation recovery in fire-affected areas. The aim of this study is to explore the vegetation recovery pattern of the catastrophic wildfires that occurred in Peloponnisos, southern Greece, in 2007. These fires caused the loss of 67 lives and were recognized as the most extreme natural disaster in the country's recent history. Satellite remote sensing data from MODIS and LANDSAT satellites in the period from 2000 to 2014 were acquired and processed to extract the temporal profiles of the spectral signal for selected areas within the fire-affected areas. This dataset and time period analyzed together with the time that these fires occurred gave the opportunity to create temporal profiles seven years before and seven years after the fire. The different scale of the data used gave us the chance to understand how vegetation phenology and therefore the recovery patterns are influenced by the spatial resolution of the satellite data used. Different metrics linked to key phenological events have been created and used to assess vegetation recovery in the fire-affected areas. Our analysis was focused in the main land cover types that were mostly affected by the 2007 wildland fires. Based on CORINE land-cover maps these were agricultural lands highly interspersed with large areas of natural vegetation followed by sclerophyllous vegetation, transitional woodland shrubs, complex cultivation patterns and olive groves. Apart of the use of the original spectral data we estimated and used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. In this study we explore the strength and the use of these time series satellite data to characterize vegetation phenology as an a aid to monitor vegetation recovery in fire affected-areas. In a recent study we found that the original spectral channels, based on which these indices are estimated, are sensitive to external vegetation parameters such as the spectral reflectance of the background soil. In such cases, the influence of the soil in the reflectance values is different in the various spectral regions depending on its type. The use of such indices is also justified according to a recent study on the sensitivity of spectral reflectance values to different burn and vegetation ratios, who concluded that the Near Infrared (NIR) and Short-Wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas. Additionally, it has been found that semi-burned classes are spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated.

Early Evaluation of the VIIRS Calibration, Cloud Mask and Surface Reflectance Earth Data Records

NASA Technical Reports Server (NTRS)

Vermote, Eric; Justice, Chris; Csiszar, Ivan

2014-01-01

Surface reflectance is one of the key products fromVIIRS and as withMODIS, is used in developing several higherorder land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is based on the heritageMODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites showthatwith the fewadjustments recommended, the accuracy iswithin the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record.

Assessment of the abnormal growth of floating macrophytes in Winam Gulf (Kenya) by using MODIS imagery time series

NASA Astrophysics Data System (ADS)

Fusilli, L.; Collins, M. O.; Laneve, G.; Palombo, A.; Pignatti, S.; Santini, F.

2013-02-01

The objective of this research study is to assess the capability of time-series of MODIS imagery to provide information suitable for enhancing the understanding of the temporal cycles shown by the abnormal growth of the floating macrophytes in order to support monitoring and management action of Lake Victoria water resources. The proliferation of invasive plants and aquatic weeds is of growing concern. Starting from 1989, Lake Victoria has been interested by the high infestation of water hyacinth with significant socio-economic impact on riparian populations. In this paper, we describe an approach based on the time-series of MODIS to derive the temporal behaviour, the abundance and distribution of the floating macrophytes in the Winam Gulf (Kenyan portion of the Lake Victoria) and its possible links to the concentrations of the main water constituencies. To this end, we consider the NDVI values computed from the MODIS imagery time-series from 2000 to 2009 to identify the floating macrophytes cover and an appropriate bio-optical model to retrieve, by means of an inverse procedure, the concentrations of chlorophyll a, coloured dissolved organic matter and total suspended solid. The maps of the floating vegetation based on the NDVI values allow us to assess the spatial and temporal dynamics of the weeds with high time resolution. A floating vegetation index (FVI) has been introduced for describing the weeds pollution level. The results of the analysis show a consistent temporal relation between the water constituent concentrations within the Winam Gulf and the FVI, especially in the proximity of the greatest proliferation of floating vegetation in the last 10 years that occurred between the second half of 2006 and the first half of 2007.The adopted approach will be useful to implement an automatic system for monitoring and predicting the floating macrophytes proliferation in Lake Victoria.

Predicting Treatment Windows for Invasive Buffelgrass in Southern Arizona using MODIS and Climate Data

NASA Astrophysics Data System (ADS)

Wallace, C.; Weltzin, J. F.; Skirvin, S. M.; Patrick-Birdwell, C.; Raichle, H.

2014-12-01

The increasing spread and abundance of an invasive perennial grass, buffelgrass (Pennisetum ciliare), represents an important shift in the vegetation composition of the Sonoran Desert in southern Arizona. Buffelgrass out-competes native species and alters fire regimes, and its control and management is a high-priority issue for resource managers who seek to preserve the unique and iconic Sonoran Desert flora. Herbicidal treatment of buffelgrass is most effective when the vegetation is actively growing; however, the erratic timing and length of active buffelgrass growth periods in southern Arizona confound effective management decision-making. The goal of our research is to enable the strategic application of buffelgrass herbicide by using remote sensing data to detect when and where buffelgrass is photosynthetically active. We integrated ground-based observations of buffelgrass phenology (green-up and senescence) in the Tucson, Arizona area with climate information and Moderate-resolution Imaging Spectroradiometer (MODIS) satellite imagery at 250m spatial and both 8-day and 16-day composite temporal resolution to understand dynamics, relationships and resonance between these disparate datasets during 2011 to 2013. Fourier harmonics analysis was used to derive land surface phenology (LSP) metrics from MODIS Enhanced Vegetation Index (EVI) greenness data and to quantify the temporal patterns of the climate and phenophase abundance datasets. Regression analyses and statistical tests were used to identify correlations between temporal patterns of the data sets. Our results reveal strong correlations between the observed greenness of in-situ buffelgrass and satellite LSP metrics, confirming that MODIS-EVI data can be a useful indicator of active buffelgrass growth at multiple scales. The analysis also reveals strong harmonics between precipitation and greenness, but with a lagged response, suggesting that precipitation can be a predictor of the location and intensity of buffelgrass green-up at landscape scales. This information can be used by resource managers to treat buffelgrass during optimal conditions.

System for Continuous Delivery of MODIS Imagery to Internet Mapping Applications

NASA Technical Reports Server (NTRS)

Plesea, Lucian

2008-01-01

This software represents a complete, unsupervised processing chain that generates a continuously updating global image of the Earth from the most recent available MODIS Level 1B scenes. The software constantly updates a global image of the Earth at 250 m per pixel.

Wide-area ratios of evapotranspiration to precipitation in monsoon-dependent semiarid vegetation communities

USGS Publications Warehouse

Glenn, Edward P.; Scott, Russell L.; Nguyen, Uyen; Nagler, Pamela L.

2015-01-01

Evapotranspiration (ET) and the ratio of ET to precipitation (PPT) are important factors in the water budget of semiarid rangelands and are in part determined by the dominant plant communities. Our goal was to see if landscape changes such as tree or shrub encroachment and replacement of native grasses by invasive grasses impacted ET and ET/PPT and therefore watershed hydrology in this biome. We determined ET and ET/PPT for shrublands, grasslands and mesquite savannas in southern Arizona at five moisture flux towers and determined the environmental factors controlling ET in each plant community. We then scaled ET over areas of 4–36 km2, representing homogeneous patches of each plant community, using the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite. Over wide areas, estimated ET/PPT projected from MODIS EVI ranged from 0.71 for a sparsely-vegetated shrub site to 1.00 for grasslands and mesquite savannas. The results did not support hypotheses that encroachment of mesquites into grasslands or that replacement of native grasses with introduced Eragrostis lehmanniana (lehmann lovegrass) have increased rangeland ET.

Terrestrial vegetation dynamics and global climate controls

NASA Astrophysics Data System (ADS)

Potter, Christopher; Boriah, Shyam; Steinbach, Michael; Kumar, Vipin; Klooster, Steven

2008-07-01

Monthly data from the moderate resolution imaging spectroradiometer (MODIS) and its predecessor satellite sensors was used to reconstruct vegetation dynamics in response to climate patterns over the period 1983 2005. Results suggest that plant growth over extensive land areas of southern Africa and Central Asia were the most closely coupled of any major land area to El Niño southern oscillation (ENSO) effects on regional climate. Others land areas strongly tied to recent ENSO climate effects were in northern Canada, Alaska, western US, northern Mexico, northern Argentina, and Australia. Localized variations in precipitation were the most common controllers of monthly values for the fraction absorbed of photosynthetically active radiation (FPAR) over these regions. In addition to the areas cited above, seasonal FPAR values from MODIS were closely coupled to rainfall patterns in grassland and cropland areas of the northern and central US. Historical associations between global vegetation FPAR and atmospheric carbon dioxide (CO2) anomalies suggest that the terrestrial biosphere can contribute major fluxes of CO2 during major drought events, such as those triggered by 1997 1998 El Niño event.

Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002–2011

USGS Publications Warehouse

Alemu, Henok; Senay, Gabriel B.; Kaptue, Armel T.; Kovalskyy, Valeriy

2014-01-01

Evapotranspiration (ET) is a vital component in land-atmosphere interactions. In drylands, over 90% of annual rainfall evaporates. The Nile Basin in Africa is about 42% dryland in a region experiencing rapid population growth and development. The relationship of ET with climate, vegetation and land cover in the basin during 2002–2011 is analyzed using thermal-based Simplified Surface Energy Balance Operational (SSEBop) ET, Normalized Difference Vegetation Index (NDVI)-based MODIS Terrestrial (MOD16) ET, MODIS-derived NDVI as a proxy for vegetation productivity and rainfall from Tropical Rainfall Measuring Mission (TRMM). Interannual variability and trends are analyzed using established statistical methods. Analysis based on thermal-based ET revealed that >50% of the study area exhibited negative ET anomalies for 7 years (2009, driest), while >60% exhibited positive ET anomalies for 3 years (2007, wettest). NDVI-based monthly ET correlated strongly (r > 0.77) with vegetation than thermal-based ET (0.52 < r < 0.73) at p < 0.001. Climate-zone averaged thermal-based ET anomalies positively correlated (r = 0.6, p < 0.05) with rainfall in 4 of the 9 investigated climate zones. Thermal-based and NDVI-based ET estimates revealed minor discrepancies over rainfed croplands (60 mm/yr higher for thermal-based ET), but a significant divergence over wetlands (440 mm/yr higher for thermal-based ET). Only 5% of the study area exhibited statistically significant trends in ET.

Study of Maowusu Sandy Land Vegetation Coverage Change Based on Modis Ndvi

NASA Astrophysics Data System (ADS)

Ye, Q.; Liu, H.; Lin, Y.; Han, R.

2018-04-01

This paper selected 2006-2016 MODIS NDVI data with a spatial resolution of 500m and time resolution of 16d, got the 11 years' time series NDVI data of Maowusu sandy land through mosaicking, projection transformation, cutting process in batch. Analysed the spatial and temporal distribution and variation characteristics of vegetation cover in year, season and month time scales by maximum value composite, and unary linear regression analysis. Then, we combined the meteorological data of 33 sites around the sandy area, analysed the response characteristics of vegetation cover change to temperature and precipitation through Pearson correlation coefficient. Studies have shown that: (1) The NDVI value has a stable increase trend, which rate is 0.0075 / a. (2) The vegetation growth have significantly difference in four seasons, the NDVI value of summer > autumn > spring > winter. (3) The NDVI value change trend is conformed to the gauss normal distribution in a year, and it comes to be largest in August, its green season is in April, and yellow season is in the middle of November, the growth period is about 220 d. (4) The vegetation has a decreasing trend from the southeast to the northwest, most part is slightly improved, and Etuokeqianqi improved significantly. (5) The correlation indexes of annual NDVI with temperature and precipitation are -0.2178 and 0.6309, the vegetation growth is mainly affected by precipitation. In this study, a complete vegetation cover analysis and evaluation model for sandy land is established. It has important guiding significance for the sand ecological environment protection.

Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa

NASA Astrophysics Data System (ADS)

Dubovyk, Olena; Landmann, Tobias; Erasmus, Barend F. N.; Tewes, Andreas; Schellberg, Jürgen

2015-06-01

Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000-2013) of medium spatial resolution (250 m) MODIS-EVI time-series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000-2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012. Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models.

Discrepancy Between ASTER- and MODIS- Derived Land Surface Temperatures: Terrain Effects

PubMed Central

Liu, Yuanbo; Noumi, Yousuke; Yamaguchi, Yasushi

2009-01-01

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. PMID:22399955

Combining Vegetation Index Derived from PhenoCam with EVI to Estimate Daily GPP in Semi-arid Grassland

NASA Astrophysics Data System (ADS)

Wang, H.; Jia, G.

2017-12-01

Accurate estimation of temporal continuous gross primary production (GPP) plays an important role in mechanistic understanding of global carbon budget and exchange between atmosphere and terrestrial ecosystems. Ground based PhenoCam can provide near surface observations of plant phenology with high temporal resolution and have great potential in modeling seasonal dynamics of GPP. However, due to the empirical approaches for estimating fAPAR, there still exist some uncertainties of adopting PhenoCam images in GPP modeling. In this study, we combined excess green index (EGI) derived from PhenoCam and EVI retrieved from MODIS to generate daily time-series of fAPAR (fAPARcam), and then to estimate daily GPP (GPPpre) with a light use efficiency model in semi-arid grassland from 2012 to 2014. Among the three continuous years, daily fAPARcam exhibited similar temporal behaviors with eddy covariance observed GPP (GPPobs). The overall determination coefficients (R2) were all greater than 0.81. GPPpre agreed well with GPPobs and these agreements showed highly statistically significant (p <0.01). R2 ranged from 0.80 to 0.87, RE ranged from -2.9% to 2.81% and RMSE ranged from 0.83 (gC/m2d-1) to 0.98 (gC/m2d-1). GPPpre was then further evaluated by comparing with MODIS GPP products and VPM modeled GPP. Validation showed the variance explained by GPPpre is still the highest. RMSE and RE were also lower than the other two in general. Explanatory power of inputs in GPP modeling was also explored: fAPAR is the most influential input and PAR takes the second place. Contributions of Tscalar and Wscalar are lower than PAR. These results highlight the potential of PhenoCam images in high temporal resolution GPP modeling. Our GPP modeling method will help to reduce uncertainties of using PhenoCam images in monitoring of seasonal development of vegetation production.

Monitoring Spatial Patterns of Vegetation Phenology in AN Australian Tropical Transect Using Modis Evi

NASA Astrophysics Data System (ADS)

Ma, X.; Huete, A.; Yu, Q.; Davies, K.; Coupe, N. R.

2012-07-01

Phenology is receiving increasing interest in the area of climate change and vegetation adaptation to climate. The phenology of a landscape can be used as a key parameter in land surface models and dynamic global vegetation models to more accurately simulate carbon, water and energy exchanges between land cover and atmosphere. However, the characterisation of phenology is lacking in tropical savannas which cover more than 30% of global land area, and are highly vulnerable to climate change. The objective of this study is to investigate the spatial pattern of vegetation phenology along the Northern Australia Tropical Transect (NATT) where the major biomes are wet and dry tropical savannas. For this analysis we used more than 11 years Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) product from 2000 to 2011. Eight phenological metrics were derived: Start of Season (SOS), End of Season (EOS), Length of Season (LOS), Maximum EVI (MaxG), Minimum EVI (MinG), annual amplitude (AMP), large integral (LIG), and small integral (SIG) were generated for each year and each pixel. Our results showed there are significant spatial patterns and considerable interannual variations of vegetation phenology along the NATT study area. Generally speaking, vegetation growing season started and ended earlier in the north, and started and ended later in the south, resulting in a southward decrease of growing season length (LOS). Vegetation productivity, which was represented by annual integral EVI (LIG), showed a significant descending trend from the northern part of NATT to the southern part. Segmented regression analysis showed that there exists a distinguishable breakpoint along the latitudinal gradient, at least in terms of annual minimum EVI (EVI), which is located between 18.84°S to 20.04°S.

Study on generation and sharing of on-demand global seamless data—Taking MODIS NDVI as an example

NASA Astrophysics Data System (ADS)

Shen, Dayong; Deng, Meixia; Di, Liping; Han, Weiguo; Peng, Chunming; Yagci, Ali Levent; Yu, Genong; Chen, Zeqiang

2013-04-01

By applying advanced Geospatial Data Abstraction Library (GDAL) and BigTIFF technology in a Geographical Information System (GIS) with Service Oriented Architecture (SOA), this study has derived global datasets using tile-based input data and implemented Virtual Web Map Service (VWMS) and Virtual Web Coverage Service (VWCS) to provide software tools for visualization and acquisition of global data. Taking MODIS Normalized Difference Vegetation Index (NDVI) as an example, this study proves the feasibility, efficiency and features of the proposed approach.

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2010-01-01

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

Accessing and Understanding MODIS Data

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory; Jenkerson, Calli B.; Jodha, Siri

2003-01-01

The National Aeronautics and Space Administration (NASA) launched the Terra satellite in December 1999, as part of the Earth Science Enterprise promotion of interdisciplinary studies of the integrated Earth system. Aqua, the second satellite from the series of EOS constellation, was launched in May 2002. Both satellites carry the MODerate resolution Imaging Spectroradiometer (MODIS) instrument. MODIS data are processed at the Goddard Space Flight Center, Greenbelt, MD, and then archived and distributed by the Distributed Active Archive Centers (DAACs). Data products from the MODIS sensors present new challenges to remote sensing scientists due to specialized production level, data format, and map projection. MODIS data are distributed as calibrated radiances and as higher level products such as: surface reflectance, water-leaving radiances, ocean color and sea surface temperature, land surface kinetic temperature, vegetation indices, leaf area index, land cover, snow cover, sea ice extent, cloud mask, atmospheric profiles, aerosol properties, and many other geophysical parameters. MODIS data are stored in HDF- EOS format in both swath format and in several different map projections. This tutorial guides users through data set characteristics as well as search and order interfaces, data unpacking, data subsetting, and potential applications of the data. A CD-ROM with sample data sets, and software tools for working with the data will be provided to the course participants.

CCRS Landcover Maps From Satellite Data

DOE Data Explorer

Trishchenko, Alexander

2008-01-15

The Canadian Centre for Remote Sensing (CCRS) presents several landcover maps over the SGP CART site area (32-40N, 92-102W) derived from satellite data including AVHRR, MODIS, SPOT vegetation data, and Landsat satellite TM imagery.

Retrieval of Aerosol Properties from MODIS Terra, MODIS Aqua, and VIIRS SNPP: Calibration Focus

NASA Technical Reports Server (NTRS)

Levy, Robert C.; Mattoo, Shana; Sawyer, Virginia; Kleidman, Richard; Patadia, Falguni; Zhou, Yaping; Gupta, Pawan; Shi, Yingxi; Remer, Lorraine; Holz, Robert

2016-01-01

MODIS-DT Collection 6 - Aqua/Terra level 2, 3; entire record processed - "Trending" issues reduced - Still a 15% or 0.02 Terra vs Aqua offset. - Terra/Aqua convergence improved with C6+, but bias remains. - Other calibration efforts yield mixed results. VIIRS-­-DT in development - VIIRS is similar, yet different then MODIS - With 50% wider swath, VIIRS has daily coverage - Ensures algorithm consistency with MODIS. - Currently: 20% NPP vs Aqua offset over ocean. - Only small bias (%) over land (2012-­-2016) - Can VIIRS/MODIS create aerosol CDR? Calibration for MODIS - VIIRS continues to fundamentally important. It's not just Terra, or just Aqua, or just NPP-­-VIIRS, I really want to push synergistic calibration.

Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis

Treesearch

Richard Tran Mills; Jitendra Kumar; Forrest M. Hoffman; William W. Hargrove; Joseph P. Spruce; Steven P. Norman

2013-01-01

We investigated the use of principal components analysis (PCA) to visualize dominant patterns and identify anomalies in a multi-year land surface phenology data set (231 m × 231 m normalized difference vegetation index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)) used for detecting threats to forest health in the conterminous...

Estimation on rubber tree disturbance caused by typhoon Damery (200518) with Landsat and MODIS data in Hainan Island of China

NASA Astrophysics Data System (ADS)

Tan, Chenyan; Fang, Weihua; Li, Jian

2016-04-01

In 2005, Typhoon Damery (200518) caused severe damage to the rubber trees in Hainan Island with its destructive winds and rainfall. Selection of proper vegetation indices using multi-source remote sensing data is critical to the assessment of forest disturbance and damage loss for this event. In this study, we will compare the performance of seven vegetation indices derived from MODIS and Landsat TM imageries prior to and after typhoon Damery, in order to select an optimal index for identifying rubber tree disturbance. The indices to be compared are normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII), Enhanced vegetation index (EVI), Leaf area index (LAI), forest z-score (IFZ), and Disturbance Index (DI). The ground truth data of rubber tree damage collected through field investigation was used to verify and compare the results. Our preliminary result for the area with ground-truth data shows that DI has the most significant performance for disturbance detection for this typhoon event. This index DI is then applied to all the areas in Hainan Island hit by Darmey to evaluate the overall forest damage severity. At last, rubber tree damage severity is analyzed with other typhoon hazard factors such as wind, topography, soil and precipitation.

Daily MODIS data trends of hurricane-induced forest impact and early recovery

USGS Publications Warehouse

Ramsey, Elijah W.; Spruce, Joseph; Rangoonwala, Amina; Suzuoki, Yukihiro; Smoot, James; Gasser, Jerry; Bannister, Terri

2011-01-01

We studied the use of daily satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors to assess wetland forest damage and recovery from Hurricane Katrina (29 August 2005 landfall). Processed MODIS daily vegetation index (VI) trends were consistent with previously determined impact and recovery patterns provided by the "snapshot" 25 m Landsat Thematic Mapper optical and RADARSAT-1 synthetic aperture radar satellite data. Phenological trends showed high 2004 and 2005 pre-hurricane temporal correspondence within bottomland hardwood forest communities, except during spring green-up, and temporal dissimilarity between these hardwoods and nearby cypress-tupelo swamp forests (Taxodium distichum [baldcypress] and Nyssa aquatica [water tupelo]). MODIS VI trend analyses established that one year after impact, cypress-tupelo and lightly impacted hardwood forests had recovered to near pre-hurricane conditions. In contrast, canopy recovery lagged in the moderately and severely damaged hardwood forests, possibly reflecting regeneration of pre-hurricane species and stand-level replacement by invasive trees.

Towards Continuity in Cloud Properties from MODIS and Suomi-NPP Polar-Orbiting Sensors

NASA Astrophysics Data System (ADS)

Baum, B. A.; Menzel, P.; Gladkova, I.; Heidinger, A. K.

2015-12-01

The intent of this talk is to discuss the progress and issues involved with developing a continuous record of cloud properties since 1978, beginning with the High Resolution Infrared Radiation Sounder (HIRS), then MODIS on the NASA Terra/Aqua platforms, and into the future from merged CrIS and VIIRS data. The MODIS measurements include infrared (IR) window radiances at 8.5-, 11- and 12-μm and four 15-μm channels in the broad CO2 absorption band. Cloud top pressure/height and emissivity are derived using a technique in which the strength is in retrievals for mid-to-high clouds but less so for low clouds where there is little thermal contrast with the surface. Additionally, MODIS provides a decadal IR cloud phase product. The goal now is to extend this continuity from HIRS and MODIS to the S-NPP era. However, there is one large drawback to consider: VIIRS has no infrared (IR) absorption channels. The lack of at least one IR absorption channel on VIIRS degrades the accuracy of the cloud properties. There is a solution: we can construct a 13.3-μm channel from a combination of VIIRS and CrIS (Cross-track Infrared Sounder). The approach involves using the high spatial resolution VIIRS IR window channels in combination with a lower spatial resolution 13.3-μm channel derived using CrIS high spectral resolution measurements. The result is a 13.3-μm pseudo-channel at the VIIRS pixel spatial resolution of 750 m (i.e., M-band resolution). The radiometric accuracy of this approach was tested using MODIS and AIRS, and found to be within 1-2%. The availability of the pseudo-channel increases the potential for achieving continuity between MODIS and S-NPP. Since future platforms will likely continue with a pairing of an imager and hyperspectral sounder, this work lays a foundation for future cloud product continuity. We will show how the use of this new channel will impact the cloud height and phase products.

MODIS Land Data Products: Generation, Quality Assurance and Validation

NASA Technical Reports Server (NTRS)

Masuoka, Edward; Wolfe, Robert; Morisette, Jeffery; Sinno, Scott; Teague, Michael; Saleous, Nazmi; Devadiga, Sadashiva; Justice, Christopher; Nickeson, Jaime

2008-01-01

The Moderate Resolution Imaging Spectrometer (MODIS) on-board NASA's Earth Observing System (EOS) Terra and Aqua Satellites are key instruments for providing data on global land, atmosphere, and ocean dynamics. Derived MODIS land, atmosphere and ocean products are central to NASA's mission to monitor and understand the Earth system. NASA has developed and generated on a systematic basis a suite of MODIS products starting with the first Terra MODIS data sensed February 22, 2000 and continuing with the first MODIS-Aqua data sensed July 2, 2002. The MODIS Land products are divided into three product suites: radiation budget products, ecosystem products, and land cover characterization products. The production and distribution of the MODIS Land products are described, from initial software delivery by the MODIS Land Science Team, to operational product generation and quality assurance, delivery to EOS archival and distribution centers, and product accuracy assessment and validation. Progress and lessons learned since the first MODIS data were in early 2000 are described.

Using normalized difference vegetation index to estimate carbon fluxes from small rotationally grazed pastures

USGS Publications Warehouse

Skinner, R.H.; Wylie, B.K.; Gilmanov, T.G.

2011-01-01

Satellite-based normalized difference vegetation index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rotationally-grazed pastures in the northeastern United States might be limited because paddock size is often smaller than the resolution limits of the satellite image. This research compared NDVI data from satellites with data obtained using a ground-based system capable of fine-scale (submeter) NDVI measurements. Gross primary productivity was measured by eddy covariance on two pastures in central Pennsylvania from 2003 to 2008. Weekly 250-m resolution satellite NDVI estimates were also obtained for each pasture from the moderate resolution imaging spectroradiometer (MODIS) sensor. Ground-based NDVI data were periodically collected in 2006, 2007, and 2008 from one of the two pastures. Multiple-regression and regression-tree estimates of GPP, based primarily on MODIS 7-d NDVI and on-site measurements of photosynthetically active radiation (PAR), were generally able to predict growing-season GPP to within an average of 3% of measured values. The exception was drought years when estimated and measured GPP differed from each other by 11 to 13%. Ground-based measurements improved the ability of vegetation indices to capture short-term grazing management effects on GPP. However, the eMODIS product appeared to be adequate for regional GPP estimates where total growing-season GPP across a wide area would be of greater interest than short-term management-induced changes in GPP at individual sites.

Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index

PubMed Central

Wang, Cong; Li, Jing; Wu, Shanlong; Xia, Chuanfu

2017-01-01

Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands. PMID:28867773

Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and Precipitation Observations

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Njoku, E. G.; Colliander, A.

2016-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE and precipitation measurements from GPCP to delineate and characterize drought and water supply pattern and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply and have important implications for water resource management. We use these data to investigate the supply changes from different water components in relation to satellite based vegetation productivity metrics from MODIS, before, during and following the major drought events observed in the continental US during the past 13 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, and vegetation productivity. In Texas and surrounding semi-arid areas, we find that the spatial pattern of the vegetation-moisture relation follows the gradient in mean annual precipitation. In Texas, GRACE TWS and surface SM show strong coupling and similar characteristic time scale in relatively normal years, while during the 2011 onward hydrological drought, GRACE TWS manifests a longer time scale than that of surface SM, implying stronger drought persistence in deeper water storage. In the Missouri watershed, we find a spatially varying vegetation-moisture relationship where in the drier northwestern portion of the basin, the inter-annual variability in summer vegetation productivity is closely associated with changes in carry-on GRACE TWS from spring, whereas in the moist southeastern portion of the basin, summer precipitation is the dominant controlling factor on vegetation growth.

Monitoring and Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and In-situ Observations.

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.; Colliander, A.; Njoku, E. G.

2015-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE, in-situ groundwater measurements and atmospheric moisture data to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depth in relation to satellite based vegetation metrics, including vegetation greenness (NDVI) measures from MODIS and related higher order productivity (GPP) before, during and following the major drought events observed in the continental US for the past 14 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, NDVI and GPP. We study how changes in atmosphere moisture stress and coupling of water storage components at different depth impact on the spatial and temporal correlation between TWS, SM and vegetation metrics. In Texas, we find that surface SM and GRACE TWS agree with each other in general, and both capture the underlying water supply constraints to vegetation growth. Triggered by a transit increase in precipitation following the 2011 hydrological drought, vegetation productivity in Texas shows more sensitivity to surface SM than TWS. In the Great Plains, the correspondence between TWS and vegetation productivity is modulated by temperature-induced atmosphere moisture stress and by the coupling between surface soil moisture and groundwater through irrigation.

Results and Lessons from a Decade of Terra MODIS On-Orbit Spectral Characterization

NASA Technical Reports Server (NTRS)

Xiong, X.; Choi, T.; Che, N.; Wang, Z.; Dodd, J.

2010-01-01

Since its launch in December 1999, the NASA EOS Terra MODIS has successfully operated for more than a decade. MODIS makes observations in 36 spectral bands from visible (VIS) to longwave infrared (LWIR) and at three nadir spatial resolutions: 250m (2 bands), 500m (5 bands), and 1km (29 bands). In addition to its on-board calibrators designed for the radiometric calibration, MODIS was built with a unique device, called the spectro-radiometric calibration assembly (SRCA). It can be configured in three different modes: radiometric, spatial, and spectral. When it is operated in the spectral modes, the SRCA can monitor changes in Sensor spectral performance for the VIS and near-infrared (NIR) spectral bands. For more than 10 years, the SRCA operation has continued to provide valuable information for MODIS on-orbit spectral performance. This paper briefly describes SRCA on-orbit operation and calibration activities; it presents decade-long spectral characterization results for Terra MODIS VIS and NIR spectral bands in terms of chances in their center wavelengths (CW) and bandwidths (BW). It is shown that the SRCA on-orbit wavelength calibration capability remains satisfactory. For most spectral bands, the changes in CW and BW are less than 0.5 and 1 nm, respectively. Results and lessons from Terra MODIS on-orbit spectral characterization have and will continue to benefit its successor, Aqua MODIS, and other future missions.

Comparison of Sub-pixel Classification Approaches for Crop-specific Mapping

EPA Science Inventory

The Moderate Resolution Imaging Spectroradiometer (MODIS) data has been increasingly used for crop mapping and other agricultural applications. Phenology-based classification approaches using the NDVI (Normalized Difference Vegetation Index) 16-day composite (250 m) data product...

Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra

PubMed Central

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-01-01

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors. PMID:24287529

Comparability of red/near-infrared reflectance and NDVI based on the spectral response function between MODIS and 30 other satellite sensors using rice canopy spectra.

PubMed

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-11-26

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from -12.67% to 36.30% for the red reflectance, -8.52% to -0.23% for the NIR reflectance, and -9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.

Predicting the Invasion Potential of a Puerto Rican Frog in Hawaii using MODIS Satellite Imagery

NASA Astrophysics Data System (ADS)

Bisrat, S. A.; White, M. A.

2008-12-01

The Puerto Rican coqui frog (Eleutherodactylus coqui, hereafter coqui), which was introduced into Hawaii accidentally via commercial nurseries, is an aggressive invasive species in Hawaii. The coqui threatens Hawaii's unique ecological communities because it predates upon endemic invertebrates, which comprise the large majority of Hawaii's endemic fauna. Coqui frogs also affect real estate valuations because of their loud mating calls. Despite this widespread problem, the potential coqui range in Hawaii is currently unknown, making control and management efforts difficult. We fitted linear discriminant analysis (LDA), logistic regression (LR) via generalized linear models (GLMs), generalized additive models (GAMs), classification trees (CTs), random forests (RF), and support vector machine (SVM) to model the species distribution and map their invasion potential. We used five MODIS satellite imagery-derived biophysical variables as explanatory variables: leaf area index (LAI), fraction of photosynthetically active radiation absorbed by vegetation (FPAR), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and land surface temperature (LST) from three MODIS products: MOD11 (LST), MOD13 (LAI and FPAR), and MOD15 (Vegetation Index) (collection 4). We used 2000-2005 MODIS data from Aqua and Terra satellites to generate monthly climatologies for each biophysical variable. We collected presence/absence data from Puerto Rico and Hawaii using a 1 km grid overlaid over the entire islands of Puerto Rico and the Island of Hawaii by sampling every other pixel of the grid intersecting with the road network. We then used the dataset from Puerto Rico to train the six models while the Hawaii dataset was used as a test set. All six models predicted the invasion potential of coqui frogs in Hawaii with a moderate success with mean Kappa value of 0.31, mean area under the curve of receiver operating characteristics (AUC) of 0.75 and mean classification accuracy (CA) of 0.69. RF and SVM outperformed the other classifiers with Kappa value of 0.4, AUC value of 0.79 and CA of 0.71 for RF and Kappa value of 0.4, AUC value of 0.71 and CA value of 0.72 for SVM. These results suggest climate matching between the native and the introduced habitats of coqui frogs is not strong. Further, the results suggest coqui frogs in their introduced habitat are not showing strong niche conservation.

Assessment of the short-term radiometric stability between Terra MODIS and Landsat 7 ETM+ sensors

USGS Publications Warehouse

Choi, Taeyoung; Xiong, Xiaoxiong; Chander, Gyanesh; Angal, A.

2009-01-01

Short-term radiometric stability was evaluated using continuous ETM+ scenes within a single orbit (contact period) and the corresponding MODIS scenes for the four matching solar reflective visible and near-infrared (VNIR) band pairs between the two sensors. The near-simultaneous earth observations were limited by the smaller swath size of ETM+ (183 km) compared to MODIS (2330 km). Two sets of continuous granules for Terra MODIS and Landsat 7 ETM+ were selected and mosaicked based on pixel geolocation information for noncloudy pixels over the African continent. The matching pixel pairs were resampled from a fine to a coarse pixel resolution, and the at-sensor spectral radiance values for a wide dynamic range of the sensors were compared and analyzed, covering various surface types. The following study focuses on radiometric stability analysis from the VNIR band-pairs of ETM+ and MODIS. The Libya-4 desert target was included in the path of this continuous orbit, which served as a verification point between the short-term and the long-term trending results from previous studies. MODTRAN at-sensor spectral radiance simulation is included for a representative desert surface type to evaluate the consistency of the results.

