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Landsat-8: Science and product vision for terrestrial global change research
- Remote Sens. Environ. 2014
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Article A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US
, 2013
"... Abstract: Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to ..."
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Abstract: Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated
Environmental Livelihood Security in Southeast Asia and Oceania A Water-Energy-Food-Livelihoods Nexus Approach for Spatially Assessing Change
"... IWMI is a member of the CGIAR Consortium and leads the: RESEARCH ..."
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
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Using MODIS-NDVI and Climatic Variables for Drought Assessment and Monitoring in Pakistan and Adjoining South Asian Countries
"... The normalized difference vegetation index (NDVI) has proven to be usually engaged to assess terrestrial vegetation conditions. Spatial and temporal rainfall distribution and its effect on NDVI can be useful for drought monitoring. To better understand this relationship, time series NDVI during (Jan ..."
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The normalized difference vegetation index (NDVI) has proven to be usually engaged to assess terrestrial vegetation conditions. Spatial and temporal rainfall distribution and its effect on NDVI can be useful for drought monitoring. To better understand this relationship, time series NDVI during (Jan- Dec) 2014 for every three month interval in Pakistan and adjoining South Asian countries were analyzed. We also obtained and analyzed time series of different variable i.e. rainfall, soil moisture, evapotranspiration and soil temperature data with the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Goddard Earth Sciences Data and Information Services Center (GES DISC) online data analysis system Giovanni to assess NDVI and other climatic variables from Jan- Dec, during the year 2014. NDVI time series maps and data are based on the maximum value compositing monthly product by NASA GES DISC. Rainfall anomaly data is obtained from the NOAA Climate Prediction Center from International Research institute (IRI) for climate and society’s platform. We found that NDVI values varies and depend on land cover types and its spatial location. We also found a strong positive relationship among NDVI, rainfall and soil moisture. Seasonal variation of rainfall also affects on evapotranspiration, soil temperature, and soil moisture. 1.
Article Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale
, 2014
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