Response of alpine vegetation growth dynamics to snow cover phenology on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wang, X.; Wu, C.

2017-12-01

Alpine vegetation plays a crucial role in global energy cycles with snow cover, an essential component of alpine land cover showing high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000-2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among plant functional types, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidences between vegetation growth and snow cover phenology, and changes in snow cover should be also considered when analyzing alpine vegetation growth dynamics in future.

Clear-sky narrowband albedos derived from VIRS and MODIS

NASA Astrophysics Data System (ADS)

Sun-Mack, Sunny; Minnis, Patrick; Chen, Yan; Arduini, Robert F.

2004-02-01

The Clouds and Earth"s Radiant Energy System (CERES) project is using multispectral imagers, the Visible Infrared Scanner (VIRS) on the tropical Rainfall Measuring Mission (TRMM) satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra, operating since spring 2000, and Aqua, operating since summer 2002, to provide cloud and clear-sky properties at various wavelengths. This paper presents the preliminary results of an analysis of the CERES clear-sky reflectances to derive a set top-of-atmosphere clear sky albedo for 0.65, 0.86, 1.6, 2.13 μm, for all major surface types using the combined MODIS and VIRS datasets. The variability of snow albedo with surface type is examined using MODIS data. Snow albedo was found to depend on the vertical structure of the vegetation. At visible wavelengths, it is least for forested areas and greatest for smooth desert and tundra surfaces. At 1.6 and 2.1-μm, the snow albedos are relatively insensitive to the underlying surface because snow decreases the reflectance. Additional analyses using all of the MODIS results will provide albedo models that should be valuable for many remote sensing, simulation and radiation budget studies.

Linking Landsat observations with MODIS derived Land Surface Phenology data to map agricultural expansion and contraction in Russia

NASA Astrophysics Data System (ADS)

Caliskan, S.; de Beurs, K.

2010-12-01

Direct human impacts on the land surface are especially pronounced in agricultural regions that cover a substantial portion of the global land surface: 12% of the terrestrial surface is under active agricultural management. Crops display phenologies distinct from natural vegetation; the growing seasons are often shifted in time, crop establishment is generally fast and the vegetation is rapidly removed at harvest. Previously we have demonstrated that agricultural land abandonment alters land surface phenology sufficiently to be detectable from a time series of coarse resolution imagery. With land surface phenology models based on accumulated growing degree-days (AGDD) and AVHRR NDVI, we demonstrated that abandoned croplands covered with native grasses and weeds typically greened-up and peaked sooner than active croplands. Here we present an expansion of these analyses for the MODIS time period with the ultimate goal to map agricultural abandonment and expansion in European Russia from 2000 to 2010. We used the 8-day, 1km L3 Land Surface Temperature data (MOD11A2) to generate the accumulated growing degree days and the 16-day L3 Nadir BRDF-Adjusted reflectance data at 500m resolution (MCD43A4) to calculate NDVI. We calculated phenological metrics based on three methods: 1) Double-logistic models such as those applied to produce the standard MODIS phenology product (MOD12Q2); 2) A combination of NDII and NDVI; this method has been shown to provide start/end of season measurement closest to field observations in snowy areas; and 3) A quadratic model linking accumulated growing degree days and vegetation indices which we successfully applied in agricultural areas of Kazakhstan and semi-arid Africa. We selected Landsat imagery for two vastly different regions in Russia and present a Landsat-guided probabilistic detection of abandoned and active croplands for all available years of the MODIS image time series (2000-2010). For each region, we selected at least two images during the growing season and calculated the following indices: Normalized Difference Vegetation Index (NDVI), Tasseled Cap indices (Brightness, Greenness, Wetness), as well as the first three principal components for each image. We used the selected images to distinguish between the basic classes of agriculture, water, forest and urban areas, with the primary goal to separate between agricultural and non-agricultural regions. We compared class membership with ancillary regional agricultural statistics and targeted field observations collected in the summer of 2010. In the last part, we linked the Landsat based agricultural estimates and the MODIS phenological measurements using logistic regression and compared the agricultural maps with globally available land cover classifications.

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.

A simple estimate of ecosystem respiration across biomes based on MODIS products

NASA Astrophysics Data System (ADS)

Jaegermeyr, J.; Hostert, P.; Lucht, W.

2010-12-01

Beside carbon sequestration by terrestrial photosynthesis, in particular the subsequent carbon release by ecosystem respiration (Reco) is a crucial flux for estimating carbon budgets. Heterotrophic soil decomposition rates (Rh) and autotrophic respiration rates (Ra), which add up to Reco, are highly sensitive to environmental conditions and in some cases they determine net ecosystem productivity. Prior respiration modeling approaches revealed that a precise process-based and bottom-up modeling is important for realistic estimates. On a short timescale, as in the case of satellite environmental monitoring, simplified empirical models are not necessarily less accurate, though. For most major biomes, ecosystem carbon efflux is predominantly driven by air temperature. It can further be limited by water stress, plant activity and substrate quality. Developing simple, empirical and wall-to-wall respiration models from continuous Moderate Resolution Imaging Spectroradiometer (MODIS) land products on a continental scale can enhance our understanding of spatially explicit respiration patterns. We therefore accept model uncertainties by simplifying decay and respiratory processes in that we account for a single static carbon pool and do not include any feedback mechanisms. Preliminary results suggest that the 8-day MODIS 1km land surface temperature product (LST) and the vegetation-water index (NDWI) derived from the 8-day MODIS 500m surface reflectance product are sufficient to largely explain the variability of Reco. Spatial flux variations can be attributed to plant activity variation. We therefore introduce a site-specific, maximum leaf area index (LAI) from the MODIS 1km LAI product as a proxy. A biome-specific model parameterization and validation is performed, based on 8-day composite FLUXNET tower data representing major global biomes. We found that the frequently used temperature model by Loyd and Taylor (1994) does not show superior performance on 8-day ecosystem respiration data. The model by Del Grosso et al. (2005) is more flexible to account for lower Q10 values at high temperatures and thus it is used to describe the temperature dependency here. Although we cannot explain flux variations arising from overall carbon pool variations, results suggest that our approach may contribute to simplified Reco estimates.

Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region.

PubMed

Song, Youngkeun; Njoroge, John B; Morimoto, Yukihiro

2013-05-01

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E ~ 1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.

A multidisciplinary and multi-sensor assessment of continuous degassing at Turrialba volcano, Costa Rica; insights and their application to hazard management

NASA Astrophysics Data System (ADS)

van Manen, S. M.; Tortini, R.; Burson, B.; Carn, S. A.

2013-12-01

Turrialba is an active stratovolcano located in the Central Cordillera of Costa Rica with an elevation of 3,340 m. Located just 35 km northeast of Costa Rica's capital city San Jose it looms over Costa Rica's Central Valley, the social and economic hub of the country. After more than 100 years of quiescence Turrialba resumed activity in 1996, marked by progressive increases in degassing and seismic activity with gas emissions becoming continuous in 2007. Intermittent phreatic explosions accompanied by ash emissions that have reached the capital have been occurring since 2010. The activity has resulted in the evacuation of two villages, closure of the National Park that comprises the summit region of the volcano and devastation of the local ecosystem. In this work we present a multi-disciplinary and multi-sensor assessment of the persistent degassing and its impacts on the local ecosystem. Combining a variety of high temporal and high spatial resolution satellite-based time series with ground-based measurements of ambient gas concentrations, element deposition and surveys of species richness, enables a comprehensive assessment of SO2 emissions and changes in vegetation. Satellite-based time-series were obtained from Landsat TM and ETM+, Terra ASTER and MODIS, Aqua MODIS, EO-1 and Aura OMI, with some of the data dating back to 2000. Preliminary results show exposure to the volcanic plume results in high soil acidity and significant uptake of certain heavy metals (e.g. Cd, Co, Cu, Hg and Pb) by vegetation, in contrast other elements such as Ba, Ca and Sr are leached from the soil as a result of the acid deposition. These factors are likely to be responsible for decreased species richness and physiological damage observed downwind of Turrialba. Ambient SO2 concentrations that exceed WHO guideline values have been recorded, which has potentially important consequences for human health in the area. Analyzing and relating the remote observations to conditions and impacts on the ground provides an increased understanding of volcanic degassing, its impacts in terms of the long-term vegetation response and how satellite-based monitoring can be used to inform hazard management strategies related to land use, agricultural productivity and human health in near-real time.

Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake

NASA Astrophysics Data System (ADS)

Cao, Zhigang; Duan, Hongtao; Shen, Ming; Ma, Ronghua; Xue, Kun; Liu, Dong; Xiao, Qitao

2018-02-01

Inland lakes are generally an important source of drinking water, and information on their water quality needs to be obtained in real time. To date, Moderate-resolution imaging spectroradiometer (MODIS) data have played a critical, effective and long-term role in fulfilling this function. However, the MODIS instruments on board both the Terra and Aqua satellites have operated beyond their designed five-year mission lifespans (Terra was launched in 1999, whereas Aqua was launched in 2002), and these instruments may stop running at any time in the near future. The Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership (Suomi NPP, which was launched in Oct 2011) is expected to provide a consistent, long-term data record and continue the series of observations initiated by MODIS. To date, few evaluations of the consistency between VIIRS and MODIS have been conducted for turbid inland waters. In this study, we first used synchronous MODIS/Aqua and VIIRS/NPP data (±1 h) collected during 2012-2015 to evaluate the consistency of Rayleigh-corrected reflectance (Rrc) observations over Lake Hongze (the fourth-largest freshwater lake in China), since accurate remote sensing reflectance (Rrs) values cannot be acquired over turbid inland waters. Second, we used recently developed algorithms based on Rrc in the red band to estimate the concentrations of suspended particulate matter (SPM) from MODIS/Aqua and VIIRS/NPP data. Finally, we assessed the consistency of the SPM products derived from MODIS/Aqua and VIIRS/NPP. The results show the following. (1) The differences in Rrc among the green (VIIRS 551 nm and MODIS 555 nm) and red bands (VIIRS 671 nm and MODIS 645 nm) indicate a satisfactory consistency, and the unbiased percentage difference (UPD) is <12%. Meanwhile, the results for the near infrared (NIR) band (MODIS 859 nm and VIIRS 862 nm) indicate relatively large differences (UPD = 21.84%). (2) The satellite-derived SPM products obtained using MODIS/Aqua and VIIRS/NPP have a satisfactory degree of consistency (0-150 mg/L SPM: R2 = 0.81, UPD < 16% and 0-80 mg/L SPM: R2 = 0.85, UPD < 12%, respectively). These results demonstrate that VIIRS/NPP can continue to record the SPM observations initiated by MODIS/Aqua for turbid inland waters and establish environmental datasets over long time periods to support water quality management endeavors.

MODIS-Derived Terrestrial Primary Production

NASA Astrophysics Data System (ADS)

Zhao, Maosheng; Running, Steven; Heinsch, Faith Ann; Nemani, Ramakrishna

Temporal and spatial changes in terrestrial biological productivity have a large impact on humankind because terrestrial ecosystems not only create environments suitable for human habitation, but also provide materials essential for survival, such as food, fiber and fuel. A recent study estimated that consumption of terrestrial net primary production (NPP; a list of all the acronyms is available in the appendix at the end of the chapter) by the human population accounts for about 14-26% of global NPP (Imhoff et al. 2004). Rapid global climate change is induced by increased atmospheric greenhouse gas concentration, especially CO2, which results from human activities such as fossil fuel combustion and deforestation. This directly impacts terrestrial NPP, which continues to change in both space and time (Melillo et al. 1993; Prentice et al. 2001; Nemani et al. 2003), and ultimately impacts the well-being of human society (Milesi et al. 2005). Additionally, substantial evidence show that the oceans and the biosphere, especially terrestrial ecosystems, currently play a major role in reducing the rate of the atmospheric CO2 increase (Prentice et al. 2001; Schimel et al. 2001). NPP is the first step needed to quantify the amount of atmospheric carbon fixed by plants and accumulated as biomass. Continuous and accurate measurements of terrestrial NPP at the global scale are possible using satellite data. Since early 2000, for the first time, the MODIS sensors onboard the Terra and Aqua satellites, have operationally provided scientists with near real-time global terrestrial gross primary production (GPP) and net photosynthesis (PsnNet) data. These data are provided at 1 km spatial resolution and an 8-day interval, and annual NPP covers 109,782,756 km2 of vegetated land. These GPP, PsnNet and NPP products are collectively known as MOD17 and are part of a larger suite of MODIS land products (Justice et al. 2002), one of the core Earth System or Climate Data Records (ESDR or CDR).

Linkages between Land Surface Phenology Metrics and Natural and Anthropogenic Events in Drylands (Invited)

NASA Astrophysics Data System (ADS)

de Beurs, K.; Brown, M. E.; Ahram, A.; Walker, J.; Henebry, G. M.

2013-12-01

Tracking vegetation dynamics across landscapes using remote sensing, or 'land surface phenology,' is a key mechanism that allows us to understand ecosystem changes. Land surface phenology models rely on vegetation information from remote sensing, such as the datasets derived from the Advanced Very High Resolution Radiometer (AVHRR), the newer MODIS sensors on Aqua and Terra, and sometimes the higher spatial resolution Landsat data. Vegetation index data can aid in the assessment of variables such as the start of season, growing season length and overall growing season productivity. In this talk we use Landsat, MODIS and AVHRR data and derive growing season metrics based on land surface phenology models that couple vegetation indices with satellite derived accumulated growing degreeday and evapotranspiration estimates. We calculate the timing and the height of the peak of the growing season and discuss the linkage of these land surface phenology metrics with natural and anthropogenic changes on the ground in dryland ecosystems. First we will discuss how the land surface phenology metrics link with annual and interannual price fluctuations in 229 markets distributed over Africa. Our results show that there is a significant correlation between the peak height of the growing season and price increases for markets in countries such as Nigeria, Somalia and Niger. We then demonstrate how land surface phenology metrics can improve models of post-conflict resolution in global drylands. We link the Uppsala Conflict Data Program's dataset of political, economic and social factors involved in civil war termination with an NDVI derived phenology metric and the Palmer Drought Severity Index (PDSI). An analysis of 89 individual conflicts in 42 dryland countries (totaling 892 individual country-years of data between 1982 and 2005) revealed that, even accounting for economic and political factors, countries that have higher NDVI growth following conflict have a lower risk of reverting to civil war. Finally, the patchy and heterogeneous arrangement of vegetation in dryland areas sometimes complicates the extraction of phenological signals using existing remote sensing data. We conclude by demonstrating how the phenological analysis of a range of dryland land cover classes benefits from the availability of synthetic images at Landsat spatial resolution and MODIS time intervals.

Statistical intercomparison and validation of multisensory aerosol optical depth retrievals over three AERONET sites in Kenya, East Africa

NASA Astrophysics Data System (ADS)

Boiyo, Richard; Kumar, K. Raghavendra; Zhao, Tianliang

2017-11-01

Over the last two decades, a number of space-borne sensors have been used to retrieve aerosol optical depth (AOD). The reliability of these datasets over East Africa (EA), however, is an important issue in the interpretation of regional aerosol variability. This study provides an intercomparison and validation of AOD retrievals from the MODIS-Terra (DT and DB), MISR and OMI sensors against ground-based measurements from the AERONET over three sites (CRPSM_Malindi, Nairobi, and ICIPE_Mbita) in Kenya, EA during the periods 2008-2013, 2005-2009 and 2006-2015, respectively. The analysis revealed that MISR performed better over the three sites with about 82.5% of paired AOD data falling within the error envelope (EE). MODIS-DT showed good agreement against AERONET with 59.05% of paired AOD falling within the sensor EE over terrestrial surfaces with relatively high vegetation cover. The comparison between MODIS-DB and AERONET revealed an overall lower performance with lower Gfraction (48.93%) and lower correlation r = 0.58; while AOD retrieved from OMI showed less correspondence with AERONET data with lower Gfraction (68.89%) and lowest correlation r = 0.31. The monthly evaluation of AODs retrieved from the sensors against AERONET AOD indicates that MODIS-DT has the best performance over the three sites with highest correlation (0.71-0.84), lowest RMSE and spread closer to the AERONET. Regarding seasonal analysis, MISR performed well during most seasons over Nairobi and Mbita; while MODIS-DT performed better than all other sensors during most seasons over Malindi. Furthermore, the best seasonal performance of most sensors relative to AERONET data occurred during June-August (JJA) attributed to modulations induced by a precipitation-vegetation factor to AOD satellite retrieval algorithms. The study revealed the strength and weakness of each of the retrieval algorithm and forms the basis for further research on the validation of satellite retrieved aerosol products over EA.

Generating a Long-Term Land Data Record from the AVHRR and MODIS Instruments

NASA Technical Reports Server (NTRS)

Pedelty, Jeffrey; Devadiga, Sadashiva; Masuoka, Edward; Brown, Molly; Pinzon, Jorge; Tucker, Compton; Vermote, Eric; Prince, Stephen; Nagol, Jyotheshwar; Justice, Christopher;

A Web Architecture to Geographically Interrogate CHIRPS Rainfall and eMODIS NDVI for Land Use Change

NASA Technical Reports Server (NTRS)

Burks, Jason E.; Limaye, Ashutosh

2014-01-01

Monitoring of rainfall and vegetation over the continent of Africa is important for assessing the status of crop health and agriculture, along with long-term changes in land use change. These issues can be addressed through examination of long-term precipitation (rainfall) data sets and remote sensing of land surface vegetation and land use types. Two products have been used previously to address these goals: the Climate Hazard Group Infrared Precipitation with Stations (CHIRPS) rainfall data, and multi-day composites of Normalized Difference Vegetation Index (NDVI) from the USGS eMODIS product. Combined, these are very large data sets that require unique tools and architecture to facilitate a variety of data analysis methods or data exploration by the end user community. To address these needs, a web-enabled system has been developed to allow end-users to interrogate CHIRPS rainfall and eMODIS NDVI data over the continent of Africa. The architecture allows end-users to use custom defined geometries, or the use of predefined political boundaries in their interrogation of the data. The massive amount of data interrogated by the system allows the end-users with only a web browser to extract vital information in order to investigate land use change and its causes. The system can be used to generate daily, monthly and yearly averages over a geographical area and range of dates of interest to the user. It also provides analysis of trends in precipitation or vegetation change for times of interest. The data provided back to the end-user is displayed in graphical form and can be exported for use in other, external tools. The development of this tool has significantly decreased the investment and requirements for end-users to use these two important datasets, while also allowing the flexibility to the end-user to limit the search to the area of interest.

Monitoring Urbanization-Related Land Cover Change on the U.S. Great Plains and Impacts on Remotely Sensed Vegetation Dynamics

NASA Astrophysics Data System (ADS)

Krehbiel, C. P.; Jackson, T.; Henebry, G. M.

2014-12-01

Earth is currently in an era of rapid urban growth with >50% of global population living in urban areas. Urbanization occurs alongside urban population growth, as cities expand to meet the demands of increasing population. Consequently, there is a need for remote sensing research to detect, monitor, and measure urbanization and its impacts on the biosphere. Here we used MODIS and Landsat data products to (1) detect urbanization-related land cover changes, (2) investigate urbanization-related impacts on land surface phenology (LSP) across rural to urban gradients and (3) explore fractional vegetation and impervious surface area regionally across the US Great Plains and within 14 cities in this region. We used the NLCD Percent Impervious Surface Area (%ISA) and Land Cover Type (LCT) products from 2001, 2006, and 2011 for 30m classification of the peri-urban environment. We investigated the impacts of urbanization-related land cover change on urban LSP at 30m resolution using the NDVI product from Web Enabled Landsat Data (http://weld.cr.usgs.gov) with accumulated growing degree-days calculated from first-order weather stations. We fitted convex quadratic LSP models to a decade (2003-2012) of observations to yield these phenometrics: modeled peak NDVI, time (thermal and calendar) to modeled peak, duration of season (DOS), and model fit. We compared our results to NDVI from MODIS NBAR (500m) and we explored the utility of 4 μm radiance (MODIS band 23) at 1 km resolution to characterize fractional vegetation dynamics in and around urbanized areas. Across all 14 cities we found increases in urbanized area (>25 %ISA) exceeding 10% from 2001-2011. Using LSP phenometrics, we were able to detect changes from cropland to suburban LCTs. In general we found negative relationships between DOS and distance from city center. We found a distinct seasonal cycle of MIR radiance over cropland LCTs due to the spectral contrast between bare soils and green vegetation.

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

NASA Astrophysics Data System (ADS)

Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

2015-04-01

Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

Improving Access to MODIS Biophysical Science Products for NACP Investigators

NASA Technical Reports Server (NTRS)

Wolfe, Robert E.; Gao, Feng; Morisette, Jeffrey T.; Ederer, Gregory A.; Pedelty, Jeffrey A.

2007-01-01

MODIS 4 NACP is a NASA-funded project supporting the North American Carbon Program (NACP). The purpose of this Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) project is to provide researchers with Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical data products that are custom tailored for use in NACP model studies. Standard MODIS biophysical products provide used to improve our understanding on the climate and ecosystem changes. However, direct uses of the MODIS biophysical parameters are constrained by retrieval quality and cloud contamination. Another challenge that NACP users face is acquiring MODIS data in formats and at spatial-temporal resolutions consistent with other data sets they use. We have been working closely with key NACP users to tailor the MODIS products to fit their needs. First, we provide new temporally smoothed and spatially continuous MODIS biophysical data sets. Second, we are distributing MODIS data at suitable spatial-temporal resolutions and in formats consistent with other data integration into model studies.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Wildfires, smoke, and burn scars, near Yakutsk, Russia

NASA Technical Reports Server (NTRS)

2002-01-01

The Lena River in central Siberia is hidden beneath a veil of smoke from multiple wildfires burning around the city of Yakutsk, Russia. Fires have been burning in the region off and on since late May 2002, and may be agricultural in cause. This image was acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite on July 23, 2002. In the false=-color image, vegetation is bright green, smoke is blueish-white, and burned areas are reddish-brown. In both images, fire detections are marked with red outlines. Credit: Jacques Descloitres, MODIS Land Rapid Response Team, NASA/GSFC

Flooding of the Taz, Pur, and Yenisey Rivers, Russia

NASA Technical Reports Server (NTRS)

2002-01-01

Each spring and summer, rivers across Siberia experience flooding as the waters in the south begin to melt and run before the ice has retreated from the northern limits. The ice causes jams which are sometimes loosened up using explosives. This pair of MODIS images from June 18, 2002, shows flooding on the Pur (left), Taz (center), and Yenisey (right) Rivers in central Siberia. In the false-color image, ice and snow are red, clouds are white, water is black, and vegetation is green. Bare soil is brown. Credit: Jacques Descloitres, MODIS Land Rapid Response Team, NASA/GSFC

ncreasing altitudinal spring phenology gradient of vegetation over the last decade in Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

Shen, M.; Piao, S.

2013-12-01

Vegetation spring phenology in temperate and cold regions is widely expected to advance with temperature elevation and is often used as an indicator of regional climatic change. The Qinghai-Tibetan Plateau (QTP) has experienced intensive warming recently, but substantial contradictions exist about the changes of vegetation spring phenology. We investigated spatiotemporal variations in green-up dates in the QTP from 2000 to 2011 determined through five methods using four satellite-derived datasets including the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), Système Pour l'Observation de la Terre, and MODerate resolution Imaging Spectroradiometer (MODIS), and the enhanced vegetation index from MODIS. On regional scale, no significant temporal trends (all P > 0.05) were found in the green-up dates, consistently among all the vegetation indices and methods. This insignificance was resulted from the substantial spatial heterogeneity of trends in green-up date, with delay by greater than 0.5 day yr-1 in the southwest region, and extensive advance in the other areas, although the temperature elevation was region-wide. These changes doubled the altitudinal gradient of green-up date, from 0.63 day 100m-1 in the early 2000s to 1.30 days 100m-1 in the early 2010s. The delay in the southwest region and high altitudes was likely caused by the decline in spring precipitation, despite the increasing spring temperature. This study suggests that spring precipitation is an important regulator of phenological response to climatic warming in QTP, and that, even in cold region, delay of vegetation spring phenology does not necessarily indicate spring cooling. Besides, the phenological changes retrieved from the widely used AVHRR NDVI differed from those from the other 3 vegetation indices, necessitating the use of multi-datasets while monitoring vegetation dynamics from space.

Peat drainage conditions assessment in Scotland

NASA Astrophysics Data System (ADS)

Poggio, Laura; Artz, Rebekka; Donaldson-Selby, Gillian; Aitkenhead, Matt; Donnelly, David; Gimona, Alessandro

2017-04-01

Large areas of Scotland are covered in peat, providing an important sink of carbon but also a notable source of emission where peatlands are not in good condition. However, despite data from designated sites that peat degradation is common, a detailed spatial assessment of the condition of most peatlands across the whole of Scotland is missing. An assessment of peatland drainage was carried out at >600 random sampling locations with an expert-based estimation of presence or absence of drainage ditches within a 500 metre block using 25 cm resolution aerial imagery. The resulting dataset was modelled using a scorpan-kriging approach, in particular using Generalised Additive Models for the description of the trend. Remote sensing images from different sensors (i.e. MODIS, Landsat and Sentinel 1 and 2) were used. In particular we used indices describing vegetation greenness (Enhanced Vegetation Index), water availability (Normalised Water Difference index), Land Surface Temperature and vegetation productivity. When considering MODIS indices we used time series and phenological summaries. The model provides also uncertainty of the estimations. The derived dataset can then be used in the decision making process for the selection of sites for restoration, emissions estimation and accounting.

Drought in Southeastern United States

NASA Technical Reports Server (NTRS)

2007-01-01

May 2007 was a record-setting month in Georgia. Typically a dry month in this southern state, May 2007 was exceptionally so, with many locations setting record-low rainfall records and some receiving no rain at all, said state climatologist David Emory Stooksbury on GeorgiaDrought.org. The lack of rain slowed plant growth, as shown in this vegetation index image. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite collected the data used to make this image between May 9 and May 24, 2007. The image shows vegetation conditions compared to average conditions observed from 2000 through 2006. Areas in which plants are more sparse or are growing more slowly than average are brown, while better-than-average growth is green. Georgia and its neighbors (South Carolina, Alabama, and Florida) are all brown, an indication that the lack of rainfall is suppressing plant growth. The gray area in southern Georgia and northern Florida shows where MODIS could not collect valid vegetation measurements, either because of clouds or smoke. In this case, the area corresponds with land that burned during this period and was probably masked by smoke. NASA image created by Jesse Allen, Earth Observatory, using data provided by Inbal Reshef, Global Agricultural Monitoring Project.

Long-term of analysis of MODIS, NDVI and NDWI for the Mesopotamian Marshlands, Iraq.

NASA Astrophysics Data System (ADS)

Al barakat, R. H. R.; Lakshmi, V.

2016-12-01

The Mesopotamian marshlands are considered as a one of the most important wetlands in the world. During past decades, the marsh area has varied between 10,500 km² to 20,000 km² in flood seasons. These marshes are located in the Mesopotamain plain lying mostly within Southern Iraq and a portion of South western Iran, along Euphrates,Tigris and Shatt Al-Arab river which formed by the confluence of Tigris and Euphrates rivers. They are characterized by a good environment for various flora such as Phragmites australis and fauna. Through early 1990 to the present the marshes subjected to many changes such as water supply diversions that have dramatically impacted the ecosystem. By using a long-term values of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), between 2000 and 2016, we examined the annual changes during entire time series in both of the vegetation and water coverage in the three majar marshes; Al-Huwaizah marsh, the Central marshes and Al-Hammar marsh. The long-term has been divided into three periods (2000-2003, 2004-2008 and 2009-2016) based on ratios of coverage vegetation and water. The 1st period is characterized by low coverage in both vegetation and water due to human activities, which is represented by the construction of a large number of dams on the downstream of Tigris and Euphrates rivers during late 1980s until 2003. The 2nd period shows significantly increasing coverage of greater than 50% were the increases in the vegetation coverage of the original marsh areas. The 3rd period shows increases in the barren lands, while the water bodies and vegetation coverage are decreased. This variations are attributed to different effects. First, the marshes have received little water due to constructions of dams in the upstream countries, and they were completed during 3rd period 2009-2016. Second they occurred during a period of severe drought in the neighboring countries (upstream). Additional to that, this research aims to detect the environmental changes in the marshes by using multi-temporal and multi-spectral satellite images. The spatial resolution of the MODIS imagery is enhanced using Landsat data.

The Climate-Population Nexus in the East African Horn: Emerging Degradation Trends in Rangeland and Pastoral Livelihood Zones

NASA Astrophysics Data System (ADS)

Pricope, N. G.; Husak, G. J.; Funk, C. C.; Lopez-Carr, D.

2014-12-01

Increasing climate variability and extreme weather conditions along with declining trends in both rainfall and temperature represent major risk factors affecting agricultural production and food security in many regions of the world. We identify regions where significant rainfall decrease from 1979-2011 over the entire continent of Africa couples with significant human population density increase. The rangelands of Ethiopia, Kenya, and Somalia in the East African Horn remain one of the world's most food insecure regions, yet have significantly increasing human populations predominantly dependent on pastoralist and agro-pastoralist livelihoods. Vegetation in this region is characterized by a variable mosaic of land covers, generally dominated by grasslands necessary for agro-pastoralism, interspersed by woody vegetation. Recent assessments indicate that widespread degradation is occurring, adversely impacting fragile ecosystems and human livelihoods. Using two underutilized MODIS products, we observe significant changes in vegetation patterns and productivity over the last decade all across the East African Horn. We observe significant vegetation browning trends in areas experiencing drying precipitation trends in addition to increasing population pressures. We also found that the drying precipitation trends only partially statistically explain the vegetation browning trends, further indicating that other factors such as population pressures and land use changes are responsible for the observed declining vegetation health. Furthermore, we show that the general vegetation browning trends persist even during years with normal rainfall conditions such as 2012, indicating potential long-term degradation of rangelands on which approximately 10 million people depend. These findings have serious implications for current and future regional food security monitoring and forecasting as well as for mitigation and adaptation strategies in a region where population is expected to continue increasing against a backdrop of drying climate trends.

Recent Upgrades to NASA SPoRT Initialization Datasets for the Environmental Modeling System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Molthan, Andrew L.; Zavodsky, Bradley T.; Rozumalski, Robert A.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its National Weather Service (NWS) partners that can initialize specific fields for local model runs within the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS). In last year's NWA abstract on this topic, the suite of SPoRT products supported in the STRC EMS was presented, which includes a Sea Surface Temperature (SST) composite, a Great Lakes sea-ice extent, a Green Vegetation Fraction (GVF) composite, and NASA Land Information System (LIS) gridded output. This abstract and companion presentation describes recent upgrades made to the SST and GVF composites, as well as the real-time LIS runs. The Great Lakes sea-ice product is unchanged from 2011. The SPoRT SST composite product has been expanded geographically and as a result, the resolution has been coarsened from 1 km to 2 km to accommodate the larger domain. The expanded domain covers much of the northern hemisphere from eastern Asia to western Europe (0 N to 80 N latitude and 150 E to 10 E longitude). In addition, the NESDIS POES-GOES product was added to fill in gaps caused by the Moderate Resolution Imaging Spectroradiometer (MODIS) being unable to sense in cloudy regions, replacing the recently-lost Advanced Microwave Scanning Radiometer for EOS with negligible change to product fidelity. The SST product now runs twice per day for Terra and Aqua combined data collections from 0000 to 1200 UTC and from 1200 to 0000 UTC, with valid analysis times at 0600 and 1800 UTC. The twice-daily compositing technique reduces the overall latency of the previous version while still representing the diurnal cycle characteristics. The SST composites are available at approximately four hours after the end of each collection period (i.e. 1600 UTC for the nighttime analysis and 0400 UTC for the daytime analysis). The real-time MODIS GVF composite has only received minor updates in the past year. The domain was expanded slightly to extend further west, north, and east to improve coverage over parts of southern Canada. Minor adjustments were also made to the manner in which GVF is calculated from the distribution of maximum Normalized Difference Vegetation Index from MODIS. The presentation will highlight some examples of the substantial inter-annual change in GVF that occurred from 2010 to 2011 in the U.S. Southern Plains as a result of the summer 2011 drought, and the early vegetation green up across the eastern U.S. due to the very warm conditions in March 2012. Finally, the SPoRT LIS runs the operational Noah land surface model (LSM) in real time over much of the eastern half of the CONUS. The Noah LSM is continually cycled in real time, uncoupled to any model, and driven by operational atmospheric analyses over a long-term, multi-year integration. The LIS-Noah provides the STRC EMS with high-resolution (3 km) LSM initialization data that are in equilibrium with the operational analysis forcing. The Noah LSM within the SPoRT LIS has been upgraded from version 2.7.1 to version 3.2, which has improved look-up table attributes for several land surface quantities. The surface albedo field is now being adjusted based on the input real-time MODIS GVF, thereby improving the net radiation. Also, the LIS-Noah now uses the newer MODIS-based land use classification scheme (i.e. the International Biosphere-Geosphere Programme [IGBP]) that has a better depiction of urban corridors in areas where urban sprawl has occurred. STRC EMS users interested in initializing their LSM fields with high-resolution SPoRT LIS data should set up their model domain with the MODIS-IGBP 20-class land use database and select Noah as the LSM.

Terra and Aqua MODIS Thermal Emissive Bands On-Orbit Calibration and Performance

NASA Technical Reports Server (NTRS)

Xiong, Xiaoxiong; Wu, Aisheng; Wenny, Brian N.; Madhavan, Sriharsha; Wang, Zhipeng; Li, Yonghong; Chen, Na; Barnes, William L.; Salomonson, Vincent V.

2015-01-01

Since launch, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua spacecraft have operated successfully for more than 14 and 12 years, respectively. A key instrument for National Aeronautics and Space Administration Earth Observing System missions, MODIS was designed to make continuous observations for studies of Earth's land, ocean, and atmospheric properties and to extend existing data records from heritage Earth observing sensors. The 16 thermal emissive bands (TEBs) (3.75-14.24 micrometers) are calibrated on orbit using a temperature controlled blackbody (BB). Both Terra and Aqua MODIS BBs have displayed minimal drift over the mission lifetime, and the seasonal variations of the BB temperature are extremely small in Aqua MODIS. The long-term gain and noise equivalent difference in temperature performance of the 160 TEB detectors on both MODIS instruments have been well behaved and generally very stable. Small but noticeable variations of Aqua MODIS bands 33-36 (13.34-14.24 micrometer) response in recent years are primarily due to loss of temperature control margin of its passive cryoradiative cooler. As a result, fixed calibration coefficients, previously used by bands when the BB temperature is above their saturation temperatures, are replaced by the focal-plane-temperature-dependent calibration coefficients. This paper presents an overview of the MODIS TEB calibration, the on-orbit performance, and the challenging issues likely to impact the instruments as they continue operating well past their designed lifetime of six years.

Reunion Island Volcano Erupts

NASA Technical Reports Server (NTRS)

2002-01-01

On January 16, 2002, lava that had begun flowing on January 5 from the Piton de la Fournaise volcano on the French island of Reunion abruptly decreased, marking the end of the volcano's most recent eruption. These false color MODIS images of Reunion, located off the southeastern coast of Madagascar in the Indian Ocean, were captured on the last day of the eruption (top) and two days later (bottom). The volcano itself is located on the southeast side of the island and is dark brown compared to the surrounding green vegetation. Beneath clouds (light blue) and smoke, MODIS detected the hot lava pouring down the volcano's flanks into the Indian Ocean. The heat, detected by MODIS at 2.1 um, has been colored red in the January 16 image, and is absent from the lower image, taken two days later on January 18, suggesting the lava had cooled considerably even in that short time. Earthquake activity on the northeast flank continued even after the eruption had stopped, but by January 21 had dropped to a sufficiently low enough level that the 24-hour surveillance by the local observatory was suspended. Reunion is essentially all volcano, with the northwest portion of the island built on the remains of an extinct volcano, and the southeast half built on the basaltic shield of 8,630-foot Piton de la Fournaise. A basaltic shield volcano is one with a broad, gentle slope built by the eruption of fluid basalt lava. Basalt lava flows easily across the ground remaining hot and fluid for long distances, and so they often result in enormous, low-angle cones. The Piton de la Fournaise is one of Earth's most active volcanoes, erupting over 150 times in the last few hundred years, and it has been the subject of NASA research because of its likeness to the volcanoes of Mars. Image courtesy Jacques Descloitres, MODIS Land Rapid Response Team at NASA GSFC

Spatial and temporal remote sensing data fusion for vegetation monitoring

USDA-ARS?s Scientific Manuscript database

The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery...

Transition and Evaluation of RGB Imagery to WFOs and National Centers by NASA SPoRT

NASA Technical Reports Server (NTRS)

Fuell, Kevin K.; Molthan, Andrew L.

2012-01-01

MODIS Snow/Cloud and True Color RGB imagery has been used by SPoRT partners since 2004 to examine changes in surface features such as snow cover, vegetation, ocean color, fires, smoke plumes, and oil spills.

Classification and Accuracy Assessment for Coarse Resolution Mapping within the Great Lakes Basin, USA

EPA Science Inventory

This study applied a phenology-based land-cover classification approach across the Laurentian Great Lakes Basin (GLB) using time-series data consisting of 23 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) composite images (250 ...

Satellite and ground-based analysis of the effects on vegetation of continuous SO2 degassing at Turrialba volcano (Costa Rica) and its application to hazard management

NASA Astrophysics Data System (ADS)

Tortini, R.; van Manen, S. M.; Burson, B.; Carn, S. A.

2014-12-01

Turrialba is an active stratovolcano located 35 km northeast of San Jose, Costa Rica's capital city and socioeconomic hub. After over 100 years of quiescence Turrialba resumed activity in 1996 progressively increasing its degassing and seismic activity, showing continuous gas emissions since 2007. Intermittent phreatic explosions with ash emissions that have reached the capital have occurred since 2010. This activity has resulted in the temporary evacuation of two villages, closure of the National Park that comprises the summit region of the volcano and devastation of the local ecosystem. We combined a variety of satellite-based time series with ground-based measurements of ambient gas concentrations, element deposition and surveys of species richness to enable a comprehensive assessment of SO2 emissions and changes in vegetation. Satellite-based time-series were obtained from Landsat ETM+, Terra ASTER, Terra/Aqua MODIS and Aura OMI, with some of the data dating back to 2000. From 2007-2010 we observed emissions of SO2 and loss of vegetation healthiness (i.e. decrease of EVI2) downwind of the vents. From 2010 onwards these stabilized, but we observe an apparent decrease in agriculture. Other multi-temporal products, such as the ALOS PALSAR FNF data, confirm our observations. The exposure to the volcanic plume resulted in high soil acidity and significant uptake of certain heavy metals by vegetation; in contrast other elements are leached from the soil as a result of the acid deposition. These factors are likely to be responsible for decreased species richness and physiological damage observed at Turrialba. Our study shows ecological impacts, in terms of soil characteristics, vegetation composition and diversity and physiological damage of vegetation, which all correlate to fumigation by Turrialba's plume. Analyzing and relating the remote observations to conditions and impacts on the ground provides a better understanding of volcanic degassing, its impacts on vegetation and how satellite-based monitoring can be used to inform hazard management strategies related to land use and agricultural productivity in near-real time.

Creating a consistent dark-target aerosol optical depth record from MODIS and VIIRS

NASA Astrophysics Data System (ADS)

Levy, R. C.; Mattoo, S.; Munchak, L. A.; Patadia, F.; Holz, R.

2014-12-01

To answer fundamental questions about our changing climate, we must quantify how aerosols are changing over time. This is a global question that requires regional characterization, because in some places aerosols are increasing and in others they are decreasing. Although NASA's Moderate resolution Imaging Spectrometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, the creation of an aerosol climate data record (CDR) requires consistent multi-decadal data. With the Visible and Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP, there is potential to continue the MODIS aerosol time series. Yet, since the operational VIIRS aerosol product is produced by a different algorithm, it is not suitable to continue MODIS to create an aerosol CDR. Therefore, we have applied the MODIS Dark-target (DT) algorithm to VIIRS observations, taking into account the slight differences in wavelengths, resolutions and geometries between the two sensors. More specifically, we applied the MODIS DT algorithm to a dataset known as the Intermediate File Format (IFF), created by the University of Wisconsin. The IFF is produced for both MODIS and VIIRS, with the idea that a single (MODIS-like or ML) algorithm can be run either dataset, which can in turn be compared to the MODIS Collection 6 (M6) retrieval that is run on standard MODIS data. After minimizing or characterizing remaining differences between ML on MODIS-IFF (or ML-M) and M6, we have performed apples-to-apples comparison between ML-M and ML on VIIRS IFF (ML-V). Examples of these comparisons include time series of monthly global mean, monthly and seasonal global maps at 1° resolution, and collocations as compared to AERONET. We concentrate on the overlapping period January 2012 through June 2014, and discuss some of the remaining discrepancies between the ML-V and ML-M datasets.

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey; Sprigg, William; Huete, Alfredo; Levetin, Estelle; VandeWater, Peter; Nickovic, Slobodan; Pejanovic, Goran; Budge, Amelia; Heidi Krapfl; Myers, Orrin;

Winter wheat mapping combining variations before and after estimated heading dates

NASA Astrophysics Data System (ADS)

Qiu, Bingwen; Luo, Yuhan; Tang, Zhenghong; Chen, Chongcheng; Lu, Difei; Huang, Hongyu; Chen, Yunzhi; Chen, Nan; Xu, Weiming

2017-01-01

Accurate and updated information on winter wheat distribution is vital for food security. The intra-class variability of the temporal profiles of vegetation indices presents substantial challenges to current time series-based approaches. This study developed a new method to identify winter wheat over large regions through a transformation and metric-based approach. First, the trend surfaces were established to identify key phenological parameters of winter wheat based on altitude and latitude with references to crop calendar data from the agro-meteorological stations. Second, two phenology-based indicators were developed based on the EVI2 differences between estimated heading and seedling/harvesting dates and the change amplitudes. These two phenology-based indicators revealed variations during the estimated early and late growth stages. Finally, winter wheat data were extracted based on these two metrics. The winter wheat mapping method was applied to China based on the 250 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) time series datasets. Accuracy was validated with field survey data, agricultural census data, and Landsat-interpreted results in test regions. When evaluated with 653 field survey sites and Landsat image interpreted data, the overall accuracy of MODIS-derived images in 2012-2013 was 92.19% and 88.86%, respectively. The MODIS-derived winter wheat areas accounted for over 82% of the variability at the municipal level when compared with agricultural census data. The winter wheat mapping method developed in this study demonstrates great adaptability to intra-class variability of the vegetation temporal profiles and has great potential for further applications to broader regions and other types of agricultural crop mapping.

Detecting leaf pulvinar movements on NDVI time series of desert trees: a new approach for water stress detection.

PubMed

Chávez, Roberto O; Clevers, Jan G P W; Verbesselt, Jan; Naulin, Paulette I; Herold, Martin

2014-01-01

Heliotropic leaf movement or leaf 'solar tracking' occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVI mo-mi) and between winter and summer (ΔNDVI W-S). In this paper, we showed that the ΔNDVI mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVI W-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVI mo-mi and ΔNDVI W-S. For an 11-year time series without rainfall events, Landsat ΔNDVI W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVI mo-mi and ΔNDVI W-S have potential to detect early water stress of paraheliotropic vegetation.

Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

PubMed Central

Chávez, Roberto O.; Clevers, Jan G. P. W.; Verbesselt, Jan; Naulin, Paulette I.; Herold, Martin

2014-01-01

Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVImo-mi) and between winter and summer (ΔNDVIW-S). In this paper, we showed that the ΔNDVImo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVIW-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVImo-mi and ΔNDVIW-S. For an 11-year time series without rainfall events, Landsat ΔNDVIW-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVImo-mi and ΔNDVIW-S have potential to detect early water stress of paraheliotropic vegetation. PMID:25188305

Satellite-observed changes in vegetation sensitivities to surface soil moisture and total water storage variations since the 2011 Texas drought

NASA Astrophysics Data System (ADS)

A, Geruo; Velicogna, Isabella; Kimball, John S.; Du, Jinyang; Kim, Youngwook; Colliander, Andreas; Njoku, Eni

2017-05-01

We combine soil moisture (SM) data from AMSR-E and AMSR-2, and changes in terrestrial water storage (TWS) from time-variable gravity data from GRACE to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE-derived TWS provides spatially continuous observations of changes in overall water supply and regional drought extent, persistence and severity, while satellite-derived SM provides enhanced delineation of shallow-depth soil water supply. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depths in relation to satellite-based enhanced vegetation index (EVI) and gross primary productivity (GPP) from MODIS and solar-induced fluorescence (SIF) from GOME-2, during and following major drought events observed in the state of Texas, USA and its surrounding semiarid area for the past decade. We find that in normal years the spatial pattern of the vegetation-moisture relationship follows the gradient in mean annual precipitation. However since the 2011 hydrological drought, vegetation growth shows enhanced sensitivity to surface SM variations in the grassland area located in central Texas, implying that the grassland, although susceptible to drought, has the capacity for a speedy recovery. Vegetation dependency on TWS weakens in the shrub-dominated west and strengthens in the grassland and forest area spanning from central to eastern Texas, consistent with changes in water supply pattern. We find that in normal years GRACE TWS shows strong coupling and similar characteristic time scale to surface SM, while in drier years GRACE TWS manifests stronger persistence, implying longer recovery time and prolonged water supply constraint on vegetation growth. The synergistic combination of GRACE TWS and surface SM, along with remote-sensing vegetation observations provides new insights into drought impact on vegetation-moisture relationship, and unique information regarding vegetation resilience and the recovery of hydrological drought.

Characterization of extreme years in Central Europe between 2000 and 2016 according to specific vegetation characteristics based on Earth Observatory data

NASA Astrophysics Data System (ADS)

Kern, Anikó; Marjanović, Hrvoje; Barcza, Zoltán

2017-04-01

Extreme weather events frequently occur in Central Europe, affecting the state of the vegetation in large areas. Droughts and heat-waves affect all plant functional types, but the response of the vegetation is not uniform and depends on other parameters, plant strategies and the antecedent meteorological conditions as well. Meteorologists struggle with the definition of extreme events and selection of years that can be considered as extreme in terms of meteorological conditions due to the large variability of the meteorological parameters both in time and space. One way to overcome this problem is the definition of extreme weather based on its observed effect on plant state. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), the Fraction of Photosynthetically Active Radiation (FPAR) and the Gross Primary Production (GPP) are different measures of the land vegetation derived from remote sensing data, providing information about the plant state, but it is less known how weather anomalies affect these measures. We used the vegetation related official products created from the measurements of the MODerate resolution Imaging Spectroradiometer (MODIS) on board satellite Terra to select and characterize the extreme years in Central European countries during the 2000-2016 time period. The applied Collection-6 MOD13 NDVI/EVI, MOD15 LAI/FPAR and MOD17 GPP datasets have 500 m × 500 m spatial resolution covering the region of the Carpathian-Basin. After quality and noise filtering (and temporal interpolation in case of MOD13) 8-day anomaly values were derived to investigate the different years. The freely available FORESEE meteorological database was used to study climate variability in the region. Daily precipitation and maximum/minimum temperature fields at 1/12° × 1/12° grid were resampled to the 8-day temporal and 500 m × 500 m spatial resolution of the MODIS products. To discriminate the different behavior of the various plant functional types MODIS (MCD12) and CORINE (CLC2012) land cover datasets were applied and handled together. Based on the determination of the reliable pixels with different plant types the response of broadleaf forests, coniferous forests, grasslands and croplands were discriminated and investigated. Characteristic time periods were selected based on the remote sensing data to define anomalies, and then the meteorological data were used to define critical time periods within the year that has the strongest effect on the observed anomalies. Similarities/dissimilarities between the behaviors of the different remotely sensed measures are also studied to elucidate the consistency of the indices. The results indicate that the diverse remote sensing indices typically co-vary but reveal strong plant functional type dependency. The study suggest that the selection of extreme years based on annual data is not the best choice, as shorter time periods within the years explain the anomalies to a higher degree than annual data. The results can be used to select anomalous years outside of the satellite era as well. Keywords: Remote sensing, meteorology; extreme years; MODIS, NDVI; EVI; LAI; FPAR; GPP; phenology

MOD06 Optical and Microphysical Retrievals

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, Steven; Arnold, G. T.; Dinsick, J.; Gatebe, C. K.; Gray, M. A.; Hubanks, P. A.; Moody, E. G.; Wind, B.; Wind, G.

2003-01-01

Major efforts over the past six months included: (1) submission of MOD06 Optical and Microphysical Retrieval recompetition proposal, (2) delivery of a MODIS Atmosphere Level-3 update, (3) delivery of the MODIS Atmosphere s new combined Level-2 product, (4) development of an above-cloud precipitable water research algorithm and a multi-layer cloud detection algorithm, (5) continued development of a Fortran 90 version of the retrieval code for use with MAS as well as operational MODIS processing, (6) preliminary analysis of CRYSTAL-FACE field experiment in July 2002, (7) continued analysis of data obtained during the SAFARI 2000 dry season campaign in southern Africa, and the Arctic FIRE-ACE experiment.

Infrared Algorithm Development for Ocean Observations with EOS/MODIS

NASA Technical Reports Server (NTRS)

Brown, Otis B.

1997-01-01

Efforts continue under this contract to develop algorithms for the computation of sea surface temperature (SST) from MODIS infrared measurements. This effort includes radiative transfer modeling, comparison of in situ and satellite observations, development and evaluation of processing and networking methodologies for algorithm computation and data accession, evaluation of surface validation approaches for IR radiances, development of experimental instrumentation, and participation in MODIS (project) related activities. Activities in this contract period have focused on radiative transfer modeling, evaluation of atmospheric correction methodologies, undertake field campaigns, analysis of field data, and participation in MODIS meetings.

Summary of Terra and Aqua MODIS Long-Term Performance

NASA Technical Reports Server (NTRS)

Xiong, Xiaoxiong (Jack); Wenny, Brian N.; Angal, Amit; Barnes, William; Salomonson, Vincent

2011-01-01

Since launch in December 1999, the MODIS ProtoFlight Model (PFM) onboard the Terra spacecraft has successfully operated for more than 11 years. Its Flight Model (FM) onboard the Aqua spacecraft, launched in May 2002, has also successfully operated for over 9 years. MODIS observations are made in 36 spectral bands at three nadir spatial resolutions and are calibrated and characterized regularly by a set of on-board calibrators (OBC). Nearly 40 science products, supporting a variety of land, ocean, and atmospheric applications, are continuously derived from the calibrated reflectances and radiances of each MODIS instrument and widely distributed to the world-wide user community. Following an overview of MODIS instrument operation and calibration activities, this paper provides a summary of both Terra and Aqua MODIS long-term performance. Special considerations that are critical to maintaining MODIS data quality and beneficial for future missions are also discussed.

Spatial variations in atmospheric CO2 concentrations during the ARCTAS-CARB 2008 Summer Campaign

NASA Astrophysics Data System (ADS)

Vadrevu, K. P.; Choi, Y.; Vay, S. A.

2009-12-01

The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a major NASA field campaign designed to understand the transport and transformation of trace gases and aerosols on transcontinental and intercontinental scales and their impact on the composition of the arctic atmosphere and climate. Preceding the summer ARCTAS deployment, measurements were conducted over the state of California in collaboration with the California Air Resources Board (CARB) utilizing the airborne chemistry payload already integrated on the NASA DC-8. In situ CO2 measurements were made using a modified infrared CO2 gas analyzer having a precision of 0.1 ppmv and accuracy of ±0.25 ppmv traceable to the WMO scale. This analysis focuses on the atmospheric CO2 variability and biospheric/atmospheric exchange over California. We used multi-satellite remote sensing datasets to relate airborne observations of CO2 to infer sources and sinks. Georeferencing the airborne CO2 transect data with the LANDSAT derived land cover datasets over California suggested significant spatial variations. The airborne CO2 concentrations were found to be 375-380ppm over the Pacific ocean, 385-391ppm in the highly vegetated agricultural areas, 400-420 in the near coastal areas and greater than 425ppmv in the urban areas. Analysis from MODIS fire products suggested significant fires in northern California. CO2 emissions exceeded 425ppmv in the fire affected regions, where mostly Douglas and White Fir conifers and mixed Chaparral vegetation was burnt. Analysis from GOES-East and GOES-West visible satellite imagery suggested significant smoke plumes moving from northern California towards Nevada and Idaho. To infer the biospheric uptake of CO2, we tested the potential correlations between airborne CO2 data and MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Results suggested significant anti-correlations between the airborne CO2 data and vegetation indices. The correlations were stronger in agriculture than forested regions. We also tested the correlations between the MODIS leaf area index (LAI) and airborne CO2 data. Although, the correlation strength was low, higher LAI values were associated with low CO2 concentrations suggesting biospheric uptake. Particulate matter (PM2.5) concentration records obtained from the US EPA corresponding to the airborne sampling domain in the peak forest fire areas suggested concentrations exceeded 42.0ug/m3. Furthermore, analysis of MODIS aerosol optical depth values at 500nm exceeded 0.70 at several places, suggesting intense fire activity. Using the airborne measurements as well as multi-satellite remote sensing datasets, the implications of fire and other land use activities on atmospheric CO2 concentrations in California during the summer of 2008 will be presented.

Estimation of fire emissions from satellite-based measurements

NASA Astrophysics Data System (ADS)

Ichoku, C. M.; Kaufman, Y. J.

2004-12-01

Biomass burning is a worldwide phenomenon affecting many vegetated parts of the globe regularly. Fires emit large quantities of aerosol and trace gases into the atmosphere, thus influencing the atmospheric chemistry and climate. Traditional methods of fire emissions estimation achieved only limited success, because they were based on peripheral information such as rainfall patterns, vegetation types and changes, agricultural practices, and surface ozone concentrations. During the last several years, rapid developments in satellite remote sensing has allowed more direct estimation of smoke emissions using remotely-sensed fire data. However, current methods use fire pixel counts or burned areas, thereby depending on the accuracy of independent estimations of the biomass fuel loadings, combustion efficiency, and emission factors. With the enhanced radiometric range of its 4-micron fire channel, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which flies aboard both of the Earth Observing System (EOS) Terra and Aqua Satellites, is able to measure the rate of release of fire radiative energy (FRE) in MJ/s (something that older sensors could not do). MODIS also measures aerosol distribution. Taking advantage of these new resources, we have developed a procedure combining MODIS fire and aerosol products to derive FRE-based smoke emission coefficients (Ce in kg/MJ) for different regions of the globe. These coefficients are simply used to multiply FRE from MODIS to derive the emitted smoke aerosol mass. Results from this novel methodology are very encouraging. For instance, it was found that the smoke total particulate mass emission coefficient for the Brazilian Cerrado ecosystem (approximately 0.022 kg/MJ) is about twice the value for North America or Australia, but about 50 percent lower than the value for Zambia in southern Africa.

Estimation of Fire Emissions from Satellite-Based Measurements

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Yoram J.

2004-01-01

Biomass burning is a worldwide phenomenon affecting many vegetated parts of the globe regularly. Fires emit large quantities of aerosol and trace gases into the atmosphere, thus influencing the atmospheric chemistry and climate. Traditional methods of fire emissions estimation achieved only limited success, because they were based on peripheral information such as rainfall patterns, vegetation types and changes, agricultural practices, and surface ozone concentrations. During the last several years, rapid developments in satellite remote sensing has allowed more direct estimation of smoke emissions using remotely-sensed fire data. However, current methods use fire pixel counts or burned areas, thereby depending on the accuracy of independent estimations of the biomass fuel loadings, combustion efficiency, and emission factors. With the enhanced radiometric range of its 4-micron fire channel, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which flies aboard both of the Earth Observing System EOS) Terra and Aqua Satellites, is able to measure the rate of release of fire radiative energy (FRE) in MJ/s (something that older sensors could not do). MODIS also measures aerosol distribution. Taking advantage of these new resources, we have developed a procedure combining MODIS fire and aerosol products to derive FRE-based smoke emission coefficients (C(e), in kg/MJ) for different regions of the globe. These coefficients are simply used to multiply FRE from MODIS to derive the emitted smoke aerosol mass. Results from this novel methodology are very encouraging. For instance, it was found that the smoke total particulate mass emission coefficient for the Brazilian Cerrado ecosystem (approximately 0.022 kg/MJ) is about twice the value for North America, Western Europe, or Australia, but about 50% lower than the value for southern Africa.

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

PubMed

Zheng, Yang; Wu, Bingfang; Zhang, Miao; Zeng, Hongwei

2016-12-10

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5's spatial resolution and at MODIS's temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R ² of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R ² of the SOS ranging from 0.68 to 0.86 and with an R ² of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

Evaluating NASA S-NPP continuity cloud products for climate research using CALIPSO, CATS and Level-3 analysis

NASA Astrophysics Data System (ADS)

Holz, R.; Platnick, S. E.; Meyer, K.; Frey, R.; Wind, G.; Ackerman, S. A.; Heidinger, A. K.; Botambekov, D.; Yorks, J. E.; McGill, M. J.

2016-12-01

The launch of VIIRS and CrIS on Suomi NPP in the fall of 2011 introduced the next generation of U.S. operational polar orbiting environmental observations. Similar to MODIS, VIIRS provides visible and IR observations at moderate spatial resolution and has a 1:30 pm equatorial crossing time consistent with the MODIS on Aqua platform. However unlike MODIS, VIIRS lacks water vapor and CO2 absorbing channels that are used by the MODIS cloud algorithms for both cloud detection and to retrieve cloud top height and cloud emissivity for ice clouds. Given the different spectral and spatial characteristics of VIIRS, we seek to understand the extent to which the 15-year MODIS climate record can be continued with VIIRS/CrIS observations while maintaining consistent sensitivities across the observational systems. This presentation will focus on the evaluation of the latest version of the NASA funded cloud retrieval algorithms being developed for climate research. We will present collocated inter-comparisons between the imagers (VIIRS and MODIS Aqua) with CALIPSO and Cloud Aerosol Transport System (CATS) lidar observations as well as long term statistics based on a new Level-3 (L3) product being developed as part the project. The CALIPSO inter-comparisons will focus on cloud detection (cloud mask) with a focus on the impact of recent modifications to the cloud mask and how these changes impact the global statistics. For the first time we will provide inter-comparisons between two different cloud lidar systems (CALIOP and CATS) and investigate how the different sensitivities of the lidars impact the cloud mask and cloud comparisons. Using CALIPSO and CATS as the reference, and applying the same algorithms to VIIRS and MODIS, we will discuss the consistency between products from both imagers. The L3 analysis will focus on the regional and seasonal consistency between the suite of MODIS and VIIRS continuity cloud products. Do systematic biases remains when using consistent algorithms but applied to different observations (MODIS or VIIRS)?

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

The Growth Periods Responses of Double-season Paddy Rice to Climate Change in Hunan Province, China over the Past Two Decades

NASA Astrophysics Data System (ADS)

Wang, Y.; Li, Y.; Yi, M.; Ye, T.

2015-12-01

The shifts of timing and length of the growing season (TLGS) are important indicators of crop response to climate change. With the help of satellite image data, it becomes feasible to retrieve the TLGS in a spatially continuous manner, which also accommodates local variation of TGSs. In this article, the TGSs of paddy rice in Hunan Province, China since 1995 was retrieved using times-series curves of MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI). The change in TLGS and its connection to regional climate change was discussed. The results showed the advance of TGSs of double-season paddy rice and the reduction of GSL in the past 20 years, which is believed to be linked to the rise in the temperature and precipitation in the growth periods. Understanding the local variation and trend of TLGS influenced by climate change is essential for making agricultural adaptive policies to reduce the risk of crop damaged, also can provide key information for studying how multi-hazards affect crop exposure.

Forest canopy growth dynamic modeling based on remote sensing prodcuts and meteorological data in Daxing'anling of Northeast China

NASA Astrophysics Data System (ADS)

Wu, Qiaoli; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang

2014-11-01

Leaf Area Index (LAI) is an important biophysical variable for vegetation. Compared with vegetation indexes like NDVI and EVI, LAI is more capable of monitoring forest canopy growth quantitatively. GLASS LAI is a spatially complete and temporally continuous product derived from AVHRR and MODIS reflectance data. In this paper, we present the approach to build dynamic LAI growth models for young and mature Larix gmelinii forest in north Daxing'anling in Inner Mongolia of China using the Dynamic Harmonic Regression (DHR) model and Double Logistic (D-L) model respectively, based on the time series extracted from multi-temporal GLASS LAI data. Meanwhile we used the dynamic threshold method to attract the key phenological phases of Larix gmelinii forest from the simulated time series. Then, through the relationship analysis between phenological phases and the meteorological factors, we found that the annual peak LAI and the annual maximum temperature have a good correlation coefficient. The results indicate this forest canopy growth dynamic model to be very effective in predicting forest canopy LAI growth and extracting forest canopy LAI growth dynamic.

Updates on the development of Deep Blue aerosol algorithm for constructing consistent long-term data records from MODIS to VIIRS

NASA Astrophysics Data System (ADS)

Hsu, N. Y. C.; Sayer, A. M.; Lee, J.; Kim, W. V.

2017-12-01

The impacts of natural and anthropogenic sources of air pollution on climate and human health have continued to gain attention from the scientific community. In order to facilitate these effects, high quality consistent long-term global aerosol data records from satellites are essential. Several EOS-era instruments (e.g., SeaWiFS, MODIS, and MISR) are able to provide such information with a high degree of fidelity. However, with the aging MODIS sensors and the launch of the VIIRS instrument on Suomi NPP in late 2011, the continuation of long-term aerosol data records suitable for climate studies from MODIS to VIIRS is needed urgently. Recently, we have successfully modified our MODIS Deep Blue algorithm to process the VIIRS data. Extensive works were performed in refining the surface reflectance determination scheme to account for the wavelength differences between MODIS and VIIRS. Better aerosol models (including non-spherical dust) are also now implemented in our VIIRS algorithm compared to the MODIS C6 algorithm. We will show the global (land and ocean) distributions of various aerosol products from Version 1 of the VIIRS Deep Blue data set. The preliminary validation results of these new VIIRS Deep Blue aerosol products using data from AERONET sunphotometers over land and ocean will be discussed. We will also compare the monthly averaged Deep Blue aerosol optical depth (AOD) from VIIRS with the MODIS C6 products to investigate if any systematic biases may exist between MODIS C6 and VIIRS AOD. The Version 1 VIIRS Deep Blue aerosol products are currently scheduled to be released to the public in 2018.

Vegetation canopy structure from NASA EOS multiangle imaging

USDA-ARS?s Scientific Manuscript database

We used red band bidirectional reflectance data from the NASA Multiangle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS) mapped onto a 250 m grid in a multiangle approach to obtain estimates of woody plant fractional cover and crown height through adjus...

Recent weather extremes and impact agricultural production and vector-borne disease patterns

USDA-ARS?s Scientific Manuscript database

We document significant worldwide weather anomalies that affected agriculture and vector-borne disease outbreaks during the 2010-2012 period. We utilized 2000-2012 vegetation index and land surface temperature data from NASA’s satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to ...

A decadal observation of vegetation dynamics using multi-resolution satellite images

NASA Astrophysics Data System (ADS)

Chiang, Yang-Sheng; Chen, Kun-Shan; Chu, Chang-Jen

2012-10-01

Vegetation cover not just affects the habitability of the earth, but also provides potential terrestrial mechanism for mitigation of greenhouse gases. This study aims at quantifying such green resources by incorporating multi-resolution satellite images from different platforms, including Formosat-2(RSI), SPOT(HRV/HRG), and Terra(MODIS), to investigate vegetation fractional cover (VFC) and its inter-/intra-annual variation in Taiwan. Given different sensor capabilities in terms of their spatial coverage and resolution, infusion of NDVIs at different scales was used to determine fraction of vegetation cover based on NDVI. Field campaign has been constantly conducted on a monthly basis for 6 years to calibrate the critical NDVI threshold for the presence of vegetation cover, with test sites covering IPCC-defined land cover types of Taiwan. Based on the proposed method, we analyzed spatio- temporal changes of VFC for the entire Taiwan Island. A bimodal sequence of VFC was observed for intra-annual variation based on MODIS data, with level around 5% and two peaks in spring and autumn marking the principal dual-cropping agriculture pattern in southwestern Taiwan. Compared to anthropogenic-prone variation, the inter-annual VFC (Aug.-Oct.) derived from HRV/HRG/RSI reveals that the moderate variations (3%) and the oscillations were strongly linked with regional climate pattern and major disturbances resulting from extreme weather events. Two distinct cycles (2002-2005 and 2005-2009) were identified in the decadal observations, with VFC peaks at 87.60% and 88.12% in 2003 and 2006, respectively. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Hydrocentric view of Agro-ecosystem Resiliency to Extreme Hydrometeorological and Climate Events in the High Plains, US.

NASA Astrophysics Data System (ADS)

Munoz-Arriola, Francisco; Sharma, Ashutosh; Werner, Katherine; Chacon, Juan-Carlos; Corzo, Gerald; Goyal, Manish-Kumar

2017-04-01

An increasing incidence of Hydrometeorological and Climate Extreme Events (EHCEs) is challenging food, water, and ecosystem services security at local to global contexts. This study aims to understand how a large-scale representation of agroecosystems and ecosystems respond to EHCE in the Northern Highplains, US. To track such responses the Variable Infiltration Capacity model (VIC) Land Surface Hydrology model was used and two experiments were implemented. The first experiment uses the LAI MODIS15A2 product to capture dynamic responses of vegetation with a time span from 2000 to 2013. The second experiment used a climatological fixed seasonal cycle calculated as the average from the 2000-2013 dynamic MODIS15A2 product to isolate vegetation from soil physical responses. Based on the analyses of multiple hydrological variables and state variables and high-level organization of agroecosystems and ecosystems, we evidence how the influence of droughts and anomalously wet conditions affect hydrological resilience at large scale.

Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data

PubMed Central

van Leeuwen, Willem J. D.

2008-01-01

This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index) data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR) and examine burn severity at the selected sites. The phenological metrics (pheno-metrics) included the timing and greenness (i.e. NDVI) for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation cover dynamics and successional changes in response to drought, wildfire disturbances, and forest restoration treatments in fire-suppressed forests. PMID:27879809

Dynamics of the wetland vegetation in large lakes of the Yangtze Plain in response to both fertilizer consumption and climatic changes

NASA Astrophysics Data System (ADS)

Hou, Xuejiao; Feng, Lian; Chen, Xiaoling; Zhang, Yunlin

2018-07-01

Using moderate-resolution imaging spectroradiometer (MODIS) data that cover the 15-year period from 2000 to 2014 and a phenology-based classification method, the long-term changes in the wetland vegetation of 25 large lakes on the Yangtze Plain were obtained. The classification method was developed based on the phenological information extracted from time series of MODIS observations, which demonstrated mean user's/producer's accuracies of 76.17% and 84.58%, respectively. The first comprehensive record of the spatial distribution and temporal dynamics of wetland vegetation in the large lakes on the Yangtze Plain was created. Of the 25 lakes examined, 17 showed a decreasing trend of vegetation area percentages (VAPs) during the study period, and 7 were statistically significant (p < 0.05). The same number of lakes was found to display decreasing trends in vegetation greenness over this 15-year period, and these decreasing trends were statistically significant (p < 0.05) for 11 of the lakes. Substantially fewer lakes showed increases in either their VAPs or their vegetation greenness values. Analysis using a multiple general linear model revealed that the amounts of chemical fertilizer used for farmlands surrounding the lakes, precipitation, daily sunshine hours, temperature and water turbidity played the most important roles in regulating the interannual changes in vegetation greenness in 40% (10/25), 12% (3/25), 4% (1/25), 20% (5/25) and 12% (3/25) of the lake wetlands, respectively. On average, the combined effects of these five driving factors above explained 89.08 ± 7.89% of the variation in greenness over this 15-year period for the 25 lakes. This wetland vegetation environmental data record (EDR) of large lakes in Yangtze Plain demonstration will provide a crucial baseline information for the wetland environment conservation and restoration.

Validation of MODIS Aerosol Optical Depth Retrieval Over Land

NASA Technical Reports Server (NTRS)

Chu, D. A.; Kaufman, Y. J.; Ichoku, C.; Remer, L. A.; Tanre, D.; Holben, B. N.; Einaudi, Franco (Technical Monitor)

2001-01-01

Aerosol optical depths are derived operationally for the first time over land in the visible wavelengths by MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the EOSTerra spacecraft. More than 300 Sun photometer data points from more than 30 AERONET (Aerosol Robotic Network) sites globally were used in validating the aerosol optical depths obtained during July - September 2000. Excellent agreement is found with retrieval errors within (Delta)tau=+/- 0.05 +/- 0.20 tau, as predicted, over (partially) vegetated surfaces, consistent with pre-launch theoretical analysis and aircraft field experiments. In coastal and semi-arid regions larger errors are caused predominantly by the uncertainty in evaluating the surface reflectance. The excellent fit was achieved despite the ongoing improvements in instrument characterization and calibration. This results show that MODIS-derived aerosol optical depths can be used quantitatively in many applications with cautions for residual clouds, snow/ice, and water contamination.

Vegetation Cover and Evapotranspiration in the Arid Northwest China and Their Relationship with Groundwater Depth

NASA Astrophysics Data System (ADS)

Jin, X.; Zhang, Y.

2010-12-01

Evapotranspiration (ET) is a major component in the water balance of semi-arid areas and typically the largest consumer of the incoming energy. Estimation of ET and separation of evaporation and transpiration from ET are important topics in ecohydrological studies. The relationship among soil water evaporation, vegetation transpiration and groundwater depth in the arid area China was quantified by combining remote sensing with groundwater data in this paper. The Yinchuan Plain, located in northwestern China, is the upstream area of the Yellow river between the Helan mountain and the Erdos plateau with a total area of 7790 km2. In recent years, rapid development of the region’s economy has resulted in overuse of groundwater resources and caused the decline of groundwater levels in the regional aquifers. The MODIS NDVI data, the vegetation index maps depicting spatial and temporal variations in vegetation activities, is based on 16-day composites and its spatial resolution is 250 m. To be consistent with the groundwater data, the MODIS NDVI values of April and July 2004 were used to analyze the relationship between groundwater and vegetation in this study. The MODIS Surface-Reflectance Product (MOD 09) and the Aqua MOD 11 product of April and July 2004 were used to estimate daily evaporation. The values of the groundwater depth were obtained at the same resolution of the MOD 9 image by interpolating 520 measured groundwater depths in April and July of 2004 in a 500 m×500 m grid. The meteorological data used in this study includes sea level pressure, air temperature, wind speed, wind direction, relative humidity and pan evaporation. The relationship among soil evaporation, vegetation transpiration and groundwater depth was quantified with the combination of remote sensing and groundwater data. The groundwater depth data for April and July are the indicators for dry season and raining season during a year in the Yinchuan plain and thus were used in this study. Based on the results obtained by analyzing the relationship between ET and groundwater depth, the following important conclusions can be drawn. 1) In July of raining season, NDVI and vegetation fraction decreased with groundwater depth increase and NDVI was smaller than 0.3 (very low vegetation cover) when the groundwater depth was larger than 6 m. 2) The relationship between NDVI and daily ET indicated that there is vegetation cover on the ground when the NDVI is larger than 0.2 in the Yinchuan Plain. 3) The daily soil evaporation varies between 0.5 mm and 2.5 mm, whereas the vegetation transpiration ranges between 0 and 3.4 mm. 4) The threshold depth of the phreatic evaporation from the bare soil in the Yinchuan plain is 4 m and 6 m in dry season and raining season, respectively. The surface evaporation is roughly equal to soil water evaporation when the groundwater depth is larger than the threshold depth.

[Land cover classification of Four Lakes Region in Hubei Province based on MODIS and ENVISAT data].

PubMed

Xue, Lian; Jin, Wei-Bin; Xiong, Qin-Xue; Liu, Zhang-Yong

2010-03-01

Based on the differences of back scattering coefficient in ENVISAT ASAR data, a classification was made on the towns, waters, and vegetation-covered areas in the Four Lakes Region of Hubei Province. According to the local cropping systems and phenological characteristics in the region, and by using the discrepancies of the MODIS-NDVI index from late April to early May, the vegetation-covered areas were classified into croplands and non-croplands. The classification results based on the above-mentioned procedure was verified by the classification results based on the ETM data with high spatial resolution. Based on the DEM data, the non-croplands were categorized into forest land and bottomland; and based on the discrepancies of mean NDVI index per month, the crops were identified as mid rice, late rice, and cotton, and the croplands were identified as paddy field and upland field. The land cover classification based on the MODIS data with low spatial resolution was basically consistent with that based on the ETM data with high spatial resolution, and the total error rate was about 13.15% when the classification results based on ETM data were taken as the standard. The utilization of the above-mentioned procedures for large scale land cover classification and mapping could make the fast tracking of regional land cover classification.

[Spectrum similarities-based analysis of spatial difference of snow cover for multi-scale satellite data-a case study of MODIS and HJ-1B data].

PubMed

Liu, Yan; Li, Yang; Yang, Yun; Jian, Ji

2014-05-01

Vegetation and bare soil were collected in the areas of Miyaluo district in northwest of Sichuan province, the Qilian Mountains in Qinghai province and northern areas of Xinjiang during the years of 2007 and 2013. Then these data were converted to spectral reflectance by applying sensor response function of MODIS and HJ-1B respectively within the range of visible light, near-infrared and shortwave infrared. Comprehensive analysis was made on spectral characteristics and reflectivity similarities and differences of different sensors between old and new snowmelt, under the condition of different snow depth and different snow cover. The conclusions can be drawn That is, there exists high consistency of spectral response between new snow and dirty snow for each sensor in the visible wavelength range, also it is true for bare soil and low vegetation. However, low consistency happens to other types of snow; especially snowmelt and frozen snow. The range of NDSI is relatively stable under the condition of different snow depth for full snow cover and the trend of NDSI shows great consistency for different sensors; NDSI threshold method for monitoring snow by using MODIS and HJ-1B data showed very obvious difference in spatial scales, which is a reasonable explanation of the existence of mixed pixels.

Recent Shift of Deforestation to High Elevation Areas from 2001 to 2013 in Borneo Detected by MODIS Data

NASA Astrophysics Data System (ADS)

Nagai, S.; Suzuki, R.

2015-12-01

The biomass of tropical forests sequestrates tons of carbon and plays an important role in the global carbon cycle regulating the climate. Also its high biodiversity ecosystems bring us many valuable resources and cultural and educational ecosystem services. However, large areas of the tropical forest are deforested and converted to oil palm or acacia plantation for the economic benefit of the local society mainly in Southeast Asia. Monitoring of the tropical forest from satellites provides us the information about the deforestation for decadal time period over extensive areas and enables us to discuss it from a scientific point of view. The purpose of this study is to reveal the interannual change and recent trend of deforestation in relation to the land elevation for decadal time period over Borneo by using data from Moderate Resolution Imaging Spectroradiometer (MODIS). We acquired the atmospherically corrected and cloud free Terra-MODIS and Aqua-MODIS daily data products (MOD09GA and MYD09GA; collection 5) from 2001 to 2013 for Borneo. We extracted the pixel values in the 500m surface reflectance bands 1 (red) and 4 (green) products and calculated the green-red vegetation index (GRVI), (band 4 - band 1) / (band 4 + band 1), at a daily time step. GRVI shows a positive value for the land prevailed by green vegetation, while it shows a negative value for the land prevailed by no-green components such as bare land. As for the elevation data, ASTER Global Digital Elevation Model (GDEM) which has 33.3m spatial resolution was employed. The original resolution was resampled to the grid system of MODIS data (i.e. 500m resolution). Pixels which had a negative GRVI ratio more than 80 % (termed as "no green pixel") in each year were regarded as the land characterized by no vegetation, and mapped the distribution for each year. Throughout the 13 years, no green pixels mainly found over the coastal low land below 20m of the elevation and the area was almost constant (around 3000km2). It is considered the deforestation for the plantations generally occurred over the easy access low lands. By contrast, it was obvious that no green pixels extended their distribution up to high elevation (20 to 120m) areas mainly after 2006. This trend suggests recent development of the plantation has been extended to relatively inland and high elevation areas.

Evaluation of GLI Reflectance and Vegetation Indices With MODIS Products

DTIC Science & Technology

2005-07-25

collective picture of a warming world and other changes in the climate system . Vegetation over land surfaces contains carbon that is re- leased to atmosphere...irradiance based on Thuiller 2002 (Thuiller et al., 2003), Lsat[W/m2/str/µm] is GLI observed radiance, and θs[rad] is solor zenith angle. The GLI Project...longer wavelength than 2500nm, MODTRAN4.0 IR solor irradiance is used. GLI atmospheric correction for land is conducted for Rayleigh scattering and

Sixteen Years of Terra MODIS On-Orbit Operation, Calibration, and Performance

NASA Technical Reports Server (NTRS)

Xiong, X.; Angal, A.; Wu, A.; Link, D.; Geng, X.; Barnes, W.; Solomonson, V.

2016-01-01

Terra MODIS has successfully operated for more than 16 years since its launch in December 1999. From its observations, many science data products have been generated in support of a broad range of research activities and remote sensing applications. Terra MODIS has operated in a number of configurations and experienced a few anomalies, including spacecraft and instrument related events. MODIS collects data in 36 spectral bands that are calibrated regularly by a set of on-board calibrators for their radiometric, spectral, and spatial performance. Periodic lunar observations and long-term radiometric trending over well-characterized ground targets are also used to support sensor on-orbit calibration. Dedicated efforts made by the MODIS Characterization Support Team (MCST) and continuing support from the MODIS Science Team have contributed to the mission success, enabling well-calibrated data products to be continuously generated and routinely delivered to users worldwide. This paper presents an overview of Terra MODIS mission operations, calibration activities, and instrument performance of the past 16 years. It illustrates and describes the results of key sensor performance parameters derived from on-orbit calibration and characterization, such as signal-to-noise ratio (SNR), noise equivalent temperature difference (NEdT), solar diffuser (SD) degradation, changes in sensor responses, center wavelengths, and band-to-band registration (BBR). Also discussed in this paper are the calibration approaches and strategies developed and implemented in support of MODIS Level 1B data production and re-processing, major challenging issues, and lessons learned. (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Grassland Npp Monitoring Based on Multi-Source Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Cai, Y. R.; Zheng, J. H.; Du, M. J.; Mu, C.; Peng, J.

2018-04-01

Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006-2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.

Integrating satellite remote sensing data and field data to predict rangeland structural indicators at the continental scale

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2016-12-01

Rangelands provide a variety of important ecosystem goods and services across drylands globally. They are also the most important emitters of dust across the globe. Field data collection based on points does not represent spatially continuous information about surface variables and, given the vast size of the world's rangelands, cannot cover even a small fraction of their area. Remote sensing is potentially a labor- and time-saving method to observe important rangeland vegetation variables at both temporal and spatial scales. Information on vegetation cover, bare gap size, and plant height provide key rangeland vegetation variables in arid and semiarid rangelands, in part because they strongly impact dust emission and determine wildlife habitat characteristics. This study reports on relationships between remote sensing in the reflected solar spectrum and field measures related to these three variables, and shows how these relationships can be extended to produce spatially and temporally continuous datasets coupled with quantitative estimates of error. Field data for this study included over 3,800 Assessment, Inventory, and Monitoring (AIM) measurements on Bureau of Land Management (BLM) lands throughout the western US. Remote sensing data were derived from MODIS nadir BRDF-adjusted reflectance (NBAR) and Landsat 8 OLI surface reflectance. Normalized bare gap size, total foliar cover, herbaceous cover and herbaceous height exhibit the greatest predictability from remote sensing variables with physically-reasonable relationships between remote sensing variables and field measures. Data fields produced using these relationships across the western US exhibit good agreement with independent high-resolution imagery.

Analysis of the pattern of potential woody cover in Texas savanna

NASA Astrophysics Data System (ADS)

Yang, Xuebin; Crews, Kelley A.; Yan, Bowei

2016-10-01

While woody plant encroachment has been observed worldwide in savannas and adversely affected the ecosystem structure and function, a thorough understanding of the nature of this phenomenon is urgently required for savanna management and restoration. Among others, potential woody cover (the maximum realizable woody cover that a given site can support), especially its variation over environment has huge implication on the encroachment management in particular, and on tree-grass interactions in general. This project was designed to explore the pattern of potential woody cover in Texas savanna, an ecosystem with a large rainfall gradient in west-east direction. Substantial random pixels were sampled across the study area from MODIS Vegetation Continuous Fields (VCF) tree cover layer (250 m). Since potential woody cover is suggested to be limited by water availability, a nonlinear 99th quantile regression was performed between the observed woody cover and mean annual precipitation (MAP) to model the pattern of potential woody cover. Research result suggests a segmented relationship between potential woody cover and MAP at MODIS scale. Potential biases as well as the practical and theoretical implications were discussed. Through this study, the hypothesis about the primary role of water availability in determining savanna woody cover was further confirmed in a relatively understudied US-located savanna.

Global Operational Remotely Sensed Evapotranspiration System for Water Resources Management: Case Study for the State of New Mexico

NASA Astrophysics Data System (ADS)

Halverson, G. H.; Fisher, J.; Magnuson, M.; John, L.

2017-12-01

An operational system to produce and disseminate remotely sensed evapotranspiration using the PT-JPL model and support its analysis and use in water resources decision making is being integrated into the New Mexico state government. A partnership between the NASA Western Water Applications Office (WWAO), the Jet Propulsion Laboratory (JPL), and the New Mexico Office of the State Engineer (NMOSE) has enabled collaboration with a variety of state agencies to inform decision making processes for agriculture, rangeland, and forest management. This system improves drought understanding and mobilization, litigation support, and economic, municipal, and ground-water planning through interactive mapping of daily rates of evapotranspiration at 1 km spatial resolution with near real-time latency. This is facilitated by daily remote sensing acquisitions of land-surface temperature and near-surface air temperature and humidity from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite as well as the short-term composites of Normalized Difference Vegetation Index (NDVI) and albedo provided by MODIS. Incorporating evapotranspiration data into agricultural water management better characterizes imbalances between water requirements and supplies. Monitoring evapotranspiration over rangeland areas improves remediation and prevention of aridification. Monitoring forest evapotranspiration improves wildlife management and response to wildfire risk. Continued implementation of this decision support system should enhance water and food security.

Post-wildfire summer greening depends on winter snowpack

NASA Astrophysics Data System (ADS)

Wilson, A.; Nolin, A. W.

2017-12-01

Forested, mountain landscapes in the Pacific Northwest (PNW) are changing at an unprecedented rate, largely due to shifts in the regional climate regime. Documented climatic trends include increasing wildfire frequency and intensity and an increasingly ephemeral snowpack, especially at moderate elevations. One relationship that has yet to be studied thoroughly is the dependence of post-wildfire forest recovery on winter snowpack. This study will correlate winter snowpack with summer greenness in the context of 15 recent severe wildfires across the PNW. Winter snow water equivalent will be estimated using a new Snow Cover Frequency (SCF) metric derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product. Summer forest greenness will be assessed using the Enhanced Vegetation Index (EVI), also derived from daily MODIS reflectance data. Regression tree analysis will be employed to characterize the relative importance of snowpack, elevation, slope, aspect, soil texture, and summer precipitation to summer greenness. Using findings from the regression tree analysis, the most critical physiographic factors will frame a multivariate time series spanning the 5 years pre-wildfire and 5 years post-wildfire in an effort to illustrate how the snowpack-revegetation relationship persists over time. As northwestern mountainous forests become more vulnerable to wildfire activity, it will be vital to continue deepening our understanding of how snowpack matters to post-wildfire forest recovery.

Light Diffusion in the Tropical Dry Forest of Costa Rica

NASA Astrophysics Data System (ADS)

Calvo-Rodriguez, S.; Sanchez-Azofeifa, G. A.

2016-06-01

Leaf Area Index (LAI) has been defined as the total leaf area (one-sided) in relation to the ground. LAI has an impact on tree growth and recruitment through the interception of light, which in turn affects primary productivity. Even though many instruments exist for estimating LAI from ground, they are often laborious and costly to run continuously. Measurements of LAI from the field using traditional sensors (e.g., LAI-2000) require multiple visits to the field under very specific sky conditions, making them unsuitable to operate in inaccessible areas and forests with dense vegetation, as well as areas where persistent sunny conditions are the norm like tropical dry forests. With this context, we proposed a methodology to characterize light diffusion based on NDVI and LAI measurements taken from the field in two successional stages in the tropical dry forest of Santa Rosa National Park in Costa Rica. We estimate a "K" coefficient to characterize light diffusion by the canopy, based on field NDVI measurements derived from optical phenology instruments and MODIS NDVI. From the coefficients determined, we estimated LAI values and compared them with ground measurements of LAI. In both successional stages ground measurements of LAI had no significant difference to the tower-derived LAI and the estimated LAI from MODIS NDVI.

IN SITU AND MODIS MOD15A2 LEAF AREA INDEX MEASUREMENTS OF A MID-ATLANTIC DECIDOUS FOREST SITE: PERSPECTIVES FROM FOUR-YEARS OF FIELD STUDIES

EPA Science Inventory

The U.S. Environmental Protection Agency is interested in leaf area index as it pertains to biogenic emissions, atmospheric pollutant deposition, ecological indicators, vegetation phenology, and land cover mapping.

Post-hurricane forest damage assessment using satellite remote sensing

Treesearch

W. Wang; J.J. Qu; X. Hao; Y. Liu; J.A. Stanturf

2010-01-01

This study developed a rapid assessment algorithm for post-hurricane forest damage estimation using moderate resolution imaging spectroradiometer (MODIS) measurements. The performance of five commonly used vegetation indices as post-hurricane forest damage indicators was investigated through statistical analysis. The Normalized Difference Infrared Index (NDII) was...

Advanced Land Surface Processes in the Coupled WRF/CMAQ with MODIS Input

EPA Science Inventory

Land surface modeling (LSM) is important in WRF/CMAQ for simulating the exchange of heat, moisture, momentum, trace atmospheric chemicals, and windblown dust between the land surface and the atmosphere.? Vegetation and soil treatments are crucial in LSM for surface energy budgets...

Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters

USDA-ARS?s Scientific Manuscript database

Measuring and monitoring controls on wind erosion can facilitate detection and prediction of soil degradation important for food security. Ground cover is widely recognised as an important factor for controlling soil erosion by wind and water. Consequently, maintaining ground cover (e.g., vegetation...

Fires in Chile

NASA Technical Reports Server (NTRS)

2002-01-01

On February 5, 2002, the dense smoke from numerous forest fires stretched out over the Pacific Ocean about 400 miles south of Santiago, Chile. This true-color Moderate-resolution Imaging Spectroradiometer (MODIS) image shows the fires, which are located near the city of Temuco. The fires are indicated with red dots (boxes in the high-resolution imagery). The fires were burning near several national parks and nature reserves in an area of the Chilean Andes where tourism is very popular. Southeast of the fires, the vegetation along the banks of the Rio Negro in Argentina stands out in dark green. Image courtesy Jacques Descloitres, MODIS Land Rapid Response Team at NASA GSFC

The moderate resolution imaging spectrometer (MODIS) science and data system requirements

NASA Technical Reports Server (NTRS)

Ardanuy, Philip E.; Han, Daesoo; Salomonson, Vincent V.

1991-01-01

The Moderate Resolution Imaging Spectrometer (MODIS) has been designated as a facility instrument on the first NASA polar orbiting platform as part of the Earth Observing System (EOS) and is scheduled for launch in the late 1990s. The near-global daily coverage of MODIS, combined with its continuous operation, broad spectral coverage, and relatively high spatial resolution, makes it central to the objectives of EOS. The development, implementation, production, and validation of the core MODIS data products define a set of functional, performance, and operational requirements on the data system that operate between the sensor measurements and the data products supplied to the user community. The science requirements guiding the processing of MODIS data are reviewed, and the aspects of an operations concept for the production of data products from MODIS for use by the scientific community are discussed.

Satellite Soil Moisture and Water Storage Observations Identify Early and Late Season Water Supply Influencing Plant Growth in the Missouri Watershed

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Colliander, A.; Njoku, E. G.

2017-12-01

We employ an array of continuously overlapping global satellite sensor observations including combined surface soil moisture (SM) estimates from SMAP, AMSR-E and AMSR-2, GRACE terrestrial water storage (TWS), and satellite precipitation measurements, to characterize seasonal timing and inter-annual variations of the regional water supply pattern and its associated influence on vegetation growth estimates from MODIS enhanced vegetation index (EVI), AMSR-E/2 vegetation optical depth (VOD) and GOME-2 solar-induced florescence (SIF). Satellite SM is used as a proxy of plant-available water supply sensitive to relatively rapid changes in surface condition, GRACE TWS measures seasonal and inter-annual variations in regional water storage, while precipitation measurements represent the direct water input to the analyzed ecosystem. In the Missouri watershed, we find surface SM variations are the dominant factor controlling vegetation growth following the peak of the growing season. Water supply to growth responds to both direct precipitation inputs and groundwater storage carry-over from prior seasons (winter and spring), depending on land cover distribution and regional climatic condition. For the natural grassland in the more arid central and northwest watershed areas, an early season anomaly in precipitation or surface temperature can have a lagged impact on summer vegetation growth by affecting the surface SM and the underlying TWS supplies. For the croplands in the more humid eastern portions of the watershed, the correspondence between surface SM and plant growth weakens. The combination of these complementary remote-sensing observations provides an effective means for evaluating regional variations in the timing and availability of water supply influencing vegetation growth.

Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves

NASA Astrophysics Data System (ADS)

Baumann, Matthias; Ozdogan, Mutlu; Richardson, Andrew D.; Radeloff, Volker C.

2017-02-01

Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas.

Extending MODIS Cloud Top and Infrared Phase Climate Records with VIIRS and CrIS

NASA Astrophysics Data System (ADS)

Heidinger, A. K.; Platnick, S. E.; Ackerman, S. A.; Holz, R.; Meyer, K.; Frey, R.; Wind, G.; Li, Y.; Botambekov, D.

2015-12-01

The MODIS imagers on the NASA EOS Terra and Aqua satellites have generated accurate and well-used cloud climate data records for 15 years. Both missions are expected to continue until the end of this decade and perhaps beyond. The Visible and Infrared Imaging Radiometer Suite (VIIRS) imagers on the Suomi-NPP (SNPP) mission (launched in October 2011) and future NOAA Joint Polar Satellite System (JPSS) platforms are the successors for imager-based cloud climate records from polar orbiting satellites after MODIS. To ensure product continuity across a broad suite of EOS products, NASA has funded a SNPP science team to develop EOS-like algorithms that can be use with SNPP and JPSS observations, including two teams to work on cloud products. Cloud data record continuity between MODIS and VIIRS is particularly challenging due to the lack of VIIRS CO2-slicing channels, which reduces information content for cloud detection and cloud-top property products, as well as down-stream cloud optical products that rely on both. Here we report on our approach to providing continuity specifically for the MODIS/VIIRS cloud-top and infrared-derived thermodynamic phase products by combining elements of the NASA MODIS science team (MOD) and the NOAA Algorithm Working Group (AWG) algorithms. The combined approach is referred to as the MODAWG processing package. In collaboration with the NASA Atmospheric SIPS located at the University of Wisconsin Space Science and Engineering Center, the MODAWG code has been exercised on one year of SNPP VIIRS data. In addition to cloud-top and phase, MODAWG provides a full suite of cloud products that are physically consistent with MODIS and have a similar data format. Further, the SIPS has developed tools to allow use of Cross-track Infrared Sounder (CrIS) observations in the MODAWG processing that can ameliorate the loss of the CO2 absorption channels on VIIRS. Examples will be given that demonstrate the positive impact that the CrIS data can provide when combined with VIIRS for cloud height and IR-phase retrievals.

Snow Cover and Vegetation-Induced Decrease in Global Albedo From 2002 to 2016

NASA Astrophysics Data System (ADS)

Li, Qiuping; Ma, Mingguo; Wu, Xiaodan; Yang, Hong

2018-01-01

Land surface albedo is an essential parameter in regional and global climate models, and it is markedly influenced by land cover change. Variations in the albedo can affect the surface radiation budget and further impact the global climate. In this study, the interannual variation of albedo from 2002 to 2016 was estimated on the global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. The presence and causes of the albedo changes for each specific region were also explored. From 2002 to 2016, the MODIS-based albedo decreased globally, snow cover declined by 0.970 (percent per pixel), while the seasonally integrated normalized difference vegetation index increased by 0.175. Some obvious increases in the albedo were detected in Central Asia, northeastern China, parts of the boreal forest in Canada, and the temperate steppe in North America. In contrast, noticeable decreases in the albedo were found in the Siberian tundra, Europe, southeastern Australia, and northeastern regions of North America. In the Northern Hemisphere, the greening trend at high latitudes made more contribution to the decline in the albedo. However, the dramatic fluctuation of snow-cover at midlatitudes predominated in the change of albedo. Our analysis can help to understand the roles that vegetation and snow cover play in the variation of albedo on global and regional scales.

Peat conditions mapping using MODIS time series

NASA Astrophysics Data System (ADS)

Poggio, Laura; Gimona, Alessandro; Bruneau, Patricia; Johnson, Sally; McBride, Andrew; Artz, Rebekka

2016-04-01

Large areas of Scotland are covered in peatlands, providing an important sink of carbon in their near natural state but act as a potential source of gaseous and dissolved carbon emission if not in good conditions. Data on the condition of most peatlands in Scotland are, however, scarce and largely confined to sites under nature protection designations, often biased towards sites in better condition. The best information available at present is derived from labour intensive field-based monitoring of relatively few designated sites (Common Standard Monitoring Dataset). In order to provide a national dataset of peat conditions, the available point information from the CSM data was modelled with morphological features and information derived from MODIS sensor. In particular we used time series of indices describing vegetation greenness (Enhanced Vegetation Index), water availability (Normalised Water Difference index), Land Surface Temperature and vegetation productivity (Gross Primary productivity). A scorpan-kriging approach was used, in particular using Generalised Additive Models for the description of the trend. The model provided the probability of a site to be in favourable conditions and the uncertainty of the predictions was taken into account. The internal validation (leave-one-out) provided a mis-classification error of around 0.25. The derived dataset was then used, among others, in the decision making process for the selection of sites for restoration.

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000-2005

NASA Technical Reports Server (NTRS)

Yang, Wenze; Huang, Dong; Tan, Bin; Stroeve, Julienne C.; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2006-01-01

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

Monitoring Thermal Status of Ecosystems with MODIS Land-Surface Temperature and Vegetation Index Products

NASA Technical Reports Server (NTRS)

Wan, Zhengming

2002-01-01

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 C and day-night LST differences distribute from -2 to 22 C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

[Spatiotemporal characteristics of MODIS NDVI in Hulunber Grassland].

PubMed

Zhang, Hong-Bin; Yang, Gui-Xia; Wu, Wen-Bin; Li, Gang; Chen, Bao-Rui; Xin, Xiao-Ping

2009-11-01

Time-series MODIS NDVI datasets from 2000 to 2008 were used to study the spatial change trend, fluctuation degree, and occurrence time of the annual NDVImax of four typical grassland types, i.e., lowland meadow, temperate steppe, temperate meadow steppe, and upland meadow, in Hulunber Grassland. In 2000-2008, the vegetation in Hulunber Grassland presented an obvious deterioration trend. The mean annual NDVImax of the four grassland types had a great fluctuation, especially in temperate steppe where the maximum change in the mean value of annual NDVImax approximated to 50%. As for the area change of different grade grasslands, the areas with NDVImax between 0.4 and 1 accounted for about 91% of the total grassland area, which suggested the good vegetation coverage in the Grassland. However, though the areas with NDVImax values in (0.4, 0.8) showed an increasing trend, the areas with NDVImax values in (0.2, 0.4) and (0.8, 1) decreased greatly in the study period. Overall, the deteriorating grassland took up about 66.25% of the total area, and the restoring grassland took the rest. There was about 62.85% of the grassland whose NDVImax occurred between the 193rd day and the 225th day in each year, indicating that this period was the most important vegetation growth season in Hulunber Grassland.

Land, Cryosphere, and Nighttime Environmental Products from Suomi NPP VIIRS: Overview and Status

NASA Technical Reports Server (NTRS)

Roman, Miguel O.; Justice, Chris; Csiszar, Ivan

2014-01-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-orbiting Partnership (S-NPP: http://npp.gsfc.nasa.gov/). VIIRS was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provide observation continuity with NASA's Earth Observing System's (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA and NOAA funded scientists have been working to evaluate the instrument performance and derived products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the former National Polar-orbiting Environmental Satellite System (NPOESS). The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs and providing MODIS data product continuity. This paper will present to-date findings of the NASA Science Team's evaluation of the VIIRS Land and Cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization (http://viirsland.gsfc.nasa.gov/index.html). The paper will also discuss new capabilities being developed at NASA's Land Product Evaluation and Test Element (http://landweb.nascom.nasa.gov/NPP_QA/); including downstream data and products derived from the VIIRS Day/Night Band (DNB).

A Universal Ts-VI Triangle Method for the Continuous Retrieval of Evaporative Fraction From MODIS Products

NASA Astrophysics Data System (ADS)

Zhu, Wenbin; Jia, Shaofeng; Lv, Aifeng

2017-10-01

The triangle method based on the spatial relationship between remotely sensed land surface temperature (Ts) and vegetation index (VI) has been widely used for the estimates of evaporative fraction (EF). In the present study, a universal triangle method was proposed by transforming the Ts-VI feature space from a regional scale to a pixel scale. The retrieval of EF is only related to the boundary conditions at pixel scale, regardless of the Ts-VI configuration over the spatial domain. The boundary conditions of each pixel are composed of the theoretical dry edge determined by the surface energy balance principle and the wet edge determined by the average air temperature of open water. The universal triangle method was validated using the EF observations collected by the Energy Balance Bowen Ratio systems in the Southern Great Plains of the United States of America (USA). Two parameterization schemes of EF were used to demonstrate their applicability with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products over the whole year 2004. The results of this study show that the accuracy produced by both of these two parameterization schemes is comparable to that produced by the traditional triangle method, although the universal triangle method seems specifically suited to the parameterization scheme proposed in our previous research. The independence of the universal triangle method from the Ts-VI feature space makes it possible to conduct a continuous monitoring of evapotranspiration and soil moisture. That is just the ability the traditional triangle method does not possess.

Using forest inventory plot data and satellite imagery from MODIS and Landsat-TM to model spatial distribution patterns of honeysuckle and privet

Treesearch

Dumitru Salajanu; Dennis M. Jacobs

2009-01-01

Forest inventory and analysis data monitor the presence and extent of certain non-native invasive species. Onforestland, non-native species are considered part of the understory vegetation and can be found near canopyopenings as well as and...

Convergence of Dynamic Vegetation Net Productivity Responses to Precipitation Variability from 10 Years of MODIS EVI

USDA-ARS?s Scientific Manuscript database

According to Global Climate Models (GCMs) the occurrence of extreme events of precipitation will be more frequent in the future. Therefore, important challenges arise regarding climate variability, which are mainly related to the understanding of ecosystem responses to changes in precipitation patte...

Effect of MODIS Terra Radiometric Calibration Improvements on Collection 6 Deep Blue Aerosol Products: Validation and Terra/Aqua Consistency

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.; Meister, G.

2015-01-01

The Deep Blue (DB) algorithm's primary data product is midvisible aerosol optical depth (AOD). DB applied to Moderate Resolution Imaging Spectroradiometer (MODIS) measurements provides a data record since early 2000 for MODIS Terra and mid-2002 for MODIS Aqua. In the previous data version (Collection 5, C5), DB production from Terra was halted in 2007 due to sensor degradation; the new Collection 6 (C6) has both improved science algorithms and sensor radiometric calibration. This includes additional calibration corrections developed by the Ocean Biology Processing Group to address MODIS Terra's gain, polarization sensitivity, and detector response versus scan angle, meaning DB can now be applied to the whole Terra record. Through validation with Aerosol Robotic Network (AERONET) data, it is shown that the C6 DB Terra AOD quality is stable throughout the mission to date. Compared to the C5 calibration, in recent years the RMS error compared to AERONET is smaller by approximately 0.04 over bright (e.g., desert) and approximately 0.01-0.02 over darker (e.g., vegetated) land surfaces, and the fraction of points in agreement with AERONET within expected retrieval uncertainty higher by approximately 10% and approximately 5%, respectively. Comparisons to the Aqua C6 time series reveal a high level of correspondence between the two MODIS DB data records, with a small positive (Terra-Aqua) average AOD offset <0.01. The analysis demonstrates both the efficacy of the new radiometric calibration efforts and that the C6 MODIS Terra DB AOD data remain stable (to better than 0.01 AOD) throughout the mission to date, suitable for quantitative scientific analyses.

On-Orbit Calibration and Performance of Aqua MODIS Reflective Solar Bands

NASA Technical Reports Server (NTRS)

Xiong, Xiaoxiong; Sun, Junqiang; Xie, Xiaobo; Barnes, William; Salomonson, Vincent

2009-01-01

Aqua MODIS has successfully operated on-orbit for more than 6 years since its launch in May 2002, continuously making global observations and improving studies of changes in the Earth's climate and environment. 20 of the 36 MODIS spectral bands, covering wavelengths from 0.41 to 2.2 microns, are the reflective solar bands (RSB). They are calibrated on-orbit using an on-board solar diffuser (SD) and a solar diffuser stability monitor (SDSM). In addition, regularly scheduled lunar observations are made to track the RSB calibration stability. This paper presents Aqua MODIS RSB on-orbit calibration and characterization activities, methodologies, and performance. Included in this study are characterizations of detector signal-to-noise ratio (SNR), short-term stability, and long-term response change. Spectral wavelength dependent degradation of the SD bidirectional reflectance factor (BRF) and scan mirror reflectance, which also varies with angle of incidence (AOI), are examined. On-orbit results show that Aqua MODIS onboard calibrators have performed well, enabling accurate calibration coefficients to be derived and updated for the Level 1B (L1B) production and assuring high quality science data products to be continuously generated and distributed. Since launch, the short-term response, on a scan-by-scan basis, has remained extremely stable for most RSB detectors. With the exception of band 6, there have been no new RSB noisy or inoperable detectors. Like its predecessor, Terra MODIS, launched in December 1999, the Aqua MODIS visible (VIS) spectral bands have experienced relatively large changes, with an annual response decrease (mirror side 1) of 3.6% for band 8 at 0.412 microns, 2.3% for band 9 at 0.443 microns, 1.6% for band 3 at 0.469 microns, and 1.2% for band 10 at 0.488 microns. For other RSB bands with wavelengths greater than 0.5 microns, the annual response changes are typically less than 0.5%. In general, Aqua MODIS optics degradation is smaller than Terra MODIS and the mirror side differences are much smaller. Overall, Aqua MODIS RSB on-orbit performance is better than Terra MODIS.

Characterization of drought patterns through remote sensing over The Chihuahua Desert, Mexico"

NASA Astrophysics Data System (ADS)

Madrigal, J. M.; Lopez, A.; Garatuza, J.

2013-12-01

Drought is a phenomenon that has intensified during the last few decades in the arid and semi-arid zones of northern Mexico. In the Chihuahua desert, across Chihuahua, Durango and Coahuila states has caused loss of food sustainability (agriculture, livestock), an increase in human health problems, and detriment of ecosystem services as well as important economic losses. In order to understand this phenomenon, it is necessary to create tools that allow monitoring the territory's spatial heterogeneity and multi-temporality. With this purpose we propose the implementation of a drought model which includes the traditional indexes of climatic drought, such as the Palmer Drought Severity Index PDSI, the Standardized Index of Rainfall SPI, data from meteorological stations and biophysical variations obtained from the MODIS sensors product MOD13 NDVI from 2001 to 2010, as well as biophysical variables characteristic of the environment, such as land use and vegetation coverage, Eco-regions, soil moisture, digital elevation model and irrigate agriculture districts. With the MODIS images, a spatially coherent time series was created analyzing the study area's phenology (TIMESAT) created the Seasonal Greenness (SG) and Start of Season Anomaly (SOSA) for the mentioned nine years. Through this, the annual cycles were established. With a decision tree model, all the previously mentioned proposed variables were integrated. The proposed model produces a general map which characterizes the vegetation condition (extreme drought, severe drought, moderate drought, near normal). Even though different techniques have been proposed on the monitoring of droughts, most of them generate drought indexes with a spatial resolution of 1km (Wardlow, B. et. al 2008; Levent T. et al. 2013). One of the main concerns of researchers on the matter is on improving the spatial information content and on having a better representation of the phenomenon. We use the normalized difference vegetation index (NDVI) data acquired by MODIS instead of the Advanced Very High Resolution Radiometer (AVHRR). The results show a better drought pattern characterization over The Chihuahua Desert, Mexico". The future work will consist of making a sensibility and optimization study of the variables used in the CART model, including others such as evapotranspiration and rainfall. Additionally, this work will research on the potential of using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI).

Modelling Spatial Patterns and Drivers of Wildfires in Honduras Using Remote Sensing and Geographic Information Systems

NASA Astrophysics Data System (ADS)

Valdez Vasquez, M. C.; Chen, C. F.; Chiang, S. H.

2016-12-01

Forests in Honduras are one of the most important resources as they provide a wide range of environmental, economic, and social benefits. However, they are endangered as a result of the relentless occurrence of wildfires during the dry season. Despite the knowledge acquired by the population concerning the effects of wildfires, the frequency is increasing, a pattern attributable to the numerous ignition sources linked to human activity. The purpose of this study is to integrate the wildfire occurrences throughout the 2010-2015 period with a series of anthropogenic and non-anthropogenic variables using the random forest algorithm (RF). We use a series of variables that represent the anthropogenic activity, the flammability of vegetation, climatic conditions, and topography. To represent the anthropogenic activity, we included the continuous distances to rivers, roads, and settlements. To characterize the vegetation flammability, we used the normalized difference vegetation index (NDVI) and the normalized multi-band drought index (NMDI) acquired from MODIS surface reflectance data. Additionally, we included the topographical variables elevation, slope, and solar radiation derived from the ASTER global digital elevation model (GDEM V2). To represent the climatic conditions, we employed the land surface temperature (LST) product from the MODIS sensor and the WorldClim precipitation data. We analyzed the explanatory variables through native RF variable importance analysis and jackknife test, and the results revealed that the dry fuel conditions and low precipitation combined with the proximity to non-paved roads were the major drivers of wildfires. Furthermore, we predicted the areas with highest wildfire susceptibility, which are located mainly in the central and eastern regions of the country, within coniferous and mixed forests. Results acquired were validated using the area under the receiver operating characteristic (ROC) curve and the point biserial correlation and both validation metrics showed satisfactory agreement with the test data. Predictions of forest fire risk and its spatial variability are important instruments for proper management and the results acquired can lead to enhanced preventive measures to minimize risk and reduce the impacts caused by wildfires.

Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index.

PubMed

Löw, Fabian; Waldner, François; Latchininsky, Alexandre; Biradar, Chandrashekhar; Bolkart, Maximilian; Colditz, René R

2016-12-01

The Asian Migratory locust (Locusta migratoria migratoria L.) is a pest that continuously threatens crops in the Amudarya River delta near the Aral Sea in Uzbekistan, Central Asia. Its development coincides with the growing period of its main food plant, a tall reed grass (Phragmites australis), which represents the predominant vegetation in the delta and which cover vast areas of the former Aral Sea, which is desiccating since the 1960s. Current locust survey methods and control practices would tremendously benefit from accurate and timely spatially explicit information on the potential locust habitat distribution. To that aim, satellite observation from the MODIS Terra/Aqua satellites and in-situ observations were combined to monitor potential locust habitats according to their corresponding risk of infestations along the growing season. A Random Forest (RF) algorithm was applied for classifying time series of MODIS enhanced vegetation index (EVI) from 2003 to 2014 at an 8-day interval. Based on an independent ground truth data set, classification accuracies of reeds posing a medium or high risk of locust infestation exceeded 89% on average. For the 12-year period covered in this study, an average of 7504 km 2 (28% of the observed area) was flagged as potential locust habitat and 5% represents a permanent high risk of locust infestation. Results are instrumental for predicting potential locust outbreaks and developing well-targeted management plans. The method offers positive perspectives for locust management and treatment of infested sites because it is able to deliver risk maps in near real time, with an accuracy of 80% in April-May which coincides with both locust hatching and the first control surveys. Such maps could help in rapid decision-making regarding control interventions against the initial locust congregations, and thus the efficiency of survey teams and the chemical treatments could be increased, thus potentially reducing environmental pollution while avoiding areas where treatments are most likely to cause environmental degradation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Advances in the Use of In Situ Flux Tower Measures of Canopy Structure and Function for Evaluation of MODIS and VIIRS Vegetation Indices and Phenology

NASA Astrophysics Data System (ADS)

Huete, A. R.; Ma, X.; Devadas, R.; Miura, T.; Obata, K.; Restrepo-Coupe, N.; Kato, A.

2016-12-01

Prior studies have linked tower-based measures of ecosystem productivity (GEP) and satellite vegetation indices (VIs) over primarily phenologically driven ecosystems. In situ flux towers are advantageous for satellite product assessments due to their comparable footprints and finer temporal resolution measurements. However, weaker relationships have been reported in meteorological driven ecosystems in which satellite and flux tower measures of productivity are asynchronous. This suggests that satellite derived biophysical measures are not a measure of GEP, but rather a proxy for ecosystem structure (e.g. LAI) and function. Here we investigate the use of flux tower measures of photosynthetic infrastructure, including photosynthetic capacity and light use efficiency, as more appropriate measures for the evaluation of satellite VI products and phenology. We related MODIS and VIIRS VIs (NDVI, EVI, EVI2) and phenology over a series of flux tower sites located across a climatically-diverse north to south gradient in central Australia, encompassing wet and dry subtropcal savannas, semi-arid shrub/grass, and temperate broadleaf evergreen forests. These represented environmental conditions where phenology and meteorology are not always synchronized. Our objectives were to (1) advance the use of in situ tower data for more accurate evaluation of satellite products and assess challenges and limitations of in situ tower networks; (2) intercompare cross-sensor VI products and derived phenology with tower measures across biomes to assess consistencies; and (3) better understand satellite and tower relationships to improve upon interpretation of satellite VI and phenology data. Our results show, that in contrast to measures of GEP, our measures of photosynthetic infrastructure were much better related to satellite VIs; they removed strong hysteresis influences on VI- productivity relationships across phenologic green-up and brown-down phases; and thus were better related to satellite phenology profiles. We conclude that flux tower networks offer a valuable source of in situ and consistent data that can be used for satellite time series continuity and cross sensor studies. This can also bring forward the validation of vegetation indices beyond simple reflectance-based accuracy assessments.

Calibration Adjustments to the MODIS Aqua Ocean Color Bands

NASA Technical Reports Server (NTRS)

Meister, Gerhard

2012-01-01

After the end of the SeaWiFS mission in 2010 and the MERIS mission in 2012, the ocean color products of the MODIS on Aqua are the only remaining source to continue the ocean color climate data record until the VIIRS ocean color products become operational (expected for summer 2013). The MODIS on Aqua is well beyond its expected lifetime, and the calibration accuracy of the short wavelengths (412nm and 443nm) has deteriorated in recent years_ Initially, SeaWiFS data were used to improve the MODIS Aqua calibration, but this solution was not applicable after the end of the SeaWiFS mission_ In 2012, a new calibration methodology was applied by the MODIS calibration and support team using desert sites to improve the degradation trending_ This presentation presents further improvements to this new approach. The 2012 reprocessing of the MODIS Aqua ocean color products is based on the new methodology.

Challenges of flood monitoring in the Senegal river valley using multi-temporal data

NASA Astrophysics Data System (ADS)

Bruckmann, Laurent; Delbart, Nicolas

2017-04-01

In Sub-Saharan Africa, floodplains wetlands play an important role for livelihoods and economy, especially for agriculture and fishing. However, tropical rivers flows are increasingly modified by climate change and dam regulation. In the Senegal river valley, the annual flood, from August to November, is an important water resources creating ecosystems services for people. Senegal river basin face to hydrological changes, due to rainfall diminution during the 1970's and building of large dams during 1980's to secure water resources. Water management and development of irrigation have modified the floodplain functioning. Flood recession agriculture, grazing and fishing are now confronted to a high uncertainty about floods level, duration and extension. Thus, spatiotemporal information of flood extension and duration are important for local communities and stakeholders to ensure food security and ecosystems services. Multi-temporal satellite data demonstrates an important applicability for flood mapping. Aims of this work is to present potentiality of using multi-temporal data from MODIS and new satellite Sentinel-2 for flood monitoring in a Sahelian context. It will also discuss the potential of flood mapping for the analysis of the dynamics of riparian vegetation and flood recession agriculture. This study uses two datasets to explore flood monitoring in Senegal river valley. Firstly, MODIS 8-days data (MOD09A) are first used, because of its temporal resolution of 8 days covering the period from 2000 to 2016. However, MODIS data are limited due to a low spatial resolution, that's why we also use Sentinel-2 data, available since summer 2015. The data were processed by constructing NDWI time-series (NDWI threshold is empirically defined) and extracting NDWI values for each inundated pixel during flood. First results demonstrate that using MODIS on a large scale is enough for analyze interannual variability of the flooded surfaces. We present here maps of flood frequency of the pixels between 2000 and 2016. MODIS spatial resolution is insufficient to analyze the interaction between flood hydrology and vegetation dynamics, whereas flood monitoring by Sentinel data seems to offer better potential. We illustrate our observations through a cartographic example of these interactions at local scale in Senegal river floodplain.

Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data

NASA Astrophysics Data System (ADS)

Bibi, Humera; Alam, Khan; Chishtie, Farrukh; Bibi, Samina; Shahid, Imran; Blaschke, Thomas

2015-06-01

This study provides an intercomparison of aerosol optical depth (AOD) retrievals from satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation over Karachi, Lahore, Jaipur, and Kanpur between 2007 and 2013, with validation against AOD observations from the ground-based Aerosol Robotic Network (AERONET). Both MODIS Deep Blue (MODISDB) and MODIS Standard (MODISSTD) products were compared with the AERONET products. The MODISSTD-AERONET comparisons revealed a high degree of correlation for the four investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDB-AERONET comparisons revealed even better correlations, and the MISR-AERONET comparisons also indicated strong correlations, as did the OMI-AERONET comparisons, while the CALIPSO-AERONET comparisons revealed only poor correlations due to the limited number of data points available. We also computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB). Using AERONET data to validate MODISSTD, MODISDB, MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over vegetated locations than over un-vegetated locations, while MISR data was more accurate over areas close to the ocean than over other areas. The MISR instrument performed better than the other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better than those from the other instruments over Lahore and Jaipur. We also computed the expected error bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently outperformed MODISDB in all of the investigated areas. High AOD values were observed by the MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (April-August); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast, high AOD values were observed by the CALIPSO instrument between September and December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the AERONET data, but underestimation by the MISR instrument.

MITRA Virtual laboratory for operative application of satellite time series for land degradation risk estimation

NASA Astrophysics Data System (ADS)

Nole, Gabriele; Scorza, Francesco; Lanorte, Antonio; Manzi, Teresa; Lasaponara, Rosa

2015-04-01

This paper aims to present the development of a tool to integrate time series from active and passive satellite sensors (such as of MODIS, Vegetation, Landsat, ASTER, COSMO, Sentinel) into a virtual laboratory to support studies on landscape and archaeological landscape, investigation on environmental changes, estimation and monitoring of natural and anthropogenic risks. The virtual laboratory is composed by both data and open source tools specifically developed for the above mentioned applications. Results obtained for investigations carried out using the implemented tools for monitoring land degradation issues and subtle changes ongoing on forestry and natural areas are herein presented. In detail MODIS, SPOT Vegetation and Landsat time series were analyzed comparing results of different statistical analyses and the results integrated with ancillary data and evaluated with field survey. The comparison of the outputs we obtained for the Basilicata Region from satellite data analyses and independent data sets clearly pointed out the reliability for the diverse change analyses we performed, at the pixel level, using MODIS, SPOT Vegetation and Landsat TM data. Next steps are going to be implemented to further advance the current Virtual Laboratory tools, by extending current facilities adding new computational algorithms and applying to other geographic regions. Acknowledgement This research was performed within the framework of the project PO FESR Basilicata 2007/2013 - Progetto di cooperazione internazionale MITRA "Remote Sensing tecnologies for Natural and Cultural heritage Degradation Monitoring for Preservation and valorization" funded by Basilicata Region Reference 1. A. Lanorte, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance International Journal of Applied Earth Observation and Geoinformation 2441-446 2. G Calamita, A Lanorte, R Lasaponara, B Murgante, G Nole 2013 Analyzing urban sprawl applying spatial autocorrelation techniques to multi-temporal satellite data. Urban and Regional Data Management: UDMS Annual 2013, 161 3. R Lasaponara 2013 Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation International Journal of Applied Earth Observations and Geoinformation 20 . 1-3 4. R Lasaponara, A Lanorte 2011 Satellite time-series analysis International Journal of Remote Sensing 33 (15), 4649-4652 5. G Nolè, M Danese, B Murgante, R Lasaponara, A Lanorte Using spatial autocorrelation techniques and multi-temporal satellite data for analyzing urban sprawl Computational Science and Its Applications-ICCSA 2012, 512-527

The Zoning of Forest Fire Potential of Gulestan Province Forests Using Granular Computing and MODIS Images

NASA Astrophysics Data System (ADS)

Jalilzadeh Shadlouei, A.; Delavar, M. R.

2013-09-01

There are many vegetation in Iran. This is because of extent of Iran and its width. One of these vegetation is forest vegetation most prevalent in Northern provinces named Guilan, Mazandaran, Gulestan, Ardebil as well as East Azerbaijan. These forests are always threatened by natural forest fires so much so that there have been reports of tens of fires in recent years. Forest fires are one of the major environmental as well as economic, social and security concerns in the world causing much damages. According to climatology, forest fires are one of the important factors in the formation and dispersion of vegetation. Also, regarding the environment, forest fires cause the emission of considerable amounts of greenhouse gases, smoke and dust into the atmosphere which in turn causes the earth temperature to rise up and are unhealthy to humans, animals and vegetation. In agriculture droughts are the usual side effects of these fires. The causes of forest fires could be categorized as either Human or Natural Causes. Naturally, it is impossible to completely contain forest fires; however, areas with high potentials of fire could be designated and analysed to decrease the risk of fires. The zoning of forest fire potential is a multi-criteria problem always accompanied by inherent uncertainty like other multi-criteria problems. So far, various methods and algorithm for zoning hazardous areas via Remote Sensing (RS) and Geospatial Information System (GIS) have been offered. This paper aims at zoning forest fire potential of Gulestan Province of Iran forests utilizing Remote Sensing, Geospatial Information System, meteorological data, MODIS images and granular computing method. Granular computing is part of granular mathematical and one way of solving multi-criteria problems such forest fire potential zoning supervised by one expert or some experts , and it offers rules for classification with the least inconsistencies. On the basis of the experts' opinion, 6 determinative criterias contributing to forest fires have been designated as follows: vegetation (NDVI), slope, aspect, temperature, humidity and proximity to roadways. By applying these variables on several tentatively selected areas and formation information tables and producing granular decision tree and extraction of rules, the zoning rules (for the areas in question) were extracted. According to them the zoning of the entire area has been conducted. The zoned areas have been classified into 5 categories: high hazard, medium hazard (high), medium hazard (low), low hazard (high), low hazard (low). According to the map, the zoning of most of the areas fall into the low hazard (high) class while the least number of areas have been classified as low hazard (low). Comparing the forest fires in these regions in 2010 with the MODIS data base for forest fires, it is concluded that areas with high hazards of forest fire have been classified with a 64 percent precision. In other word 64 percent of pixels that are in high hazard classification are classified according to MODIS data base. Using this method we obtain a good range of Perception. Manager will reduce forest fire concern using precautionary proceeding on hazardous area.

Developing a Dynamic SPARROW Water Quality Decision Support System Using NASA Remotely-Sensed Products

NASA Astrophysics Data System (ADS)

Al-Hamdan, M. Z.; Smith, R. A.; Hoos, A.; Schwarz, G. E.; Alexander, R. B.; Crosson, W. L.; Srikishen, J.; Estes, M., Jr.; Cruise, J.; Al-Hamdan, A.; Ellenburg, W. L., II; Flores, A.; Sanford, W. E.; Zell, W.; Reitz, M.; Miller, M. P.; Journey, C. A.; Befus, K. M.; Swann, R.; Herder, T.; Sherwood, E.; Leverone, J.; Shelton, M.; Smith, E. T.; Anastasiou, C. J.; Seachrist, J.; Hughes, A.; Graves, D.

2017-12-01

The USGS Spatially Referenced Regression on Watershed Attributes (SPARROW) surface water quality modeling system has been widely used for long term, steady state water quality analysis. However, users have increasingly requested a dynamic version of SPARROW that can provide seasonal estimates of nutrients and suspended sediment to receiving waters. The goal of this NASA-funded project is to develop a dynamic decision support system to enhance the southeast SPARROW water quality model and finer-scale dynamic models for selected coastal watersheds through the use of remotely-sensed data and other NASA Land Information System (LIS) products. The spatial and temporal scale of satellite remote sensing products and LIS modeling data make these sources ideal for the purposes of development and operation of the dynamic SPARROW model. Remote sensing products including MODIS vegetation indices, SMAP surface soil moisture, and OMI atmospheric chemistry along with LIS-derived evapotranspiration (ET) and soil temperature and moisture products will be included in model development and operation. MODIS data will also be used to map annual land cover/land use in the study areas and in conjunction with Landsat and Sentinel to identify disturbed areas that might be sources of sediment and increased phosphorus loading through exposure of the bare soil. These data and others constitute the independent variables in a regression analysis whose dependent variables are the water quality constituents total nitrogen, total phosphorus, and suspended sediment. Remotely-sensed variables such as vegetation indices and ET can be proxies for nutrient uptake by vegetation; MODIS Leaf Area Index can indicate sources of phosphorus from vegetation; soil moisture and temperature are known to control rates of denitrification; and bare soil areas serve as sources of enhanced nutrient and sediment production. The enhanced SPARROW dynamic models will provide improved tools for end users to manage water quality in near real time and for the formulation of future scenarios to inform strategic planning. Time-varying SPARROW outputs will aid water managers in decision making regarding allocation of resources in protecting aquatic habitats, planning for harmful algal blooms, and restoration of degraded habitats, stream segments, or lakes.

Extracting Vegetation Coverage in Dry-hot Valley Regions Based on Alternating Angle Minimum Algorithm

NASA Astrophysics Data System (ADS)

Y Yang, M.; Wang, J.; Zhang, Q.

2017-07-01

Vegetation coverage is one of the most important indicators for ecological environment change, and is also an effective index for the assessment of land degradation and desertification. The dry-hot valley regions have sparse surface vegetation, and the spectral information about the vegetation in such regions usually has a weak representation in remote sensing, so there are considerable limitations for applying the commonly-used vegetation index method to calculate the vegetation coverage in the dry-hot valley regions. Therefore, in this paper, Alternating Angle Minimum (AAM) algorithm of deterministic model is adopted for selective endmember for pixel unmixing of MODIS image in order to extract the vegetation coverage, and accuracy test is carried out by the use of the Landsat TM image over the same period. As shown by the results, in the dry-hot valley regions with sparse vegetation, AAM model has a high unmixing accuracy, and the extracted vegetation coverage is close to the actual situation, so it is promising to apply the AAM model to the extraction of vegetation coverage in the dry-hot valley regions.

Rapid dispersal of saltcedar (Tamarix spp.) biocontrol beetles (Diorhabda carinulata) on a desert river detected by phenocams, MODIS imagery and ground observations

USGS Publications Warehouse

Nagler, Pamela L.; Pearlstein, Susanna; Glenn, Edward P.; Brown, Tim B.; Bateman, Heather L.; Bean, Dan W.; Hultine, Kevin R.

2013-01-01

We measured the rate of dispersal of saltcedar leaf beetles (Diorhabda carinulata), a defoliating insect released on western rivers to control saltcedar shrubs (Tamarix spp.), on a 63 km reach of the Virgin River, U.S. Dispersal was measured by satellite imagery, ground surveys and phenocams. Pixels from the Moderate Resolution Imaging Spectrometer (MODIS) sensors on the Terra satellite showed a sharp drop in NDVI in midsummer followed by recovery, correlated with defoliation events as revealed in networked digital camera images and ground surveys. Ground surveys and MODIS imagery showed that beetle damage progressed downstream at a rate of about 25 km yr−1 in 2010 and 2011, producing a 50% reduction in saltcedar leaf area index and evapotranspiration by 2012, as estimated by algorithms based on MODIS Enhanced Vegetation Index values and local meteorological data for Mesquite, Nevada. This reduction is the equivalent of 10.4% of mean annual river flows on this river reach. Our results confirm other observations that saltcedar beetles are dispersing much faster than originally predicted in pre-release biological assessments, presenting new challenges and opportunities for land, water and wildlife managers on western rivers. Despite relatively coarse resolution (250 m) and gridding artifacts, single MODIS pixels can be useful in tracking the effects of defoliating insects in riparian corridors.

First Complete Day from MODIS

NASA Technical Reports Server (NTRS)

2002-01-01

This spectacular, full-color image of the Earth is a composite of the first full day of data gathered by the Moderate-resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra spacecraft. MODIS collected the data for each wavelength of red, green, and blue light as Terra passed over the daylit side of the Earth on April 19, 2000. Terra is orbiting close enough to the Earth so that it cannot quite see the entire surface in a day, resulting in the narrow gaps around the equator. Although the sensor's visible channels were combined to form this true-color picture, MODIS collects data in a total of 36 wavelengths, ranging from visible to thermal infrared energy. Scientists use these data to measure regional and global-scale changes in marine and land-based plant life, sea and land surface temperatures, cloud properties, aerosols, fires, and land surface properties. Notice how cloudy the Earth is, and the large differences in brightness between clouds, deserts, oceans, and forests. The Antarctic, surrounded by clockwise swirls of cloud, is shrouded in darkness because the sun is north of the equator at this time of year. The tropical forests of Africa, Southeast Asia, and South America are shrouded by clouds. The bright Sahara and Arabian deserts stand out clearly. Green vegetation is apparent in the southeast United States, the Yucatan Peninsula, and Madagascar. Image by Mark Gray, MODIS Atmosphere Team, NASA GSFC

PERSPECTIVE Working towards a community-wide understanding of satellite skin temperature observations

NASA Astrophysics Data System (ADS)

Shreve, Cheney

2010-12-01

With more than sixty free and publicly available high-quality datasets, including ecosystem variables, radiation budget variables, and land cover products, the MODIS instrument and the MODIS scientific team have contributed significantly to scientific investigations of ecosystems across the globe. The MODIS instrument, launched in December 1999, has 36 spectral bands, a viewing swath of 2330 km, and acquires data at 250 m, 500 m, and 1000 m spatial resolution every one to two days. Radiation budget variables include surface reflectance, skin temperature, emissivity, and albedo, to list a few. Ecosystem variables include several vegetation indices and productivity measures. Land cover characteristics encompass land cover classifications as well as model parameters and vegetation classifications. Many of these products are instrumental in constraining global climate models and climate change studies, as well as monitoring events such as the recent flooding in Pakistan, the unprecedented oil spill in the Gulf of Mexico, or phytoplankton bloom in the Barents Sea. While product validation efforts by the MODIS scientific team are both vigorous and continually improving, validation is unquestionably one of the most difficult tasks when dealing with remotely derived datasets, especially at the global scale. The quality and availability of MODIS data have led to widespread usage in the scientific community that has further contributed to validation and development of the MODIS products. In their recent paper entitled 'Land surface skin temperature climatology: benefitting from the strengths of satellite observations', Jin and Dickinson review the scientific theory behind, and demonstrate application of, a MODIS temperature product: surface skin temperature. Utilizing datasets from the Global Historical Climatological Network (GHCN), daily skin and air temperature from the Atmospheric Radiation Measurement (ARM) program, and MODIS products (skin temperature, albedo, land cover, water vapor, cloud cover), they show that skin temperature is clearly a different physical parameter from air temperature and varies from air temperature in magnitude, response to atmospheric conditions, and diurnal phase. Although the accuracy of skin temperature (Tskin) algorithms has improved to within 0.5-1°C for field measurements and clear-sky satellite observations (Becker and Li 1995, Goetz et al 1995, Wan and Dozier 1996), general confusion regarding the physical definition of 'surface temperature' and how it can be used for climate studies has persisted throughout the scientific community and limited the applications of these data (Jin and Dickinson 2010). For example, satellite sea surface temperature was used as evidence of global climate change instead of skin temperature in the IPCC 2001 and 2007 reports (Jin and Dickinson 2010). This work provides clarity in the theoretical definition of temperature variables, demonstrates the difference between air and skin temperature, and aids the understanding of the MODIS Tskin product, which could be very beneficial for future climate studies. As outlined by Jin and Dickinson, 'surface temperature' is a vague term commonly used in reference to air temperature, aerodynamic temperature, and skin temperature. Air temperature (Tair), or thermodynamic temperature, is measured by an in situ instrument usually 1.5-2 m above the ground. Aerodynamic temperature (Taero) refers to the temperature at the height of the roughness length of heat. Satellite derived skin temperature (Tskin) is the radiometric temperature derived from the inverse of Planck's function. While these different temperature variables are typically correlated, they differ as a result of environmental conditions (e.g. land cover and sky conditions; Jin and Dickinson 2010). With an extensive network of Tair measurements, some have questioned the benefits of using Tskin at all (Peterson et al 1997, 1998). Tskin and Tair can vary depending on land cover or sky conditions and variations may be large, e.g., for sparsely vegetated areas where net radiation is largely balanced by sensible heat flux (Hall et al 1992, Sun and Mahrt 1995, Jin et al 1997). Tskin can be higher than Taero at midday and lower at night (Sun and Mahrt 1995) and some models use Taero to approximate surface radiative temperature (Hubband and Monteith 1986). One of the strengths of the MODIS instrument is the simultaneous collection of surface and atmospheric conditions. By incorporating a range of MODIS variables in their comparison to Tskin, the authors examine the relationship of Tskin to atmospheric and surface conditions. Results from their global evaluation of Tskin highlight its variability on an inter-annual basis, its variation with solar zenith angle, and diurnal variations, which are not achievable with Tair measurements. Comparison with land cover type illustrates the seasonality of Tskin for different land covers. Comparison with the enhanced vegetation index (EVI) suggests more vegetation reduces skin temperature. Using the MODIS albedo, they demonstrate a clear relationship between yearly averaged Tskin and land surface albedo. Lastly, their examination of water vapor and cloud cover in comparison to Tskin suggests similar seasonality between these two variables. The MODIS Tskin product is not without uncertainty; retrieving Tskin requires a calculation of radiative transfer to account for atmospheric emission and molecular absorption, which is time and resource intensive (Jin and Dickinson 2010). Additionally, surface emissivity, instrument noise, and view angle geometry contribute to error in Tskin estimations (Jin and Dickinson 2010). The transparency of the scientific theory underlying this work, and the clear demonstration of the distinction between temperature measures on varying scales, demonstrates the usefulness of Tskin despite the uncertainties. Perhaps equally as important is the tone; in a time when the controversy surrounding climate change is peaking and the very ethics of the scientific community are being questioned, it is more critical than ever to be transparent in one's work and to assist the scientific community in understanding the tools we have available to us for investigating climate change. References Becker F and Li Z-L 1995 Surface temperature and emissivity at different scales: definition, measurement and related problems Remote Sensing Rev. 12 225-53 Goetz S J, Halthore R, Hall F G and Markham B L 1995 Surface temperature retrieval in a temperate grassland with multi-resolution sensors J. Geophys. Res. Atmos. 100 25397-410 Hall F G, Huemmrich K F, Goetz P J, Sellers P J and Nickeson J E 1992 Satellite remote sensing of the surface energy balance: success, failures and unresolved issues in FIFE J. Geophys. Res. Atmos. 97 19061-90 Jin M and Dickinson R E 2010 Land surface skin temperature climatology: benefitting from the strengths of satellite observations Environ. Res. Lett. 5 044004 Jin M, Dickinson R E and Vogelmann A M 1997 A comparison of CCM2/BATS skin temperature and surface-air temperature with satellite and surface observations J. Climate 10 1505-24 Hubband N D S and Monteith J L 1986 Radiative surface temperature and energy balance of a wheat canopy Boundary Layer Meteorol. 36 107-16 Peterson T C and Vose R S 1997 An overview of the Global Historical Climatology Network temperature data base Bull. Am. Meteorol. Soc. 78 2837-49 Peterson T C, Karl T R, Jamason P F, Knight R and Easterling D R 1998 The first difference method: maximizing station density for the calculation of long-term global temperature change J. Geophys. Res. Atmos. 103 25967-74 Sun J and Mahrt L 1995 Determination of surface fluxes from the surface radiative temperature Atmos. Sci. 52 1096-106 Wan Z and Dozier J 1996 A generalized split-window algorithm for retrieving land-surface temperature from space IEEE Trans. Geosci. Remote Sensing 34 892-905

Improving Aerosol and Visibility Forecasting Capabilities Using Current and Future Generations of Satellite Observations

DTIC Science & Technology

2015-08-27

Shi et al. (2011) suggested that large spatial discrepancies exist in operational satellite aerosol products such as MODIS and MISR. Thus, before...to be fully evaluated. In the past few years of the project period, MODIS Deep Blue (DB) and MISR aerosol products have been studied, and schemes for...constructing DA-grade MODIS DB and MISR aerosol products have been developed and transitioned to the Naval Research Laboratory (NRL). Continuing

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research.

PubMed

Liu, R; Chen, J M; Liu, J; Deng, F; Sun, R

2007-11-01

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.

Satellite observed widespread decline in Mongolian grasslands largely due to overgrazing.

PubMed

Hilker, Thomas; Natsagdorj, Enkhjargal; Waring, Richard H; Lyapustin, Alexei; Wang, Yujie

2014-02-01

The Mongolian Steppe is one of the largest remaining grassland ecosystems. Recent studies have reported widespread decline of vegetation across the steppe and about 70% of this ecosystem is now considered degraded. Among the scientific community there has been an active debate about whether the observed degradation is related to climate, or over-grazing, or both. Here, we employ a new atmospheric correction and cloud screening algorithm (MAIAC) to investigate trends in satellite observed vegetation phenology. We relate these trends to changes in climate and domestic animal populations. A series of harmonic functions is fitted to Moderate Resolution Imaging Spectroradiometer (MODIS) observed phenological curves to quantify seasonal and inter-annual changes in vegetation. Our results show a widespread decline (of about 12% on average) in MODIS observed normalized difference vegetation index (NDVI) across the country but particularly in the transition zone between grassland and the Gobi desert, where recent decline was as much as 40% below the 2002 mean NDVI. While we found considerable regional differences in the causes of landscape degradation, about 80% of the decline in NDVI could be attributed to increase in livestock. Changes in precipitation were able to explain about 30% of degradation across the country as a whole but up to 50% in areas with denser vegetation cover (P < 0.05). Temperature changes, while significant, played only a minor role (r(2)  = 0.10, P < 0.05). Our results suggest that the cumulative effect of overgrazing is a primary contributor to the degradation of the Mongolian steppe and is at least partially responsible for desertification reported in previous studies. © 2013 John Wiley & Sons Ltd.

Trend analysis of GIMMS and MODIS NDVI time series for establishing a land degradation neutrality national baseline

NASA Astrophysics Data System (ADS)

Gichenje, Helene; Godinho, Sergio

2017-04-01

Land degradation is a key global environment and development problem that is recognized as a priority by the international development community. The Sustainable Development Goals (SDGs) were adopted by the global community in 2015, and include a goal related to land degradation and the accompanying target to achieve a land degradation-neutral (LDN) world by 2030. The LDN concept encompasses two joint actions of reducing the rate of degradation and increasing the rate of restoration. Using Kenya as the study area, this study aims to develop and test a spatially explicit methodology for assessing and monitoring the operationalization of a land degradation neutrality scheme at the national level. Time series analysis is applied to Normalized Difference Vegetation Index (NDVI) satellite data records, based on the hypothesis that the resulting NDVI residual trend would enable successful detection of changes in vegetation photosynthetic capacity and thus serve as a proxy for land degradation and regeneration processes. Two NDVI data sets are used to identify the spatial and temporal distribution of degraded and regenerated areas: the long term coarse resolution (8km, 1982-2015) third generation Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data record; and the shorter-term finer resolution (250m, 2001-2015) Moderate Resolution Imaging Spectroradiometer (MODIS) derived NDVI data record. Climate data (rainfall, temperature and soil moisture) are used to separate areas of human-induced vegetation productivity decline from those driven by climate dynamics. Further, weekly vegetation health (VH) indexes (4km, 1982-2015) developed by National Oceanic and Atmospheric Administration (NOAA), are assessed as indicators for early detection and monitoring of land degradation by estimating vegetation stress (moisture, thermal and combined conditions).

Predicted Water and Carbon Fluxes as well as Vegetation Distribution on the Korean Peninsula in the Future with the Ecosystem Demography Model version 2

NASA Astrophysics Data System (ADS)

Kim, J. B.; Kim, Y.

2017-12-01

This study investigates how the water and carbon fluxes as well as vegetation distribution on the Korean peninsula would vary with climate change. Ecosystem Demography (ED) Model version 2 (ED2) is used in this study, which is an integrated terrestrial biosphere model that can utilize a set of size- and age- structured partial differential equations that track the changing structure and composition of the plant canopy. With using the vegetation distribution data of Jeju Island, located at the southern part of the Korean Peninsula, ED2 is setup and driven for the past 10 years. Then the results of ED2 are evaluated and adjusted with observed forestry data, i.e., growth and mortality, and the flux tower and MODIS satellite data, i.e., evapotranspiration (ET) and gross primary production (GPP). This adjusted ED2 are used to simulate the water and carbon fluxes as well as vegetation dynamics in the Korean Peninsula for the historical period with evaluating the model against the MODIS satellite data. Finally, the climate scenarios of RCP 2.6 and 6.0 are used to predict the fluxes and vegetation distribution of the Korean Peninsula in the future. With using the state-of-art terrestrial ecosystem model, this study would provide us better understanding of the future ecosystem vulnerability of the Korean Peninsula. AcknowledgementsThis work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A2A01054800) and by the Korea Meteorological Administration R&D Program under Grant KMIPA 2015-6180. This work was also supported by the Yonsei University Future-leading Research Initiative of 2015(2016-22-0061).

Time series analysis of satellite multi-sensors imagery to study the recursive abnormal grow of floating macrophyte in the lake victoria (central Africa)

NASA Astrophysics Data System (ADS)

Fusilli, Lorenzo; Cavalli, Rosa Maria; Laneve, Giovanni; Pignatti, Stefano; Santilli, Giancarlo; Santini, Federico

2010-05-01

Remote sensing allows multi-temporal mapping and monitoring of large water bodies. The importance of remote sensing for wetland and inland water inventory and monitoring at all scales was emphasized several times by the Ramsar Convention on Wetlands and from EU projects like SALMON and ROSALMA, e.g. by (Finlayson et al., 1999) and (Lowry and Finlayson, 2004). This paper aims at assessing the capability of time series of satellite imagery to provide information suitable for enhancing the understanding of the temporal cycles shown by the macrophytes growing in order to support the monitor and management of the lake Victoria water resources. The lake Victoria coastal areas are facing a number of challenges related to water resource management which include growing population, water scarcity, climate variability and water resource degradation, invasive species, water pollution. The proliferation of invasive plants and aquatic weeds, is of growing concern. In particular, let us recall some of the problems caused by the aquatic weeds growing: Ø interference with human activities such as fishing, and boating; Ø inhibition or interference with a balanced fish population; Ø fish killing due to removal of too much oxygen from the water; Ø production of quiet water areas that are ideal for mosquito breeding. In this context, an integrated use of medium/high resolution images from sensors like MODIS, ASTER, LANDSAT/TM and whenever available CHRIS offers the possibility of creating a congruent time series allowing the analysis of the floating vegetation dynamic on an extended temporal basis. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution, further its spatial resolution can results not always adequate to map the extension of floating plants. Therefore, the integrated use of sensors with different spatial resolution, were used to map across seasons the evolution of the phenomena. The integrated use of satellite resources allowed the estimate of the temporal variability of physical parameters that were used to i) sample the spatio-temporal distribution of the whole floating vegetation (i.e. native vegetation and weed) and ii) assess the seasonal recurrence of the abnormal weeds grow, as well as, their possible relation with the hydrological regimes of the rivers. The paper describes how the 2000 - 2009 MODIS images time series, were analysed (navigated and processed) to derive i) the map the floating vegetation on the test area and ii) identify the areas more interested by the growing iii) to discriminate, whenever possible, according to the spectral and spatial resolution of the sensor applied (i.e. LANDSAT, ASTER, CHRIS), the different vegetation species in order to discriminate the weeds from the floating vegetation. The spectral identification of the different species was performed by exploiting the results of a field campaign performed in the past along the Kenyan coastal areas devoted to define a data base of spectral signatures of the main species. Spectral information was treated to define indexes and spectral analysis procedure customized to multispectral high resolution satellite data. Moreover, the results of the images time series has been analysed to identify a possible definition of the temporal occurrence of the floating vegetation growing considering both the natural phenomenological cycles and the conditions related to the abnormal growing. These results, whenever related to ancillary hydrological information (e.g. the amount of rain), they have shown that the synergy of MODIS images time series with lower temporal frequency time series imagery is a powerful tool to monitor the lake Victoria ecosystem and to follow the floating vegetation extension and even to foresee the possibility to set up a model for the abnormal vegetation growing.

Application of MODIS GPP to Forecast Risk of Hantavirus Pulmonary Syndrome Based on Fluctuations in Reservoir Population Density

NASA Astrophysics Data System (ADS)

Loehman, R.; Heinsch, F. A.; Mills, J. N.; Wagoner, K.; Running, S.

2003-12-01

Recent predictive models for hantavirus pulmonary syndrome (HPS) have used remotely sensed spectral reflectance data to characterize risk areas with limited success. We present an alternative method using gross primary production (GPP) from the MODIS sensor to estimate the effects of biomass accumulation on population density of Peromyscus maniculatus (deer mouse), the principal reservoir species for Sin Nombre virus (SNV). The majority of diagnosed HPS cases in North America are attributed to SNV, which is transmitted to humans through inhalation of excretions and secretions from infected rodents. A logistic model framework is used to evaluate MODIS GPP, temperature, and precipitation as predictors of P. maniculatus density at established trapping sites across the western United States. Rodent populations are estimated using monthly minimum number alive (MNA) data for 2000 through 2002. Both local meteorological data from nearby weather stations and 1.25 degree x 1 degree gridded data from the NASA DAO were used in the regression model to determine the spatial sensitivity of the response. MODIS eight-day GPP data (1-km resolution) were acquired and binned to monthly average and monthly sum GPP for 3km x 3km grids surrounding each rodent trapping site. The use of MODIS GPP to forecast HPS risk may result in a marked improvement over past reflectance-based risk area characterizations. The MODIS GPP product provides a vegetation dynamics estimate that is unique to disease models, and targets the fundamental ecological processes responsible for increased rodent density and amplified disease risk.

Evaluation of Operational Albedo Algorithms For AVHRR, MODIS and VIIRS: Case Studies in Southern Africa

NASA Astrophysics Data System (ADS)

Privette, J. L.; Schaaf, C. B.; Saleous, N.; Liang, S.

2004-12-01

Shortwave broadband albedo is the fundamental surface variable that partitions solar irradiance into energy available to the land biophysical system and energy reflected back into the atmosphere. Albedo varies with land cover, vegetation phenological stage, surface wetness, solar angle, and atmospheric condition, among other variables. For these reasons, a consistent and normalized albedo time series is needed to accurately model weather, climate and ecological trends. Although an empirically-derived coarse-scale albedo from the 20-year NOAA AVHRR record (Sellers et al., 1996) is available, an operational moderate resolution global product first became available from NASA's MODIS sensor. The validated MODIS product now provides the benchmark upon which to compare albedo generated through 1) reprocessing of the historic AVHRR record and 2) operational processing of data from the future National Polar-Orbiting Environmental Satellite System's (NPOESS) Visible/Infrared Imager Radiometer Suite (VIIRS). Unfortunately, different instrument characteristics (e.g., spectral bands, spatial resolution), processing approaches (e.g., latency requirements, ancillary data availability) and even product definitions (black sky albedo, white sky albedo, actual or blue sky albedo) complicate the development of the desired multi-mission (AVHRR to MODIS to VIIRS) albedo time series -- a so-called Climate Data Record. This presentation will describe the different albedo algorithms used with AVHRR, MODIS and VIIRS, and compare their results against field measurements collected over two semi-arid sites in southern Africa. We also describe the MODIS-derived VIIRS proxy data we developed to predict NPOESS albedo characteristics. We conclude with a strategy to develop a seamless Climate Data Record from 1982- to 2020.

MODIS-VIIRS Intercalibration for Dark Target Aerosol Retrieval Over Ocean

NASA Astrophysics Data System (ADS)

Sawyer, V. R.; Levy, R. C.; Mattoo, S.; Quinn, G.; Veglio, P.

2016-12-01

Any future climate record for satellite aerosol retrieval will require continuity over multiple decades, longer than the lifespan of an individual satellite instrument. The Dark Target algorithm was developed for MODIS, which began taking observations in 1999; the two MODIS instruments currently in orbit are not expected to continue taking observations beyond the early 2020s. However, the algorithm is portable, and a Dark Target product for VIIRS is scheduled for release December 2016. Because MODIS and VIIRS operate at different wavelengths, resolutions, fields of view and orbital timing, the transition can introduce artifacts that must be corrected. Without these corrections, it will be difficult to find any changes that may occur in the global aerosol climate record over time periods that span the transition from MODIS to VIIRS retrievals. The University of Wisconsin-Madison SIPS team found thousands of matches between 2012 and 2016 in which Aqua-MODIS and Suomi-NPP VIIRS observe the same location at similar times and view angles. These matched cases are used to identify corresponding matches in the Intermediate File Format (IFF) aerosol retrievals for MODIS and VIIRS, which are compared to one another in turn. Because most known sources of disagreement between the two instruments have already been corrected during the IFF retrieval, the direct comparison between near-collocated cases shows only the differences that remain at local and regional scales. The comparison is further restricted to clear-sky cases over ocean, so that the investigation of seasonal, diurnal and geographic variation is not affected by uncertainties in the land surface or cloud contamination.

Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data

USGS Publications Warehouse

Gu, Yingxin; Brown, Jesslyn F.; Miura, Tomoaki; van Leeuwen, Willem J.D.; Reed, Bradley C.

2010-01-01

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

Using Multiple Endmember Spectral Mixture Analysis of MODIS Data for Computing the Fire Potential Index in Southern California

NASA Astrophysics Data System (ADS)

Schneider, P.; Roberts, D. A.

2007-12-01

The Fire Potential Index (FPI) is currently the only operationally used wildfire susceptibility index in the United States that incorporates remote sensing data in addition to meteorological information. Its remote sensing component utilizes relative greenness derived from a NDVI time series as a proxy for computing the ratio of live to dead vegetation. This study investigates the potential of Multiple Endmember Spectral Mixture Analysis (MESMA) as a more direct and physically reasonable way of computing the live ratio and applying it for the computation of the FPI. A time series of 16-day reflectance composites of Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to perform the analysis. Endmember selection for green vegetation (GV), non- photosynthetic vegetation (NPV) and soil was performed in two stages. First, a subset of suitable endmembers was selected from an extensive library of reference and image spectra for each class using Endmember Average Root Mean Square Error (EAR), Minimum Average Spectral Angle (MASA) and a count-based technique. Second, the most appropriate endmembers for the specific data set were selected from the subset by running a series of 2-endmember models on representative images and choosing the ones that modeled the majority of pixels. The final set of endmembers was used for running MESMA on southern California MODIS composites from 2000 to 2006. 3- and 4-endmember models were considered. The best model was chosen on a per-pixel basis according to the minimum root mean square error of the models at each level of complexity. Endmember fractions were normalized by the shade endmember to generate realistic fractions of GV and NPV. In order to validate the MESMA-derived GV fractions they were compared against live ratio estimates from RG. A significant spatial and temporal relationship between both measures was found, indicating that GV fraction has the potential to substitute RG in computing the FPI. To further test this hypothesis the live ratio estimates obtained from MESMA were used to compute daily FPI maps for southern California from 2001 to 2006. A validation with historical wildfire data from the MODIS Active Fire product was carried out over the same time period using logistic regression. Initial results show that MESMA-derived GV fraction can be used successfully for generating FPI maps of southern California.

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

NASA Astrophysics Data System (ADS)

Johnson, David M.

2016-10-01

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008-2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

Using ALS and MODIS data to evaluate degradation in different forests types over the Xingu basin - Brazilian Amazon

NASA Astrophysics Data System (ADS)

Moura, Y.; Aragão, L. E.; Galvão, L. S.; Dalagnol, R.; Lyapustin, A.; Santos, E. G.; Espirito-Santo, F.

2017-12-01

Degradation of Amazon rainforests represents a vital threat to carbon storage, climate regulation and biodiversity; however its effect on tropical ecosystems is largely unknown. In this study we evaluate the effects of forest degradation on forest structure and functioning over the Xingu Basin in the Brazilian Amazon. The vegetation types in the area is dominated by Open Ombrophilous Forest (Asc), Semi-decidiuous Forest (Fse) and Dense Ombrophilous Forest (Dse). We used Airborne Laser Scanning (ALS) data together with time series of optical remote sensing images from the Moderate Resolution Imaging Spectroradiometer (MODIS) bi-directional corrected using the Multi-Angle Implementation for Atmospheric Correction (MAIAC). We derive time-series (2008 to 2016) of the Enhanced Vegetation Index (EVI) and Green-Red Normalized Difference (GRND) to analyze the dynamics of degraded areas with related changes in canopy structure and greenness values, respectively. Airborne ALS measurements showed the largest tree heights in the Dse class with values up to 40m tall. Asc and Fse vegetation types reached up to 30m and 25m in height, respectively. Differences in canopy structure were also evident from the analysis of canopy volume models (CVMs). Asc showed higher proportion of sunlit, as expected for open forest types. Fse showed gaps predominantly in lower height levels, and a higher overall proportion of shaded crown. Full canopy closure was reached at about15 m height for both Asc and Dse, and at about 20 m height for Fse. We also used a base map of degraded areas (available from Imazon - Instituto do Homen e Meio Ambiente da Amazônia) to follow these regions throughout time using EVI and GRND from MODIS. All three forest types displayed seasonal cycles. Notable differences in amplitude were detected during the periods when degradation occurred and both indexes showed a decrease in their response. However, there were marked differences in timing and amplitude depending on forest type. These responses were influenced by the spatial resolution of 1km of the MODIS images, limited the ability to observe small degraded regions. In conclusion, ASL together with optical remote sensing used in a straight multi-scale approach may contribute to understand the impacts of degradation in the structure and functioning of tropical forest.

Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

EPA Science Inventory

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteo...

Application and Evaluation of MODIS LAI, fPAR, and Albedo Products in the WRFCMAQ System

EPA Science Inventory

Leaf area index (LAI), vegetation fraction (VF), and surface albedo are important parameters in the land surface model (LSM) for meteorology and air quality modeling systems such as WRF/CMAQ. LAI and VF control not only leaf to canopy level evapotranspiration flux scaling but al...

Snow cover variability in a forest ecotone of the Oregon Cascades via MODIS Terra products

Treesearch

Tihomir Sabinov Kostadinov; Todd R. Lookingbill

2015-01-01

Snowcover pattern and persistence have important implications for planetary energy balance, climate sensitivity to forcings, and vegetation structure, function, and composition. Variability in snow cover within mountainous regions of the Pacific Northwest, USA is attributable to a combination of anthropogenic climate change and climate oscillations. However,...

Aboveground biomass in Tibetan grasslands

Treesearch

Y.H. Yang; J.Y. Fang; Y.D. Pan; C.J. Ji

2009-01-01

This study investigated spatial patterns and environmental controls of aboveground biomass (AGB) in alpine grasslands on the Tibetan Plateau by integrating AGB data collected from 135 sites during 2001-2004 and concurrent enhanced vegetation index derived from MODIS data sets. The AGB was estimated at 68.8 gm-2, with a larger value (90.8 gm

EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) Comparisons Across mMltiple Spatial Scales RSAD Oral Poster based session

EPA Science Inventory

Leaf Area Index (LAI) is an important parameter in assessing vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. However, satellite-derived LAI products can be limited by obstructed atmospheric cond...

Use of Normalized Difference Water Index for monitoring live fuel moisture

Treesearch

D.A. Roberts; P.E. Dennison; S.H. Peterson; J. Rechel

2006-01-01

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were compared for monitoring live fuel moisture in a shrubland ecosystem. Both indices were calculated from 500m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data covering a 33-month period from 2000 to 2002. Both NDVI and NDWI were...

Exploratory spatial data analysis of global MODIS active fire data

NASA Astrophysics Data System (ADS)

Oom, D.; Pereira, J. M. C.

2013-04-01

We performed an exploratory spatial data analysis (ESDA) of autocorrelation patterns in the NASA MODIS MCD14ML Collection 5 active fire dataset, for the period 2001-2009, at the global scale. The dataset was screened, resulting in an annual rate of false alarms and non-vegetation fires ranging from a minimum of 3.1% in 2003 to a maximum of 4.4% in 2001. Hot bare soils and gas flares were the major sources of false alarms and non-vegetation fires. The data were aggregated at 0.5° resolution for the global and local spatial autocorrelation Fire counts were found to be positively correlated up to distances of around 200 km, and negatively for larger distances. A value of 0.80 (p = 0.001, α = 0.05) for Moran's I indicates strong spatial autocorrelation between fires at global scale, with 60% of all cells displaying significant positive or negative spatial correlation. Different types of spatial autocorrelation were mapped and regression diagnostics allowed for the identification of spatial outlier cells, with fire counts much higher or lower than expected, considering their spatial context.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronand; Russell, Jeff; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the foundation of any interoperability or change detection technique. Satellite intercomparisons and accurate vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), require the generation of accurate reflectance maps (NDVI is used to describe or infer a wide variety of biophysical parameters and is defined in terms of near-infrared (NIR) and red band reflectances). Accurate reflectance-map generation from satellite imagery relies on the removal of solar and satellite geometry and of atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance has been widely applied to a few systems only. The ability to obtain atmospherically corrected imagery and products from various satellites is essential to enable widescale use of remotely sensed, multitemporal imagery for a variety of applications. An atmospheric correction approach derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that can be applied to high-spatial-resolution satellite imagery under many conditions was evaluated to demonstrate a reliable, effective reflectance map generation method. Additional information is included in the original extended abstract.

Extensive Burn Scars in Russia's Amur Region

NASA Technical Reports Server (NTRS)

2002-01-01

Vast areas of southeastern Russia have been scorched by fires over the last few weeks. All across Siberia fires have been raging, and this Moderate Resolution Imaging Spectroradiometer (MODIS) image from May 15, 2002, shows extensive, dark burn scars along with actively burning fires (red dots) on the north side of the Amur River, which separates Russia (north) and China (south). The southern Amur region is largely devoted to farming and other agriculture, and these fires may have been set intentionally to prepare the land for the growing season. Fire is often used to clear land of unwanted vegetation, and to return the nutrients stored in vegetation back to the soil. However, fires that are too frequent or severe can devastate the soil, eventually making it unsuitable for farming or grazing. Fires can also escape control and spread into protected areas. In this image, fires are mostly concentrated in a lowland area within the drainage basin of the Zeya River, which drains from the frozen lake at the top of the image. Image courtesy Jacques Descloitres, MODIS Land Rapid Response Team at NASA GSFC

Scaling up of Carbon Exchange Dynamics from AmeriFlux Sites to a Super-Region in the Eastern United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hans Peter Schmid; Craig Wayson

The primary objective of this project was to evaluate carbon exchange dynamics across a region of North America between the Great Plains and the East Coast. This region contains about 40 active carbon cycle research (AmeriFlux) sites in a variety of climatic and landuse settings, from upland forest to urban development. The core research involved a scaling strategy that uses measured fluxes of CO{sub 2}, energy, water, and other biophysical and biometric parameters to train and calibrate surface-vegetation-atmosphere models, in conjunction with satellite (MODIS) derived drivers. To achieve matching of measured and modeled fluxes, the ecosystem parameters of the modelsmore » will be adjusted to the dynamically variable flux-tower footprints following Schmid (1997). High-resolution vegetation index variations around the flux sites have been derived from Landsat data for this purpose. The calibrated models are being used in conjunction with MODIS data, atmospheric re-analysis data, and digital land-cover databases to derive ecosystem exchange fluxes over the study domain.« less

Space environment's effect on MODIS calibration

NASA Astrophysics Data System (ADS)

Dodd, J. L.; Wenny, B. N.; Chiang, K.; Xiong, X.

2010-09-01

The MODerate resolution Imaging Spectroradiometer flies on board the Earth Observing System (EOS) satellites Terra and Aqua in a sun-synchronous orbit that crosses the equator at 10:30 AM and 2:30 PM, respectively, at a low earth orbit (LEO) altitude of 705 km. Terra was launched on December 18,1999 and Aqua was launched on May 4, 2002. As the MODIS instruments on board these satellites continue to operate beyond the design lifetime of six years, the cumulative effect of the space environment on MODIS and its calibration is of increasing importance. There are several aspects of the space environment that impact both the top of atmosphere (TOA) calibration and, therefore, the final science products of MODIS. The south Atlantic anomaly (SAA), spacecraft drag, extreme radiative and thermal environment, and the presence of orbital debris have the potential to significantly impact both MODIS and the spacecraft, either directly or indirectly, possibly resulting in data loss. Efforts from the Terra and Aqua Flight Operations Teams (FOT), the MODIS Instrument Operations Team (IOT), and the MODIS Characterization Support Team (MCST) prevent or minimize external impact on the TOA calibrated data. This paper discusses specific effects of the space environment on MODIS and how they are minimized.

History and Future for the Happy Marriage between the MODIS Land team and Fluxnet

NASA Astrophysics Data System (ADS)

Running, S. W.

2015-12-01

When I wrote the proposal to NASA in 1988 for daily global evapotranspiration and gross primary production algorithms for the MODIS sensor, I had no validation plan. Fluxnet probably saved my MODIS career by developing a global network of rigorously calibrated towers measuring water and carbon fluxes over a wide variety of ecosystems that I could not even envision at the time that first proposal was written. However my enthusiasm for Fluxnet was not reciprocated by the Fluxnet community until we began providing 7 x 7 pixel MODIS Land datasets exactly over each of their towers every 8 days, without them having to crawl thru the global datasets and make individual orders. This system, known informally as the MODIS ASCII cutouts, began in 2002 and operates at the Oak Ridge DAAC to this day, cementing a mutually beneficial data interchange between the Fluxnet and remote sensing communities. This talk will briefly discuss the history of MODIS validation with flux towers, and flux spatial scaling with MODIS data. More importantly I will detail the future continuity of global biophysical datasets in the post-MODIS era, and what next generation sensors will provide.

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

USGS Publications Warehouse

Wang, Kun; Zhang, Li; Qiu, Yubao; Ji, Lei; Tian, Feng; Wang, Cuizhen; Wang, Zhiyong

2013-01-01

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.

Mapping the spatial and temporal dynamics of the velvet mesquite with MODIS and AVIRIS: Case study at the Santa Rita Experimental Range

NASA Astrophysics Data System (ADS)

Kaurivi, Jorry Zebby Ujama

The general objective of this research is to develop a methodology that will allow mapping and quantifying shrub encroachment with remote sensing. The multitemporal properties of the Moderate Resolution Imaging Spectroradiometer (MODIS) -250m, 16-day vegetation index products were combined with the hyperspectral and high spatial resolution (3.6m) computation of the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) to detect the dynamics of mesquite and grass/soil matrix at two sites of high (19.5%) and low (5.7%) mesquite cover in the Santa Rita Experimental Range (SRER). MODIS results showed separability between grassland and mesquite based on phenology. Mesquite landscapes had longer green peak starting in April through February, while the grassland only peaked during the monsoon season (July through October). AVIRIS revealed spectral separability, but high variation in the data implicated high heterogeneity in the landscape. Nonetheless, the methodology for larger data was developed in this study and combines ground, air and satellite data.

Terrestrial Carbon Fluxes from Deforestation in the Brazilian Amazon and Cerrado Regions Predicted from MODIS Satellite Data and Ecosystem Modeling

NASA Astrophysics Data System (ADS)

Klooster, S.; Potter, C.; Genovese, V.

2008-12-01

The NASA-CASA (Carnegie Ames Stanford Approach) simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate tropical forest and savanna (Cerrado) carbon pools for the Brazilian Amazon region over the period 2000-2004. Adjustments for mean age of forest stands were carried out across the region, resulting in a new mapping of aboveground biomass pools based on MODIS satellite data. Yearly maps of newly deforested lands from the Brazilian PRODES (Programa de calculo do desflorestamento da Amazonia ) project were combined with these NASA-CASA biomass predictions to generate seasonal budgets of potential carbon and nitrogen trace gas losses from biomass burning events. Simulations of plant residue and soil carbon decomposition were conducted in the NASA-CASA model during and following deforestation events to track the fate of aboveground biomass pools that were cut and burned each year across the region.

Human and biophysical influences on fire occurrence in the United States

USGS Publications Warehouse

Hawbaker, Todd J.; Radeloff, Volker C.; Stewart, Susan I.; Hammer, Roger B.; Keuler, Nicholas S.; Clayton, Murray K.

2013-01-01

National-scale analyses of fire occurrence are needed to prioritize fire policy and management activities across the United States. However, the drivers of national-scale patterns of fire occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of fire occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative influence of human, vegetation, climate, and topographic variables on fire occurrence in the United States, as measured by MODIS active fire detections collected between 2000 and 2006. We constructed models for all fires and for large fires only and generated predictive maps to quantify fire occurrence probabilities. Areas with high fire occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-fire occurrence were generally lower, but hot spots existed in the western and south-central United States The probability of fire occurrence is a critical component of fire risk assessments, in addition to vegetation type, fire behavior, and the values at risk. Many of the hot spots we identified have extensive development in the wildland–urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all fires and large fires and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also significant variables in most ecoregions. If recent housing growth trends and fire occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wildfires with the ecological benefits of fire.

Emerging landscape degradation trends in the East African Horn

NASA Astrophysics Data System (ADS)

Pricope, N. G.; Michaelsen, J.; Husak, G. J.; Funk, C. C.; Lopez-Carr, D.

2012-12-01

Increasing climate variability along with declining trends in rainfall represent major risk factors affecting food security in many regions of the world. We identify Africa-wide regions where significant rainfall decreases from 1979-2011 are coupled with significant human population density increases. The rangelands of the East African Horn remain one of the world's most food insecure regions with significantly increasing human populations predominantly dependent on pastoralist and agro-pastoralist livelihoods. Widespread vegetation degradation is occurring, adversely impacting fragile ecosystems and human livelihoods. Using MODIS land cover and normalized difference vegetation index (NDVI) data collected since 2000, we observe significant changes in vegetation patterns and productivity over the last decade across the East African Horn and demonstrate that these two products can be used concurrently at large spatial scales to monitor vegetation dynamics at decadal time scales. Results demonstrate that a near doubling of the population in pastoral regions is linked with hotspots of degradation in vegetation condition. The most significant land cover change and browning trends are observed in areas experiencing drying precipitation trends in addition to increasing population pressures. These findings have serious implications for current and future regional food security monitoring and forecasting and for mitigation and adaptation strategies in a region where population is expected to continue increasing against a backdrop of drying climate trends.Fig.1(a)Change in standardized precipitation index in Africa between 1979-2010 (b)Change in population density at continental scale using the GRUMPv1 1990 and 2000 and AfriPop 2010 population density datasets Fig.2 Land cover change trajectories based on 2001-2009 MOD12Q1 Land Cover product for the East African Horn overlaid over aggregated FEWS Net Livelihoods Zones.

Satellites observed widespread greening of Earth and increase of woody biomass

NASA Astrophysics Data System (ADS)

Chen, C.; Park, T.; Myneni, R.; Xu, L.; Saatchi, S. S.; Liu, Y.; Knyazikhin, Y.

2017-12-01

Global terrestrial vegetation is an important modulator of the planetary climate system that alters Earth's hydrology, atmosphere and energy circulations through biophysical and biochemical processes. Yet the internal structural change of the vegetation is not well understood. Leaf area index (LAI), unlike radiometric parameters (e.g. NDVI), is a well-defined and ground-measurable biophysical variable, which can better represent the greenness of vegetation. We evaluate 17-year (2000-2016) satellite-derived LAI from two MODIS sensors onboard Terra and Aqua. Results show that the global annual-averaged LAI has an increasing trend at 0.036 m2m-2 per decade (2.3% per decade). The widespread greening takes up 32.5% of the vegetated area, while only 5.2% of such exhibits browning. We further investigate the biome- and regional-specific patterns of the evolution of LAI: 1) Croplands (0.062 m2m-2 per decade) and forests (0.044 m2m-2 per decade) are the paramount contributors of the greening; 2) Temperate vegetation (0.052 m2m-2 per decade) greening outperform other regions, followed by high-latitude vegetation (0.031 m2m-2 per decade), and tropical vegetation (0.025 m2m-2 per decade) at the minimum. Two independent satellite-observed datasets from multiple bandwidths (optical, thermal and microwave) provide evidence that this large-scale LAI trend is mainly owing to the spatiotemporal transition of woody biomass and the change of canopy structure. The greening (browning) at the global scale is concordant with the increase (decrease) of tree cover and vegetation optical depth (VOD), while little correlation is found for herbaceous biomass (i.e. non-tree cover). The observed greening and expansion of woody biomass will lead to a smaller land surface diurnal temperature range (DTR) due to the increase of a) the evapotranspiration, b) the water storage (higher the specific heat capacity) and c) the aerodynamic resistance (vertical mixture) of the canopy. a) and c) can augment daytime cooling, while b) and c) can boost nighttime warming. We find, consistently, the MODIS observed land surface DTR decreases over greening regions, and increases over browning regions.

Early Spring Post-Fire Snow Albedo Dynamics in High Latitude Boreal Forests Using Landsat-8 OLI Data

NASA Technical Reports Server (NTRS)

Wang, Zhuosen; Erb, Angela M.; Schaaf, Crystal B.; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A.; Roman, Miguel O.

2016-01-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (less than 100 m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high-burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500 m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

Characterization, validation and intercomparison of clumping index maps from POLDER, MODIS, and MISR satellite data over reference sites

NASA Astrophysics Data System (ADS)

Pisek, Jan; He, Liming; Chen, Jing; Govind, Ajit; Sprintsin, Michael; Ryu, Youngryel; Arndt, Stefan; Hocking, Darren; Wardlaw, Timothy; Kuusk, Joel; Oliphant, Andrew; Korhonen, Lauri; Fang, Hongliang; Matteucci, Giorgio; Longdoz, Bernard; Raabe, Kairi

2015-04-01

Vegetation foliage clumping significantly alters its radiation environment and therefore affects vegetation growth as well as water and carbon cycles. The clumping index is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index (LAI) retrieved from mono-angle remote sensing and allows accurate separation of sunlit and shaded leaves in the canopy. Not accounting for the foliage clumping in LAI retrieval algorithms leads to substantial underestimation of actual LAI, especially for needleleaf forests. Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth's Reflectances (POLDER) data at ~6 km resolution, from Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product at 500 m resolution. Most recently the algorithm was applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m resolution over selected areas. In this presentation we characterize and intercompare the three products over a set of sites representing diverse biomes and different canopy structures. The products are also directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. We illustrate that the vertical distribution of foliage and especially the effect of understory needs to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements respond to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can be propagated into the foliage clumping maps. Our results indicate that MODIS data and MISR data with 275 m in particular can provide good quality clumping index estimates at pertinent scales for modeling local carbon and energy fluxes.

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data

PubMed Central

Wang, Zhuosen; Erb, Angela M.; Schaaf, Crystal B.; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A.; Román, Miguel O.

2018-01-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems. PMID:29769751

Characterization, Validation and Intercomparison of Clumping Index Maps from POLDER, MODIS, and MISR Satellite Data Over Reference Sites

NASA Astrophysics Data System (ADS)

Pisek, J.; He, L.; Chen, J. M.; Govind, A.; Sprintsin, M.; Ryu, Y.; Arndt, S. K.; Hocking, D.; Wardlaw, T.; Kuusk, J.; Oliphant, A. J.; Korhonen, L.; Fang, H.; Matteucci, G.; Longdoz, B.; Raabe, K.

2015-12-01

Vegetation foliage clumping significantly alters its radiation environment and therefore affects vegetation growth as well as water and carbon cycles. The clumping index is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index (LAI) retrieved from mono-angle remote sensing and allows accurate separation of sunlit and shaded leaves in the canopy. Not accounting for the foliage clumping in LAI retrieval algorithms leads to substantial underestimation of actual LAI, especially for needleleaf forests. Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth's Reflectances (POLDER) data at ~6 km resolution, from Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product at 500 m resolution. Most recently the algorithm was applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m resolution over selected areas. In this presentation we characterize and intercompare the three products over a set of sites representing diverse biomes and different canopy structures. The products are also directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. We illustrate that the vertical distribution of foliage and especially the effect of understory needs to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements respond to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can be propagated into the foliage clumping maps. Our results indicate that MODIS data and MISR data with 275 m resolution in particular can provide good quality clumping index estimates at pertinent scales for modeling local carbon and energy fluxes.

Central Brazil

NASA Technical Reports Server (NTRS)

2002-01-01

This Moderate resolution Imaging Spectroradiometer (MODIS) true-color image was acquired on October 19, 2000, over a region in Brazil large enough to show much of the country's diverse landscape. Spanning some 8.5 million square kilometers (3.2 million square miles), Brazil is by far the largest South American nation--both in terms of land and population. The region known as the Amazon Basin lies to the northwest (upper left) and extends well beyond the northern and western edges of this scene. Typically, from this perspective Amazonia appears as a lush, dark green carpet due to the thick canopy of vegetation growing there. Some of the Amazon Basin is visible in this image, but much is obscured by clouds (bright white pixels), as is the Amazon River. This region is home to countless plant and animal species and some 150,000 native South Americans. The clusters of square and rectangular patterns toward the center of the image (light green or reddish-brown pixels) are where people have cleared away trees and vegetation to make room for development and agriculture. Toward the western side of the scene there is considerable haze and smoke from widespread biomass burning in parts of Brazil and Bolivia, which shares its eastern border with Brazil. Toward the east in this image is the highland, or 'cerrado,' region, which is more sparsely vegetated and has a somewhat drier climate than the Amazon Basin. The capital city, Brasilia, lies within this region just southwest of the Geral de Goias Mountains (orangish pixels running north-south). There are two large water reservoirs visible in this scene--the Sobradinho Reservoir about 800 km (500 miles) northeast of Brasilia, and the Paranaiba about 500 km (300 miles) southwest of Brasilia. MODIS flies aboard NASA's Terra spacecraft. Image courtesy Brian Montgomery, Reto Stockli, and Robert Simmon, based on data from the MODIS Science Team.

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data.

PubMed

Wang, Zhuosen; Erb, Angela M; Schaaf, Crystal B; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A; Román, Miguel O

2016-11-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

New capabilities for characterizing smoke and dust aerosol over land using MODIS

NASA Astrophysics Data System (ADS)

Levy, R. C.; Remer, L. A.

2006-12-01

Smoke and dust aerosol have different chemical, optical and physical properties and both types affect many processes within the climate system. As earth's surface and atmosphere are continuously altered by natural and anthropogenic processes, the emission and presumably the effects of these aerosols are also changing. Thus it is necessary to observe and characterize aerosols on a global and climatic scale. While MODIS has been reporting characteristics of smoke and dust aerosol over land and ocean since shortly after Terra launch, the uncertainties in the over-land retrieval have been larger than expected. To better characterize different aerosol types closer to their source regions with greater accuracy, we have developed a new operational algorithm for retrieving aerosol properties over dark land surfaces from MODIS-observed visible (VIS) and infrared (IR) reflectance. Like earlier versions, this algorithm estimates the total loading (aerosol optical depth-τ) and relative weighting of fine (non-dust) and coarse (dust) -dominated aerosol to the total τ (fine weighting-η) over dark land surfaces. However, the fundamental mathematics and major assumptions have been overhauled. The new algorithm performs simultaneous multi-channel inversion that includes information about coarse aerosol in the IR channels, while assuming a fine-tuned relationship between VIS and IR surface reflectances, that is itself a function of scattering angle and vegetation condition. Finally, the suite of expected aerosol optical models described by the lookup table have been revised to closer resemble the AERONET climatology, including for smoke and dust aerosol. Beginning in April 2006, this algorithm has been used for forward processing and backward re- processing of the entire MODIS dataset observed from both Terra and Aqua. "Collection 5" products were completed for Aqua reprocessing by July 2006 and should be complete for Terra by December 2006. In this study, we used the complete Aqua dataset (July 2002-Aug 2006) and two years of Terra (2005-Aug 2006) data to evaluate the products in regions known to be dominated by smoke and/or dust. We compared with sunphotometer data at selected AERONET sites and found improved τ retrievals,within prescribed accuracy.

Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack

USGS Publications Warehouse

Dennison, P.E.; Nagler, P.L.; Hultine, K.R.; Glenn, E.P.; Ehleringer, J.R.

2009-01-01

Tamarisk (Tamarix spp.) has invaded riparian ecosystems throughout the Western United States, including significant portions of riparian ecosystems within U.S. National Parks and Monuments. Recently, the saltcedar leaf beetle (Diorhabda elongata) was released as a tamarisk biocontrol agent. Although initial releases have been monitored, no comprehensive program is currently in place to monitor the rapid spread of Diorhabda that has resulted from numerous subsequent releases by county and state agencies. Long term monitoring of tamarisk defoliation and its impacts on habitat and water resources is needed. This study examines the potential for using higher spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and lower spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data for monitoring defoliation caused by Diorhabda and subsequent changes in evapotranspiration (ET). Widespread tamarisk defoliation was observed in an eastern Utah study area during summer 2007. ASTER normalized difference vegetation index (NDVI) showed only minor changes between 2005 and 2006, but a significant drop in NDVI was found within riparian areas between 2006 and 2007. The decrease in NDVI caused by defoliation was apparent despite partial refoliation within the study area. MODIS time series data revealed that absolute decline in EVI varied by site, but that the timing of EVI decline during summer 2007 was early with respect to phenological patterns from 2001 through 2006. Defoliation caused decreases in ET values estimated from both ASTER and MODIS data. MODIS estimated ET declined earlier than in previous years, although annual ET was not significantly different than ET in previous years due to high year-to-year variability. Challenges to detection and monitoring of tamarisk defoliation include spectral mixing of tamarisk and other cover types at subpixel spatial resolution, spatial coregistration of time series images, the timing of image acquisition, and changes unrelated to defoliation in non-tamarisk land cover over time. Continued development of the techniques presented in this paper may allow monitoring the spread of Diorhabda and assessment of potential water salvage resulting from biocontrol of tamarisk. ?? 2009 Elsevier Inc.

Photon Recollision Probability: a Useful Concept for Cross Scale Consistency Check between Leaf Area Index and Foliage Clumping Products

NASA Astrophysics Data System (ADS)

Pisek, J.

2017-12-01

Clumping index (CI) is the measure of foliage aggregation relative to a random distribution of leaves in space. CI is an important factor for the correct quantification of true leaf area index (LAI). Global and regional scale CI maps have been generated from various multi-angle sensors based on an empirical relationship with the normalized difference between hotspot and darkspot (NDHD) index (Chen et al., 2005). Ryu et al. (2011) suggested that accurate calculation of radiative transfer in a canopy, important for controlling gross primary productivity (GPP) and evapotranspiration (ET) (Baldocchi and Harley, 1995), should be possible by integrating CI with incoming solar irradiance and LAI from MODIS land and atmosphere products. It should be noted that MODIS LAI/FPAR product uses internal non-empirical, stochastic equations for parameterization of foliage clumping. This raises a question if integration of the MODIS LAI product with empirically-based CI maps does not introduce any inconsistencies. Here, the consistency is examined independently through the `recollision probability theory' or `p-theory' (Knyazikhin et al., 1998) along with raw LAI-2000/2200 Plant Canopy Analyzer (PCA) data from > 30 sites, surveyed across a range of vegetation types. The theory predicts that the amount of radiation scattered by a canopy should depend only on the wavelength and the spectrally invariant canopy structural parameter p. The parameter p is linked to the foliage clumping (Stenberg et al., 2016). Results indicate that integration of the MODIS LAI product with empirically-based CI maps is feasible. Importantly, for the first time it is shown that it is possible to obtain p values for any location solely from Earth Observation data. This is very relevant for future applications of photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.

Growing Degree Vegetation Production Index (GDVPI): A Novel and Data-Driven Approach to Delimit Season Cycles

NASA Astrophysics Data System (ADS)

Graham, W. D.; Spruce, J.; Ross, K. W.; Gasser, J.; Grulke, N.

2014-12-01

Growing Degree Vegetation Production Index (GDVPI) is a parametric approach to delimiting vegetation seasonal growth and decline cycles using incremental growing degree days (GDD), and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 8-day composite cumulative integral data. We obtain a specific location's daily minimum and maximum temperatures from the nearest National Oceanic and Atmospheric Administration (NOAA) weather stations posted on the National Climate Data Center (NCDC) Climate Data Online (CDO) archive and compute GDD. The date range for this study is January 1, 2000 through December 31, 2012. We employ a novel process, a repeating logistic product (RLP), to compensate for short-term weather variability and data drops from the recording stations and fit a curve to the median daily GDD values, adjusting for asymmetry, amplitude, and phase shift that minimize the sum of squared errors when comparing the observed and predicted GDD. The resulting curve, here referred to as the surrogate GDD, is the time-temperature phasing parameter used to convert Cartesian NDVI values into polar coordinate pairs, multiplying the NDVI values as the radial by the cosine and sine of the surrogate GDD as the angular. Depending on the vegetation type and the original NDVI curve, the polar NDVI curve may be nearly circular, kidney-shaped, or pear-shaped in the case of conifers, deciduous, or agriculture, respectively. We examine the points of tangency about the polar coordinate NDVI curve, identifying values of 1, 0, -1, or infinity, as each of these represent natural inflection points. Lines connecting the origin to each tangent point illustrate and quantify the parametrically segmentation of the growing season based on the GDD and NDVI ostensible dependency. Furthermore, the area contained by each segment represents the apparent vegetation production. A particular benefit is that the inflection points are determined near real-time, as MODIS NDVI, 8-day composite data become available, affording an effective forecasting and hindcasting tool.

Satellite Observed Widespread Decline in Mongolian Grasslands Largely Due to Overgrazing

NASA Technical Reports Server (NTRS)

Hilker, Thomas; Natsagdorj, Enkhjargal; Waring, Richard H.; Lyapustin, Alexei; Wang, Yujie

2014-01-01

The Mongolian Steppe is one of the largest remaining grassland ecosystems. Recent studies have reported widespread decline of vegetation across the steppe and about 70 percent of this ecosystem is now considered degraded. Among the scientific community there has been an active debate about whether the observed degradation is related to climate, or overgrazing, or both. Here, we employ a new atmospheric correction and cloud screening algorithm (MAIAC) to investigate trends in satellite observed vegetation phenology. We relate these trends to changes in climate and domestic animal populations. A series of harmonic functions is fitted to MODIS observed phenological curves to quantify seasonal and inter-annual changes in vegetation. Our results show a widespread decline (of about 12 percent on average) in MODIS observed NDVI across the country but particularly in the transition zone between grassland and the Gobi desert, where recent decline was as much as 40 percent below the 2002 mean NDVI. While we found considerable regional differences in the causes of landscape degradation, about 80 percent of the decline in NDVI could be attributed to increase in livestock. Changes in precipitation were able to explain about 30 percent of degradation across the country as a whole but up to 50 percent in areas with denser vegetation cover (p0.05). Temperature changes, while significant, played only a minor role (r20.10, p0.05). Our results suggest that the cumulative effect of overgrazing is a primary contributor to the degradation of the Mongolian steppe and is at least partially responsible for desertification reported in previous studies.

Detection of Green up Phenomenon in Amazon Forests Using Spaceborne Solar-induced Fluorescence

NASA Astrophysics Data System (ADS)

Chen, S.; Chen, X.; Chen, J.; Cao, X.

2016-12-01

The role of Amazon forests in the global carbon budget still remains uncertain. The critical issue is whether tropical forest productivity is more limited by sunlight or rainfall. Recent studies using satellite data have challenged the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions because of the adding effects of variations in sun-sensor geometry. To reducing uncertainties in knowing the sensitivity of Amazon rainforests to dry season droughts, we evaluated a newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll for the seasonal green-up phenomenon, providing for the first time a direct measurement related to vegetation photosynthetic activity as well as unaffected by sun-sensor geometry. Moreover, NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) products (the enhanced vegetation index (EVI) and leaf area index (LAI)) and Landsat Operational Land Imager (OLI) data are also compared to evaluate this phenomenon. Here we show that the green up of Amazon forests in the study area around manas site did show in SIF of chlorophyll data in 2015 drought resulted from seasonal changes. The EVI has more apparent green up phenomenon than the NDVI data both in MODIS and OLI data, suggesting that the EVI can better reflect near-infrared (NIR) and LAI information of vegetation. The OLI data is less influenced by variations caused by bidirectional reflectance effect. In addition, SIF of chlorophyll data shows well correlation relationship with the EVI, LAI and NDVI, suggesting that the SIF of chlorophyll data present well quality to capture the characteristics of the phenology of vegetation.

Modeling water and heat balance components of large territory for vegetation season using information from polar-orbital and geostationary meteorological satellites

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2015-04-01

To date, physical-mathematical modeling processes of land surface-atmosphere interaction is considered to be the most appropriate tool for obtaining reliable estimates of water and heat balance components of large territories. The model of these processes (Land Surface Model, LSM) developed for vegetation period is destined for simulating soil water content W, evapotranspiration Ev, vertical latent LE and heat fluxes from land surface as well as vertically distributed soil temperature and moisture, soil surface Tg and foliage Tf temperatures, and land surface skin temperature (LST) Ts. The model is suitable for utilizing remote sensing data on land surface and meteorological conditions. In the study these data have been obtained from measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/geostationary satellites Meteosat-9, -10 (MSG-2, -3). The heterogeneity of the land surface and meteorological conditions has been taken into account in the model by using soil and vegetation characteristics as parameters and meteorological characteristics as input variables. Values of these characteristics have been determined from ground observations and remote sensing information. So, AVHRR data have been used to build the estimates of effective land surface temperature (LST) Ts.eff and emissivity E, vegetation-air temperature (temperature at the vegetation level) Ta, normalized vegetation index NDVI, vegetation cover fraction B, the leaf area index LAI, and precipitation. From MODIS data the values of LST Tls, Å, NDVI, LAI have been derived. From SEVIRI data there have been retrieved Tls, E, Ta, NDVI, LAI and precipitation. All named retrievals covered the vast territory of the part of the agricultural Central Black Earth Region located in the steppe-forest zone of European Russia. This territory with coordinates 49°30'-54°N, 31°-43°E and a total area of 227,300 km2 has been chosen for investigation. It has been carried out for years 2009-2013 vegetation seasons. To provide the retrieval of Ts.eff, E, Ta, NDVI, B, and LAI the previously developed technologies of AVHRR data processing have been refined and adapted to the region of interest. The updated linear regression estimators for Ts.eff and Tà have been built using representative training samples compiled for above vegetation seasons. The updated software package has been applied for AVHRR data processing to generate estimates of named values. To verify the accuracy of these estimates the error statistics of Ts.eff and Ta derivation has been investigated for various days of named seasons using comparison with in-situ ground-based measurements. On the base of special technology and Internet resources the remote sensing products Tls, E, NDVI, LAI derived from MODIS data and covering the study area have been extracted from LP DAAC web-site for the same vegetation seasons. The reliability of the MODIS-derived Tls estimates has been confirmed via comparison with analogous and collocated ground-, AVHRR-, and SEVIRI-based ones. The prepared remote sensing dataset has also included the SEVIRI-derived estimates of Tls, E, NDVI, Ta at daylight and night-time and daily estimates of LAI. The Tls estimates has been built utilizing the method and technology developed for the retrieval of Tls and E from 15 minutes time interval SEVIRI data in IR channels 10.8 and 12.0 µm (classified as 100% cloud-free and covering the area of interest) at three successive times without accurate a priori knowledge of E. Comparison of the SEVIRI-based Tls retrievals with independent collocated Tls estimates generated at the Land Surface Analysis Satellite Applications Facility (LSA SAF, Lisbon, Portugal) has given daily- or monthly-averaged values of RMS deviation in the range of 2°C for various dates and months during the mentioned vegetation seasons which is quite acceptable result. The reliability of the SEVIRI-based Tls estimates for the study area has been also confirmed by comparing with AVHRR- and MODIS-derived LST estimates for the same seasons. The SEVIRI-derived values of Ta considered as the temperature of the vegetation cover has been obtained using Tls estimates and a previously found multiple linear regression relationship between Tls and Ta formulated accounting for solar zenith angle and land elevation. A comparison with ground-based collocated Ta observations has given RMS errors of 2.5°C and lower. It can be treated as a proof of the proposed technique's functionality. SEVIRI-derived LAI estimates have been retrieved at LSA SAF from measurements by this sensor in channels 0.6, 0.8, and 1.6 μm under cloud-free conditions at that when using data in the channel 1.6 μm the accuracy of these estimates has increased. In the study the AVHRR- and SEVIRI-derived estimates of daily and monthly precipitation sums for the territory under investigation for the years 2009 - 2013 vegetation seasons have been also used. These estimates have been obtained by the improved integrated Multi Threshold Method (MTM) providing detection and identification of cloud types around the clock throughout the year as well as identification of precipitation zones and determination of instantaneous precipitation maximum intensity within the pixel using the measurement data in different channels of named sensors as predictors. Validation of the MTM has been performed by comparing the daily and monthly precipitation sums with appropriate values resulted from ground-based observations at the meteorological stations of the region. The probability of detecting precipitation zones from satellite data corresponding to the actual ones has been amounted to 70-80%. AVHRR- and SEVIRI-derived daily and monthly precipitation sums have been in reasonable agreement with each other and with results of ground-based observations although they are smoother than the last values. Discrepancies have been noted only for local maxima for which satellite-based estimates of precipitation have been much less than ground-based ones. It may be due to the different spatial scales of areal satellite-derived and point ground-based estimates. To utilize satellite-derived vegetation and meteorological characteristics in the model the special procedures have been developed including: - replacement of ground-based LAI and B estimates used as model parameters by their satellite-derived estimates from AVHRR, MODIS and SEVIRI data. Correctness of such replacement has been confirmed by comparing the time behavior of LAI over the period of vegetation as well as modeled and measured values of evapotranspiration Ev and soil moisture content W; - entering AVHRR-, MODIS- and SEVIRI-derived estimates of Ts.eff Tls, and Ta into the model as input variables instead of ground-measured values with verification of adequacy of model operation under such a change through comparison of the calculated and measured values of W and Ev; - inputing satellite-derived estimates of precipitation during vegetation period retrieved from AVHRR and SEVIRI data using the MTM into the model as input variables. When developing given procedure algorithms and programs have been created to transit from assessment of the rainfall intensity to evaluation of its daily values. The implementation of such a transition requires controlling correctness of the estimates built at each time step. This control includes comparison of areal distributions of three-hour, daily and monthly precipitation amounts obtained from satellite data and calculated by interpolation of standard network observation data; - taking into account spatial heterogeneity of fields of satellite AVHRR-, MODIS- and SEVIRI-derived estimates of LAI, B, LST and precipitation. This has involved the development of algorithms and software for entering the values of all named characteristics into the model in each computational grid node. Values of evapotranspiration E, soil water content W, vertical latent and sensible heat fluxes and other water and heat balance components as well as land surface temperature and moisture area-distributed over the territory of interest have been resulted from the model calculations for the years 2009-2013 vegetation seasons. These calculations have been carried out utilizing satellite-derived estimates of the vegetation characteristics, LST and precipitation. E and W calculation errors have not exceeded the standard values.

[Study of the microwave emissivity characteristics over different land cover types].

PubMed

Zhang, Yong-Pan; Jiang, Ling-Mei; Qiu, Yu-Bao; Wu, Sheng-Li; Shi, Jian-Cheng; Zhang, Li-Xin

2010-06-01

The microwave emissivity over land is very important for describing the characteristics of the lands, and it is also a key factor for retrieving the parameters of land and atmosphere. Different land covers have their emission behavior as a function of structure, water content, and surface roughness. In the present study the global land surface emissivities were calculated using six month (June, 2003-August, 2003, Dec, 2003-Feb, 2004) AMSR-E L2A brightness temperature, MODIS land surface temperature and the layered atmosphere temperature, and humidity and pressure profiles data retrieved from MODIS/Aqua under clear sky conditions. With the information of IGBP land cover types, "pure" pixels were used, which are defined when the fraction cover of each land type is larger than 85%. Then, the emissivity of sixteen land covers at different frequencies, polarization and their seasonal variation were analyzed respectively. The results show that the emissivity of vegetation including forests, grasslands and croplands is higher than that over bare soil, and the polarization difference of vegetation is smaller than that of bare soil. In summer, the emissivity of vegetation is relatively stable because it is in bloom, therefore the authors can use it as its emissivity in our microwave emissivity database over different land cover types. Furthermore, snow cover can heavily impact the change in land cover emissivity, especially in winter.

An approach to compute the C factor for universal soil loss equation using EOS-MODIS vegetation index (VI)

NASA Astrophysics Data System (ADS)

Li, Hui; He, Huizhong; Chen, Xiaoling; Zhang, Lihua

2008-12-01

C factor, known as cover and management factor in USLE, is one of the most important factors since it represents the combined effects of plant, soil cover and management on erosion, whereas it also most easily changed variables by men for it itself is time-variant and the uncertainty nature. So it's vital to compute C factor properly in order to model erosion effectively. In this paper we attempt to present a new method for calculating C value using Vegetation Index (VI) derived from multi-temporal MODIS imagery, which can estimate C factor in a more scientific way. Based on the theory that C factor is strongly correlated with VI, the average annual C value is estimated by adding the VI value of three growth phases within a year with different weights. Modified Fournier Index (MFI) is employed to determine the weight of each growth phase for the vegetation growth and agricultural activities are significantly influenced by precipitation. The C values generated by the proposed method were compared with that of other method, and the results showed that the results of our method is highly correlated with the others. This study is helpful to extract C value from satellite data in a scientific and efficient way, which in turn could be used to facilitate the prediction of erosion.

Resolution Enhancement of MODIS-derived Water Indices for Studying Persistent Flooding

NASA Astrophysics Data System (ADS)

Underwood, L. W.; Kalcic, M. T.; Fletcher, R. M.

2012-12-01

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Resolution Enhancement of MODIS-Derived Water Indices for Studying Persistent Flooding

NASA Technical Reports Server (NTRS)

Underwood, L. W.; Kalcic, Maria; Fletcher, Rose

2012-01-01

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Large-Scale Controls and Characteristics of Fire Activity in Central Chile, 2001-2015

NASA Astrophysics Data System (ADS)

McWethy, D. B.; Pauchard, A.; García, R.; Holz, A.; González, M.; Veblen, T. T.; Stahl, J.

2016-12-01

In recent decades, fire activity has increased in many ecosystems worldwide, even where fuel conditions and natural ignitions historically limited fire activity, and this increase begs questions of whether climate change, land-use change, and/or altered vegetation are responsible. Increased frequency of large fires in these settings has been attributed to drier-than-average summers and longer fire seasons as well as fuel accumulation related to ENSO events, raising concerns about the trajectory of post-fire vegetation dynamics and future fire regimes. In temperate and Mediterranean forests of central Chile, recent large fires associated with altered ecosystems, climate variability and land-use change highlight the risk and hazard of increasing fire activity yet the causes and consequences are poorly understood. To better understand characteristics of recent fire activity, key drivers of fire occurrence and the spatial probability of wildfire we examined the relationship between fire activity derived from MODIS satellite imagery and biophysical, land-cover and land-use variables. The probability of fire occurrence and annual area burned was best predicted by seasonal precipitation, annual temperature and land cover type. The likelihood of fire occurrence was greatest in Matorral shrublands, agricultural lands (including pasture lands) and Pinus and Eucalyptus plantations, highlighting the importance of vegetation type and fuel flammability as a critical control on fire activity. Our results suggest that land-use change responsible for the widespread presence of highly flammable vegetation and projections for continued warming and drying will likely combine to promote the occurrence of large fires in central Chile in the future.

Detailed Evaluation of MODIS Fire Radiative Power Measurements

NASA Technical Reports Server (NTRS)

Ichoku, Charles

2010-01-01

Satellite remote sensing is providing us tremendous opportunities to measure the fire radiative energy (FRE) release rate or power (FRP) from open biomass burning, which affects many vegetated regions of the world on a seasonal basis. Knowledge of the biomass burning characteristics and emission source strengths of different (particulate and gaseous) smoke constituents is one of the principal ingredients upon which the assessment, modeling, and forecasting of their distribution and impacts depend. This knowledge can be gained through accurate measurement of FRP, which has been shown to have a direct relationship with the rates of biomass consumption and emissions of major smoke constituents. Over the last decade or so, FRP has been routinely measured from space by both the MODIS sensors aboard the polar orbiting Terra and Aqua satellites, and the SEVIRI sensor aboard the Meteosat Second Generation (MSG) geostationary satellite. During the last few years, FRP has been gaining recognition as an important parameter for facilitating the development of various scientific studies relating to the quantitative characterization of biomass burning and their emissions. Therefore, we are conducting a detailed analysis of the FRP products from MODIS to characterize the uncertainties associated with them, such as those due to the MODIS bow-tie effects and other factors, in order to establish their error budget for use in scientific research and applications. In this presentation, we will show preliminary results of the MODIS FRP data analysis, including comparisons with airborne measurements.

Monitoring Lakes in Africa with Altimetry and GRACE

NASA Astrophysics Data System (ADS)

Carabajal, C. C.; Boy, J. P.

2017-12-01

Thanks to more than two decades of radar altimetry measurements from TOPEX/POSEIDON, Jason 1, 2 and 3, ENVISAT and others, 18 Ice, Cloud and Land Elevation Satellite (ICESat) laser altimeter measurement campaigns over 6 years, and 15 years of Gravity Recovery And Climate Experiment (GRACE) observations, water levels changes of major lakes and reservoirs can be remotely measured regularly with unprecedented precision, facilitating monitoring of continental water storage variations. Smaller footprint laser altimeters like ICESat are more suitable for the retrieval of water level variations of small inland water bodies, better discriminating water returns when water height measurements have the potential to be contaminated by land or vegetation. Using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) contemporaneous with the altimetry data collections, in combination with careful examination of the laser waveforms, one can better isolate returns form the water surface. Combining these altimetry observations, we derive and compare water height estimates for several lakes and reservoirs in Africa from radar and laser altimetry measurements, we estimate the surface extent of each individual water body from available MODIS imagery, and derive corresponding estimates of volume variations for each water body. Mass variations from time-variable gravity measurements from the GRACE mission, using the latest one-degree global iterated mascons solution from GSFC are then transformed into volume changes, assuming a constant density, and compared to altimetry plus imagery estimates. These methods demonstrate the power of combined observations to monitor water resources and facilitate their management. Upcoming laser altimetry missions like ICESat-2, with vastly improved coverage and temporal sampling, continuous observations, better measurements techniques, including inland water products specifically formulated for these applications, when combined with SWOT, and GRACE-follow on mission data, will help address the need for continuous monitoring of continental water storage variations from space measurements.

An intercomparison of three remote sensing-based energy balance models using large aperture scintillometer measurements over a wheat-corn production region

USDA-ARS?s Scientific Manuscript database

This paper compares three remote sensing-based models for estimating evapotranspiration (ET), namely the Surface Energy Balance System (SEBS), the Two-Source Energy Balance (TSEB) model, and the surface Temperature-Vegetation index Triangle (TVT). The models used as input MODIS/TERRA products and gr...

Validation of solar radiation surfaces from MODIS and reanalysis data over topographically complex terrain

Treesearch

Todd A. Schroeder; Robbie Hember; Nicholas C. Coops; Shunlin Liang

2009-01-01

The magnitude and distribution of incoming shortwave solar radiation (SW) has significant influence on the productive capacity of forest vegetation. Models that estimate forest productivity require accurate and spatially explicit radiation surfaces that resolve both long- and short-term temporal climatic patterns and that account for topographic variability of the land...

The browning of Alaska's boreal forest

Treesearch

Mary Beth Parent; David Verbyla

2010-01-01

We used twelve Landsat scenes from the 1980s-2009 and regional 2000-2009 MODIS data to examine the long-term trend in the normalized difference vegetation index (NDVI) within unburned areas of the Alaskan boreal forest. Our analysis shows that there has been a declining trend in NDVI in this region, with the strongest "browning trend" occurring in eastern...

Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem

USDA-ARS?s Scientific Manuscript database

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of the extreme events will be manifest in the eco...

Estimation of land photosynthetically active radiation in clear sky using MODIS atmosphere and land products

NASA Astrophysics Data System (ADS)

Xie, Xiaoping; Gao, Wei; Gao, Zhiqiang

2008-08-01

Photosynthetically active radiation (PAR) is an essential parameter in vegetation growth model and soil carbon sequestration models. A method is presented with which instantaneous PAR can be calculated with high accuracy from Moderate Resolution Imaging Spectroradiometer (MODIS) atmosphere and land products. The method is based on a simplification of the general radiative transfer equation, which considers five major processes of attenuation of solar radiation: Rayleigh scattering, absorption by ozone and water vapor, aerosol scattering, multiply reflectance between surface and atmosphere. Comparing 108 retrieveled results to filed measured PAR in Yucheng station of Chinese Ecosystem Research Network (CERN) in 2006, and the r-square of 0.855 indicates that the computed results can interpret actual PAR well.

An Imager Gaussian Process Machine Learning Methodology for Cloud Thermodynamic Phase classification

NASA Astrophysics Data System (ADS)

Marchant, B.; Platnick, S. E.; Meyer, K.

2017-12-01

The determination of cloud thermodynamic phase from MODIS and VIIRS instruments is an important first step in cloud optical retrievals, since ice and liquid clouds have different optical properties. To continue improving the cloud thermodynamic phase classification algorithm, a machine-learning approach, based on Gaussian processes, has been developed. The new proposed methodology provides cloud phase uncertainty quantification and improves the algorithm portability between MODIS and VIIRS. We will present new results, through comparisons between MODIS and CALIOP v4, and for VIIRS as well.

Harmful Algal Blooms in the Mississippi Sound and Mobile Bay: Using MODIS Aqua and In Situ Data for HABs in the Northern Gulf of Mexico

DTIC Science & Technology

2007-10-01

Blooms in the Mississippi Sound & Mobile Bay: Using MODIS Aqua & In Situ Data for HABs in the Northern Gulf of Mexico 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...shores. Phytoplankton populations and in situ water quality were monitored at 3 to 6 week intervals at 17 locations in Mobile Bay & the Mississippi...Sound beginning in July, 2005 & continuing thru June, 2006 along w/ concurrent MODIS Aqua weekly composite or same-day imagery. In situ or satellite

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

USGS Publications Warehouse

Ji, Lei; Zhang, Li; Rover, Jennifer R.; Wylie, Bruce K.; Chen, Xuexia

2014-01-01

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

Photosynthetic Efficiency of Northern Forest Ecosystems Using a MODIS-Derived Photochemical Reflectance Index (PRI)

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Huemmrich, K. F.; Landis, D. R.; Black, T. A.; Barr, A. G.; McCaughey, J. H.

2016-01-01

This study evaluates a direct remote sensing approach from space for the determination of ecosystem photosynthetic light use efficiency (LUE), through measurement of vegetation reflectance changes expressed with the Photochemical Reflectance Index (PRI). The PRI is a normalized difference index based on spectral changes at a physiologically active wavelength (approximately 531 nanometers) as compared to a reference waveband, and is only available from a very few satellites. These include the two Moderate-Resolution Imaging Spectroradiometers (MODIS) on the Aqua and Terra satellites each of which have a narrow (10-nanometer) ocean band centered at 531 nanometers. We examined several PRI variations computed with candidate reference bands, since MODIS lacks the traditional 570-nanometer reference band. The PRI computed using MODIS land band 1 (620-670 nanometers) gave the best performance for daily LUE estimation. Through rigorous statistical analyses over a large image collection (n equals 420), the success of relating in situ daily tower-derived LUE to MODIS observations for northern forests was strongly influenced by satellite viewing geometry. LUE was calculated from CO2 fluxes (moles per moles of carbon absorbed quanta) measured at instrumented Canadian Carbon Program flux towers in four Canadian forests: a mature fir site in British Columbia, mature aspen and black spruce sites in Saskatchewan, and a mixed deciduous/coniferous forest site in Ontario. All aspects of the viewing geometry had significant effects on the MODIS-PRI, including the view zenith angle (VZA), the view azimuth angle, and the displacement of the view azimuth relative to the solar principal plane, in addition to illumination related variables.Nevertheless, we show that forward scatter sector views (VZA, 16 degrees-45 degrees) provided the strongest relationships to daily LUE, especially those collected in the early afternoon by Aqua (r squared = 0.83, RMSE (root mean square error) equals 0.003 moles per moles of carbon absorbed quanta). Nadir (VZA, 0 degrees plus or minus 15 degrees) and backscatter views (VZA, -16 degrees to -45 degrees) had lower performance in estimating LUE (nadir: r squared approximately equal to 0.62-0.67; backscatter: r squared approximately equal to 0.54-0.59) and similar estimation error (RMSE equals 0.004-0.005).When directional effects were not considered, only a moderately successful MODIS-PRI vs. LUE relationship (r squared equals 0.34, RMSE equals 0.007) was obtained in the full dataset (all views & sites, both satellites), but site-specific relationships were able to discriminate between coniferous and deciduous forests. Overall, MODIS-PRI values from Terra (late morning) were higher than those from Aqua (early afternoon), before/after the onset of diurnal stress responses expressed spectrally. Therefore, we identified ninety-two Terra-Aqua "same day" pairs, for which the sum of Terra morning and Aqua afternoon MODIS-PRI values (PRI (sub sum) using all available directional observations was linearly correlated with daily tower LUE (r squared equals 0.622, RMSE equals 0.013) and independent of site differences or meteorological information. Our study highlights the value of off-nadir directional reflectance observations, and the value of pairing morning and afternoon satellite observations to monitor stress responses that inhibit carbon uptake in Canadian forest ecosystems. In addition, we show that MODIS-PRI values, when derived from either: (i) forward views only, or (ii) Terra/Aqua same day (any view) combined observations, provided more accurate estimates of tower-measured daily LUE than those derived from either nadir or backscatter views or those calculated by the widely used semi-operational MODIS GPP model (MOD17) which is based on a theoretical maximum LUE and environmental data. Consequently, we demonstrate the importance of diurnal as well as off-nadir satellite observations for detecting vegetation physiological processes.

Can the physical properties associated with uncertainties in the NASA MODIS AOD retrievals in the western U.S. be determined?

NASA Astrophysics Data System (ADS)

Loria Salazar, S. M.; Holmes, H.; Panorska, A. K.; Arnott, W. P.; Barnard, J.

2016-12-01

Previous investigations have used satellite remote sensing to estimate surface air pollution concentrations. While most of these studies rely on models developed for the dark-vegetated eastern U.S., they are being used in the semi-arid western U.S without modifications. These models are not robust in the western U.S. due to: 1. Irregular topography that leads to complicated boundary layer physics, 2. Pollutant mixtures, 3. Heterogeneous vertical profile of aerosol concentrations, and 4. High surface reflectance. Here, results from Nevada and California demonstrate poor AOD correlation between AERONET MODIS retrievals. Smoke from wildfires strengthened the aerosol signal, but the MODIS versus AERONET AOD correlation did not improve significantly during fire events [r2 0.17 (non-fire), r2 0.2 (fire)]. Furthermore, aerosol from fires increased the normalized mean bias (NMB) of MODIS retrievals of AOD[NMB 82% (non-fire), NMB 146% (fire)]. Additional results of this investigation found that aerosol plumes confined with the boundary layer improves MODIS AOD retrievals. However, when this condition is not met (i.e., 70% of the time downwind of mountains regions) MODIS AOD has a poor correlation and high bias with respect to AERONET AOD. Fire injection height, complicated boundary layer mixing, and entrainment disperse smoke plumes into the free atmosphere. Therefore, smoke plumes exacerbate the complex aerosol transport typical in the western U.S. and the non-linear relationship between surface pollutant concentrations and conditions aloft. This work uses stochastic methods, including regression to investigate the influence of each of these physical behaviors on the MODIS, AERONET AOD discrepancy using surrogates for each physical phenomenon, e.g., surface albedo for surface reflectance, boundary layer height and elevation for complex mixing, aerosol optical height for vertical aerosol concentrations, and fire radiative power for smoke plume injection height.

Remote Sensing of Cloud, Aerosol, and Water Vapor Properties from MODIS

NASA Technical Reports Server (NTRS)

King, Michael D.

2001-01-01

MODIS is an earth-viewing cross-track scanning spectroradiometer launched on the Terra satellite in December 1999. MODIS scans a swath width sufficient to provide nearly complete global coverage every two days from a polar-orbiting, sun-synchronous, platform at an altitude of 705 km, and provides images in 36 spectral bands from 0.415 to 14.235 microns with spatial resolutions of 250 m (2 bands), 500 m (5 bands) and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmospheric processes. In this presentation I will review the comprehensive set of remote sensing algorithms that have been developed for the remote sensing of atmospheric properties using MODIS data, placing primary emphasis on the principal atmospheric applications of: (1) developing a cloud mask for distinguishing clear sky from clouds, (2) retrieving global cloud radiative and microphysical properties, including cloud top pressure and temperature, effective emissivity, cloud optical thickness, thermodynamic phase, and effective radius, (3) monitoring tropospheric aerosol optical thickness over the land and ocean and aerosol size distribution over the ocean, (4) determining atmospheric profiles of moisture and temperature, and (5) estimating column water amount. The physical principles behind the determination of each of these atmospheric products will be described, together with an example of their application using MODIS observations. All products are archived into two categories: pixel-level retrievals (referred to as Level-2 products) and global gridded products at a latitude and longitude resolution of 1 deg (Level-3 products). An overview of the MODIS atmosphere algorithms and products, status, validation activities, and early level-2 and -3 results will be presented. Finally, I will present some highlights from the land and ocean algorithms developed for processing global MODIS observations, including: (1) surface reflectance, (2) vegetation indices, leaf area index, and FPAR, (3) albedo and nadir BRDF-adjusted reflectance, (4) normalized water-leaving radiance, (5) chlorophyll-a concentration, and (6) sea surface temperature.

Applications of Near Real-Time Image and Fire Products from MODIS

NASA Astrophysics Data System (ADS)

Schmaltz, J. E.; Ilavajhala, S.; Teague, M.; Ye, G.; Masuoka, E.; Davies, D.; Murphy, K. J.; Michael, K.

2010-12-01

NASA’s MODIS Rapid Response Project (http://rapidfire.sci.gsfc.nasa.gov/) has been providing MODIS fire detections and imagery in near real-time since 2001. The Rapid Response system is part of the Land and Atmospheres Near-real time Capability for EOS (LANCE-MODIS) system. Current capabilities include providing MODIS imagery in true color and false color band combinations, a vegetation index, and temperature - in both uncorrected swath format and geographically corrected subset regions. The geographically-corrected subsets images cover the world's land areas and adjoining waters, as well as the entire Arctic and Antarctic. These data are available within a few hours of data acquisition. The images are accessed by large number of user communities to obtain a rapid, 250 meter-resolution overview of ground conditions for fire management, crop and famine monitoring and forecasting, disaster response (fires, oil spills, floods, storms), dust and aerosol monitoring, aviation (tracking volcanic ash), monitoring sea ice conditions, environmental monitoring, and more. In addition, the scientific community uses imagery to locate phenomena of interest prior to ordering and processing data and to support the day-to-day planning of field campaigns. The MODIS Rapid Response project has also been providing a near real-time data feed on fire locations and MODIS imagery subsets to the Fire Information for Resource Management System (FIRMS) project (http://maps.geog.umd.edu/firms). FIRMS provides timely availability of fire location information, which is essential in preventing and fighting large forest/wild fires. Products are available through a WebGIS for visualizing MODIS hotspots and MCD45 Burned Area images, an email alerting tool to deliver fire data on daily/weekly/near real-time basis, active data downloads in formats such as shape, KML, CSV, WMS, etc., along with MODIS imagery subsets. FIRMS’ user base covers more than 100 countries and territories. A recent user survey showed that a majority of people use FIRMS for the purposes of conservation, fire-fighting, natural hazard monitoring, land cover change monitoring, and climate change. FIRMS has also developed a cellphone-based text (SMS) alerting of MODIS and MSG-detected fires in near real-time in South Africa, in partnership with the South African power company ESKOM and Council of Scientific and Industrial Research, South Africa. FIRMS was transitioned to the United Nations Food and Agricultural Organization (UN-FAO) in Rome, Italy in Spring 2010 and is scheduled to become a part of the LANCE-MODIS data system (http://lance.nasa.gov/) in Spring 2011.

An efficient approach for pixel decomposition to increase the spatial resolution of land surface temperature images from MODIS thermal infrared band data.

PubMed

Wang, Fei; Qin, Zhihao; Li, Wenjuan; Song, Caiying; Karnieli, Arnon; Zhao, Shuhe

2014-12-25

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250-500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.

Satellite derived forest phenology and its relation with nephropathia epidemica in Belgium.

PubMed

Barrios, José Miguel; Verstraeten, Willem W; Maes, Piet; Clement, Jan; Aerts, Jean-Marie; Haredasht, Sara Amirpour; Wambacq, Julie; Lagrou, Katrien; Ducoffre, Geneviève; Van Ranst, Marc; Berckmans, Daniel; Coppin, Pol

2010-06-01

The connection between nephropathia epidemica (NE) and vegetation dynamics has been emphasized in recent studies. Changing climate has been suggested as a triggering factor of recently observed epidemiologic peaks in reported NE cases. We have investigated whether there is a connection between the NE occurrence pattern in Belgium and specific trends in remotely sensed phenology parameters of broad-leaved forests. The analysis of time series of the MODIS Enhanced Vegetation Index revealed that changes in forest phenology, considered in literature as an effect of climate change, may affect the mechanics of NE transmission.

Satellite Derived Forest Phenology and Its Relation with Nephropathia Epidemica in Belgium

PubMed Central

Barrios, José Miguel; Verstraeten, Willem W.; Maes, Piet; Clement, Jan; Aerts, Jean-Marie; Haredasht, Sara Amirpour; Wambacq, Julie; Lagrou, Katrien; Ducoffre, Geneviève; Van Ranst, Marc; Berckmans, Daniel; Coppin, Pol

2010-01-01

The connection between nephropathia epidemica (NE) and vegetation dynamics has been emphasized in recent studies. Changing climate has been suggested as a triggering factor of recently observed epidemiologic peaks in reported NE cases. We have investigated whether there is a connection between the NE occurrence pattern in Belgium and specific trends in remotely sensed phenology parameters of broad-leaved forests. The analysis of time series of the MODIS Enhanced Vegetation Index revealed that changes in forest phenology, considered in literature as an effect of climate change, may affect the mechanics of NE transmission. PMID:20644685

Evaluation of vegetation post-fire resilience in the Alpine region using descriptors derived from MODIS spectral index time series

NASA Astrophysics Data System (ADS)

Di Mauro, Biagio; Fava, Francesco; Busetto, Lorenzo; Crosta, Giovanni Franco; Colombo, Roberto

2013-04-01

In this study a method based on the analysis of MODerate-resolution Imaging Spectroradiometer (MODIS) time series is proposed to estimate the post-fire resilience of mountain vegetation (broadleaf forest and prairies) in the Italian Alps. Resilience is defined herewith as the ability of a dynamical system to counteract disturbances. It can be quantified by the amount of time the disturbed system takes to resume, in statistical terms, an ecological functionality comparable with its undisturbed behavior. Satellite images of the Normalized Difference Vegetation Index (NDVI) and of the Enhanced Vegetation Index (EVI) with spatial resolution of 250m and temporal resolution of 16 days in the 2000-2012 time period were used. Wildfire affected areas in the Lombardy region between the years 2000 and 2010 were analysed. Only large fires (affected area >40ha) were selected. For each burned area, an undisturbed adjacent control site was located. Data pre-processing consisted in the smoothing of MODIS time series for noise removal and then a double logistic function was fitted. Land surface phenology descriptors (proxies for growing season start/end/length and green biomass) were extracted in order to characterize the time evolution of the vegetation. Descriptors from a burned area were compared to those extracted from the respective control site by means of the one-way analysis of variance. According to the number of subsequent years which exhibit statistically meaningful difference between burned and control site, five classes of resilience were identified and a set of thematic maps was created for each descriptor. The same method was applied to all 84 aggregated events and to events aggregated by main land cover. EVI index results more sensitive to fire impact than NDVI index. Analysis shows that fire causes both a reduction of the biomass and a variation in the phenology of the Alpine vegetation. Results suggest an average ecosystem resilience of 6-7 years. Moreover, broadleaf forest and prairies show different post-fire behavior in terms of land surface phenology descriptors. In addition to the above analysis, another method is proposed, which derives from the qualitative theory of dynamical systems. The (time dependent) spectral index of a burned area over the period of one year was plotted against its counterpart from the control site. Yearly plots (or scattergrams) before and after the fire were obtained. Each plot is a sequence of points on the plane, which are the vertices of a generally self-intersecting polygonal chain. Some geometrical descriptors were obtained from the yearly chains of each fire. Principal Components Analysis (PCA) of geometrical descriptors was applied to a set of case studies and the obtained results provide a system dynamics interpretation of the natural process.

Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations

NASA Astrophysics Data System (ADS)

de Moura, Yhasmin Mendes; Galvão, Lênio Soares; Hilker, Thomas; Wu, Jin; Saleska, Scott; do Amaral, Cibele Hummel; Nelson, Bruce Walker; Lopes, Aline Pontes; Wiedeman, Kenia K.; Prohaska, Neill; de Oliveira, Raimundo Cosme; Machado, Carolyne Bueno; Aragão, Luiz E. O. C.

2017-09-01

The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyperspectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3-5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, p<0.05). The estimated crop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Prediction of Continental-Scale Net Ecosystem Carbon Exchange by Combining MODIS and AmeriFlux Data

NASA Astrophysics Data System (ADS)

Xiao, J.; Zhuang, Q.

2007-12-01

There is growing interest in scaling up net ecosystem exchange (NEE) measured at eddy covariance flux towers to regional scales. Here we used remote sensing data from the MODIS instrument on board NASA's Terra satellite to extrapolate NEE measured at AmeriFlux sites to the continental scale. We combined MODIS data and NEE measurements from a number of AmeriFlux sites with a variety of vegetation types (e.g., forests, grasslands, shrublands, savannas, and croplands) to develop a predictive NEE model using a regression tree approach. The model was trained using 2000-2003 NEE measurements, and the performance of the model was evaluated using independent data over the period 2004-2006. We found that the model predicted NEE with reasonable accuracy at the continental scale. The R-squared values are 0.50 for all vegetation types combined and 0.72 for deciduous forests. We then applied the model to the conterminous U.S. and predicted NEE for each 500m by 500m cell over the period 2001-2006. Based on the wall-to-wall NEE estimates, we examined the spatial and temporal distributions of annual NEE and interannual variability of annual NEE across the conterminous U.S. over the study period (2001-2006). Our scaling-up approach implicitly considered the effects of climate variability, land use/land cover change, disturbances, extreme climate events, and management practices, and thus our annual NEE estimates represents the net carbon fluxes between the terrestrial biosphere and the atmosphere in the conterminous U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xuecao; Zhou, Yuyu; Asrar, Ghassem R.

Seasonal phenology of vegetation plays an important role in global carbon cycle and ecosystem productivity. In urban environments, vegetation phenology is also important because of its influence on public health (e.g., allergies), and energy demand (e.g. cooling effects). In this study, we studied the potential use of remotely sensed observations (i.e. Landsat data) to derive some phenology indicators for vegetation embedded within the urban core domains in four distinctly different U.S. regions (Washington, D.C., King County in Washington, Polk County in Iowa, and Baltimore City and County in Maryland) during the past three decades. We used all available Landsat observationsmore » (circa 3000 scenes) from 1982 to 2015 and a self-adjusting double logistic model to detect and quantify the annual change of vegetation phenophases, i.e. indicators of seasonal changes in vegetation. The proposed model can capture and quantify not only phenophases of dense vegetation in rural areas, but also those of mixed vegetation in urban core domains. The derived phenology indicators show a good agreement with similar indicators derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and in situ observations, suggesting that the phenology dynamic depicted by the proposed model is reliable. The vegetation phenology and its seasonal and interannual dynamics demonstrate a distinct spatial pattern in urban domains with an earlier (9–14 days) start-of season (SOS) and a later (13–20 days) end-of season (EOS), resulting in an extended (5–30 days) growing season length (GSL) when compared to the surrounding suburban and rural areas in the four study regions. There is a general long-term trend of decreasing SOS (-0.30 day per year), and increasing EOS and GSL (0.50 and 0.90 day per year, respectively) over past three decades for these study regions. The magnitude of these trends varies among the four urban systems due to their diverse local climate conditions, vegetation types, and different urban-rural settings. The Landsat derived phenology information for urban domains provides more details when compared to the coarse-resolution datasets such as MODIS, thus improves our understanding of human-natural systems interactions (or feedbacks) in urban domains. Such information is very valuable for urban planning in light of rapid urbanization and expansion of major metropolitans at the national and global levels.« less

Identifying Severe Weather Impacts and Damage with Google Earth Engine

NASA Astrophysics Data System (ADS)

Molthan, A.; Burks, J. E.; Bell, J. R.

2015-12-01

Hazards associated with severe convective storms can lead to rapid changes in land surface vegetation. Depending upon the type of vegetation that has been impacted, their impacts can be relatively short lived, such as damage to seasonal crops that are eventually removed by harvest, or longer-lived, such as damage to a stand of trees or expanse of forest that require several years to recover. Since many remote sensing imagers provide their highest spatial resolution bands in the red and near-infrared to support monitoring of vegetation, these impacts can be readily identified as short-term and marked decreases in common vegetation indices such as NDVI, along with increases in land surface temperature that are observed at a reduced spatial resolution. The ability to identify an area of vegetation change is improved by understanding the conditions that are normal for a given time of year and location, along with a typical range of variability in a given parameter. This analysis requires a period of record well beyond the availability of near real-time data. These activities would typically require an analyst to download large volumes of data from sensors such as NASA's MODIS (aboard Terra and Aqua) or higher resolution imagers from the Landsat series of satellites. Google's Earth Engine offers a "big data" solution to these challenges, by providing a streamlined API and option to process the period of record of NASA MODIS and Landsat products through relatively simple Javascript coding. This presentation will highlight efforts to date in using Earth Engine holdings to produce vegetation and land surface temperature anomalies that are associated with damage to agricultural and other vegetation caused by severe thunderstorms across the Central and Southeastern United States. Earth Engine applications will show how large data holdings can be used to map severe weather damage, ascertain longer-term impacts, and share best practices learned and challenges with applying Earth Engine holdings to the analysis of severe weather damage. Other applications are also demonstrated, such as use of Earth Engine to prepare pre-event composites that can be used to subjectively identify other severe weather impacts. Future extension to flooding and wildfires is also proposed.

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2012-01-01

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Vegetation Function and Physiology: Photosynthesis, Fluorescence and Non-photochemical Quenching (NPQ)

NASA Astrophysics Data System (ADS)

Zhang, Q.; Yao, T.

2017-12-01

Photosynthesis is a basic physiological function of vegetation that relies on PAR provided through photosynthetic pigments (mainly chlorophyll) for plant growth and biomass accumulation. Vegetation chlorophyll (chl) content and non-chlorophyll (non-chl) components vary with plant functional types (PFTs) and growing stages. The PAR absorbed by canopy chlorophyll (APARchl) is associated with photosynthesis (i.e., gross primary production, GPP) while the PAR absorbed by canopy non-chl components (APARnon-chl) is not associated with photosynthesis. Under non-optimal environmental conditions, vegetation is "stressed" and both photosynthesis (GPP) and light use efficiency are reduced, therefore, excess portions of APARchl are discarded as fluorescence or non-photochemical quenching (NPQ). The photochemical reflectance index (PRI) is a measurement related to NPQ. Both PRI and yield of solar induced chlorophyll fluorescence (SIFyield = SIF/APARchl) have been proposed as possible bio-indicators of LUEchl. We have successfully developed an algorithm to distinguish between chlorophyll and non-chl components of vegetation, and to retrieve fractional absorptions of PAR by chlorophyll (fAPARchl) and by non-chl components (fAPARnon-chl) with surface reflectance of MODIS bands 1 - 7. A method originally pioneered by Hanan et al. (2002) has been used to retrieve fAPAR for vegetation photosynthesis (fAPARPSN) at flux tower sites based on the light response curve of tower net ecosystem exchange (NEE) and incident PAR at low light intensity. We have also retrieved the PRI from MODIS data (bands 11 and 1) and have derived SIFyield with the Global Ozone Monitoring Experiment - 2 (GOME-2) SIF data. We find that fAPARPSN at flux tower sites matches well with site fAPARchl, and ratio fAPARnon-chl/fAPARchl varies largely. APARchl can explain >=78% variation in seasonal GPP . We disentangle the possible impact of fAPARchl on PRI from physiological stress response, disentangle the possible impact of APARchl on SIFyield from physiological stress response, and find that integrating three bio-parameters fAPARchl, PRI and SIFyield can explain >=87% variation in seasonal GPP . Therefore, quantifying fAPARchl, PRI and SIF has the best potential to monitor vegetation function and physiology.

Analysis of Vegetation Index Variations and the Asian Monsoon Climate

NASA Technical Reports Server (NTRS)

Shen, Sunhung; Leptoukh, Gregory G.; Gerasimov, Irina

2012-01-01

Vegetation growth depends on local climate. Significant anthropogenic land cover and land use change activities over Asia have changed vegetation distribution as well. On the other hand, vegetation is one of the important land surface variables that influence the Asian Monsoon variability through controlling atmospheric energy and water vapor conditions. In this presentation, the mean and variations of vegetation index of last decade at regional scale resolution (5km and higher) from MODIS have been analyzed. Results indicate that the vegetation index has been reduced significantly during last decade over fast urbanization areas in east China, such as Yangtze River Delta, where local surface temperatures were increased significantly in term of urban heat Island. The relationship between vegetation Index and climate (surface temperature, precipitation) over a grassland in northern Asia and over a woody savannas in southeast Asia are studied. In supporting Monsoon Asian Integrated Regional Study (MAIRS) program, the data in this study have been integrated into Giovanni, the online visualization and analysis system at NASA GES DISC. Most images in this presentation are generated from Giovanni system.

Characterizing phenological vegetation dynamics amidst extreme climate variability in Australia with MODIS VI data

NASA Astrophysics Data System (ADS)

Broich, M.; Huete, A. R.; Xuanlon, M.; Davies, K.; Restrepo-Coupe, N.; Ratana, P.

2012-12-01

Australia's climate is extremely variable with inter-annual rainfall at any given site varying by 5- or 6-fold or more, across the continent. In addition to such inter-annual variability, there can be significant intra-annual variability, especially in monsoonal Australia (e.g. the wet tropical savannas) and Mediterranean climates in SW Australia where prolonged dry seasons occur each year. This presents unique challenges to the characterization of seasonal dynamics with satellite datasets. In contrast to annual reoccurring temperature-driven phenology of northern hemisphere mid-latitudes, vegetation dynamics of the vast and dry Australian interior are poorly quantified by existing remote sensing products. For example, in the current global-based MODIS phenology product, central Australia is covered by ~30% fill values for any given year. Two challenges are specific to Australian landscapes: first, the difficulty of characterizing seasonality of rainfall-driven ecosystems in interior Australia where duration and magnitude of green-up and brown down cycles show high inter annual variability; second, modeling two phenologic layers, the trees and the grass in savannas were the trees are evergreen but the herbaceous understory varies with rainfall. Savannas cover >50% of Australia. Australia's vegetation and climate are different from other continents. A MODIS phenology product capable of characterizing vegetation dynamics across the continent is being developed in this research as part of the AusCover national expert network aiming to provide Australian biophysical remote sensing data time-series and continental-scale map products. These products aim to support the Terrestrial Ecosystem Research Network (TERN) serving ecosystem research in Australia. The MODIS land surface product for Australia first searches the entire time series of each Climate Modeling Grid pixel for low-high-low extreme point sequences. A double logistic function is then fit to each of these sequences allowing identification of growth periods with different magnitudes and durations anywhere in the time series. Results show that the highest absolute variability in peak greenness occurred in cropped areas while the highest relative variability (coefficient of variation) occurred in interior Australia particularly around Lake Eyre, the center of a closed drainage basin in the dry interior of the continent. Across the desert interior, the timing of the green-up onset and the peak greenness was correlated with the landfall of cyclones and the inland penetration and strength of the north Australian summer monsoon (represented by TRMM data). The variability of Australian land surface phenology magnitude and timing was found to be strongly correlated with the swings between La Nina and El Nino events. The information on vegetation dynamics represented here is critical for land surface, fuel accumulation, agricultural production, and permanent ecosystem change modeling in relation to climate trends. A unique research opportunity is provided by recent climate variability: in 2010 a persistent El Nino has given way to a strong two-year La Nina breaking a decade long drought that was followed by record-breaking rainfall across most of the continent and extensive flooding followed by sustained greening.

Use of MODIS Satellite Images and an Atmospheric Dust Transport Model to Evaluate Juniperus spp. Pollen Phenology and Transport

NASA Technical Reports Server (NTRS)

Luvall, J. C.; Sprigg, W. A.; Levetin, E.; Huete, A.; Nickovic, S.; Pejanovic, G. A.; Vukovic, A.; Van de Water, P. K.; Myers, O. B.; Budge, A. M.;

Impact of drought on surface albedo in Canadian Prairie observed from Terra- MODIS

NASA Astrophysics Data System (ADS)

Luo, Y.; Trishchenko, A. P.; Wang, S.; Khlopenkov, K. V.

2009-05-01

A new technology was developed at the Canada Centre for Remote Sensing (CCRS) for generating Canada wide clear-sky surface albedo data based on observations from MODIS sensor onboard TERRA satellite. The data include all seven MODIS land bands (B1-B7) mapped at 250m spatial resolution and 10-day temporal interval from year 2000 through 2008. The new product presents an important spatial enhancement as well as an improved retrieval of water fraction and snow characteristics relative to the standard MODIS archival products. The regional data for the entire Canadian Prairie region are extracted and aggregated for different ecozones, such as north to south, the boreal transition, aspen parkland, moist mixed grassland, and mixed grassland etc. The preliminary results indicate that in comparison to normal summer conditions (2006-2008), the albedo for the drought years (2000-2003) summer increases up to 20 percent in the visible band (B1) and decreases as low as 10 percent in the near infrared band (B2). In the shortwave infrared band (B6) where a large absorption by leaf water occurs, the albedo increases as much as 15 percent for the drought years due to less leaf water content. The derived Normalized Difference Vegetation Index (NDVI), which represents a density of healthy vegetation, drops dramatically (up to 30 percent) for the drought period of 2000-2003. Among the different ecozones, the grassland shows the largest response to droughts while the boreal zone shows the least. Further applications of this product include mapping of snow cover (fraction and grain size), the fraction of absorbed photo-synthetically active radiation (fAPAR), ecosystem productivity, water and energy budget, as well as impact of various disturbances, such as wildfires, and long term climate induced trends. This work was conducted at the Canada Centre for Remote Sensing (CCRS), Earth Sciences Sector of the Department of Natural Resources Canada as part of the Project J35 of the Program on "Enhancing Resilience in a Changing Climate". This work was also supported by the Canadian Space Agency under the Government Related Initiative Program (GRIP) and the Canadian IPY program. The MODIS data files were acquired from the NASA Distributed Data Archive Center (DAAC).

Use Of MODIS Satellite Images And An Atmospheric Dust Transport Model To Evaluate Juniperus Spp. Pollen Phenology And Transport

NASA Astrophysics Data System (ADS)

Luvall, J. C.; Sprigg, W. A.; Levetin, E.; Huete, A. R.; Nickovic, S.; Crimmins, T. M.; Van De Water, P. K.; Pejanovic, G.; Vukovic, A. J.; Myers, O.; Budge, A.; Zelicoff, A.; Bunderson, L.; Ponce-Campos, G.

2011-12-01

Pollen can be transported great distances. Van de Water et al., 2003 reported Juniperus spp. pollen, a significant aeroallergen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. Direct detection of pollen via satellite is not practical. A practical alternative combines modeling and phenological observations using ground based sampling and satellite data. The DREAM (Dust REgional Atmospheric Model) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust (Nickovic et al. 2001). The use of satellite data products for studying phenology is well documented (White and Nemani 2006). In the current project MODIS data will provide critical input to the PREAM model providing pollen source location, timing of pollen release, and vegetation type. We are modifying the DREAM model (PREAM - Pollen REgional Atmospheric Model) to incorporate pollen transport. The linkages already exist with DREAM through PHAiRS (Public Health Applications in Remote Sensing) to the public health community. This linkage has the potential to fill this data gap so that the potential association of health effects of pollen can better be tracked for possible linkage with health outcome data which may be associated with asthma, respiratory effects, myocardial infarction, and lost workdays. Juniperus spp. pollen phenology may respond to a wide range of environmental factors such as day length, growing degree-days, precipitation patterns and soil moisture. Species differences are also important. These environmental factors vary over both time and spatial scales. Ground based networks such as the USA National Phenology Network have been established to provide national wide observations of vegetation phenology. However, the density of observers is not adequate to sufficiently document the phenology variability over large regions. Hence the use of satellite data is critical to observe Juniperus spp. pollen phenology. MODIS data was used to observe Juniperus spp. pollen phenology. The MODIS surface reflectance product(MOD09) provided information on the Juniper spp. cone formation and cone density. Ground based observational records of pollen release timing and quantities were used as verification. Techniques developed using MOD09 surface reflectance products will be directly applicable to the next generation sensors such as VIIRS.

A New Approach to Monitoring Coastal Marshes for Persistent Flooding

NASA Astrophysics Data System (ADS)

Kalcic, M. T.; Underwood, L. W.; Fletcher, R. M.

2012-12-01

Many areas in coastal Louisiana are below sea level and protected from flooding by a system of natural and man-made levees. Flooding is common when the levees are overtopped by storm surge or rising rivers. Many levees in this region are further stressed by erosion and subsidence. The floodwaters can become constricted by levees and trapped, causing prolonged inundation. Vegetative communities in coastal regions, from fresh swamp forest to saline marsh, can be negatively affected by inundation and changes in salinity. As saltwater persists, it can have a toxic effect upon marsh vegetation causing die off and conversion to open water types, destroying valuable species habitats. The length of time the water persists and the average annual salinity are important variables in modeling habitat switching (cover type change). Marsh type habitat switching affects fish, shellfish, and wildlife inhabitants, and can affect the regional ecosystem and economy. There are numerous restoration and revitalization projects underway in the coastal region, and their effects on the entire ecosystem need to be understood. For these reasons, monitoring persistent saltwater intrusion and inundation is important. For this study, persistent flooding in Louisiana coastal marshes was mapped using MODIS (Moderate Resolution Imaging Spectroradiometer) time series of a Normalized Difference Water Index (NDWI). The time series data were derived for 2000 through 2009, including flooding due to Hurricane Rita in 2005 and Hurricane Ike in 2008. Using the NDWI, duration and extent of flooding can be inferred. The Time Series Product Tool (TSPT), developed at NASA SSC, is a suite of software developed in MATLAB® that enables improved-quality time series images to be computed using advanced temporal processing techniques. This software has been used to compute time series for monitoring temporal changes in environmental phenomena, (e.g. NDVI times series from MODIS), and was modified and used to compute the NDWI indices and also the Normalized Difference Soil Index (NDSI). Coastwide Reference Monitoring System (CRMS) water levels from various hydrologic monitoring stations and aerial photography were used to optimize thresholds for MODIS-derived time series of NDWI and to validate resulting flood maps. In most of the profiles produced for post-hurricane assessment, the increase in the NDWI index (from storm surge) is accompanied by a decrease in the vegetation index (NDVI) and then a period of declining water. The NDSI index represents non-green or dead vegetation and increases after the hurricane's destruction of the marsh vegetation. Behavior of these indices over time is indicative of which areas remain flooded, which areas recover to their former levels of vegetative vigor, and which areas are stressed or in transition. Tracking these indices over time shows the recovery rate of vegetation and the relative behavior to inundation persistence. The results from this study demonstrated that identification of persistent marsh flooding, utilizing the tools developed in this study, provided an approximate 70-80 percent accuracy rate when compared to the actual days flooded at the CRMS stations.

A New Approach to Monitoring Coastal Marshes for Persistent Flooding

NASA Technical Reports Server (NTRS)

Kalcic, M. T.; Undersood, Lauren W.; Fletcher, Rose

2012-01-01

Many areas in coastal Louisiana are below sea level and protected from flooding by a system of natural and man-made levees. Flooding is common when the levees are overtopped by storm surge or rising rivers. Many levees in this region are further stressed by erosion and subsidence. The floodwaters can become constricted by levees and trapped, causing prolonged inundation. Vegetative communities in coastal regions, from fresh swamp forest to saline marsh, can be negatively affected by inundation and changes in salinity. As saltwater persists, it can have a toxic effect upon marsh vegetation causing die off and conversion to open water types, destroying valuable species habitats. The length of time the water persists and the average annual salinity are important variables in modeling habitat switching (cover type change). Marsh type habitat switching affects fish, shellfish, and wildlife inhabitants, and can affect the regional ecosystem and economy. There are numerous restoration and revitalization projects underway in the coastal region, and their effects on the entire ecosystem need to be understood. For these reasons, monitoring persistent saltwater intrusion and inundation is important. For this study, persistent flooding in Louisiana coastal marshes was mapped using MODIS (Moderate Resolution Imaging Spectroradiometer) time series of a Normalized Difference Water Index (NDWI). The time series data were derived for 2000 through 2009, including flooding due to Hurricane Rita in 2005 and Hurricane Ike in 2008. Using the NDWI, duration and extent of flooding can be inferred. The Time Series Product Tool (TSPT), developed at NASA SSC, is a suite of software developed in MATLAB(R) that enables improved-quality time series images to be computed using advanced temporal processing techniques. This software has been used to compute time series for monitoring temporal changes in environmental phenomena, (e.g. NDVI times series from MODIS), and was modified and used to compute the NDWI indices and also the Normalized Difference Soil Index (NDSI). Coastwide Reference Monitoring System (CRMS) water levels from various hydrologic monitoring stations and aerial photography were used to optimize thresholds for MODIS-derived time series of NDWI and to validate resulting flood maps. In most of the profiles produced for post-hurricane assessment, the increase in the NDWI index (from storm surge) is accompanied by a decrease in the vegetation index (NDVI) and then a period of declining water. The NDSI index represents non-green or dead vegetation and increases after the hurricane s destruction of the marsh vegetation. Behavior of these indices over time is indicative of which areas remain flooded, which areas recover to their former levels of vegetative vigor, and which areas are stressed or in transition. Tracking these indices over time shows the recovery rate of vegetation and the relative behavior to inundation persistence. The results from this study demonstrated that identification of persistent marsh flooding, utilizing the tools developed in this study, provided an approximate 70-80 percent accuracy rate when compared to the actual days flooded at the CRMS stations.