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  • 1
    Publication Date: 2004-12-03
    Description: Global study of land surface properties uses AVHRR channels 1 and 2, but channel 3 may be of interest, although its use requires preprocessing. It consists of both a reflective part and an emissive part, the former can be derived from T3, T4 and T5. Since the water vapor affects channel 3, its content is retrieved from the channel 4 and 5 using the split window technique. A formula of reflective part retrieval at 3.75 micrometers is tested in the case of sunglint observations where the emissivities of channels 4 and 5 can be set to the unity. The formula is adapted and validated to land surface using the FIFE-87 data set. Preliminary applications of the reflectance at 3.75 micrometers to the studies of surface properties retrieval, aerosol retrieval over land, and desertic aerosol retrieval, are addressed.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: CNES, Proceedings of 6th International Symposium on Physical Measurements and Signatures in Remote Sensing; p 817-824
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  • 2
    Publication Date: 2019-07-13
    Description: Many applications in climate change and environmental and agricultural monitoring rely heavily on the exploitation of multi-temporal satellite imagery. Combined use of freely available Landsat-8 and Sentinel-2 images can offer high temporal frequency of about 1 image every 3-5 days globally.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN52147 , IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017); 23-28 Jul. 2017; Fort Worth, TX; United States
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  • 3
    Publication Date: 2019-07-13
    Description: Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN40729 , IGARSS 2016; 10-15 Jul. 2016; Beijing; China
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  • 4
    Publication Date: 2019-07-13
    Description: Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100 percent of wheat within the pixel) from MODIS (Moderate-resolution Imaging Spectroradiometer) data at 1-kilometer resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN65497 , IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018); 22-27 Jul. 2018; Valencia; Spain|IGARSS 2018 - IEEE International Geoscience and Remote Sensing Symposium; 8177-8180
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  • 5
    Publication Date: 2019-07-13
    Description: Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100 percent of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (tonnes per hectare) (15-18 percent). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7 percent) while for Ukraine it is 0.27 t/ha (8.4 percent).
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN65500 , International Journal of Applied Earth Observation Geoinformation (ISSN 0303-2434); 76; 112-127
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  • 6
    Publication Date: 2019-07-13
    Description: This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS and LANDSAT data and its validation using the AERONET data.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN58184 , International Geoscience and Remote Sensing Symposium (IGARSS 2018); 22-27 Jul. 2018; Valencia; Spain
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  • 7
    Publication Date: 2019-07-13
    Description: This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS and LANDSAT data and its validation using the AERONET data.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN40732 , IGARSS 2016; 10-15 Jul. 2016; Beijing; China
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  • 8
    Publication Date: 2019-07-20
    Description: In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2〉0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN66729 , IGARSS 2018; 22-27 Jul. 2018; Valencia; Spain
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  • 9
    Publication Date: 2018-12-14
    Type: http://purl.org/escidoc/metadata/ves/publication-types/article
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  • 10
    Publication Date: 2019-12-14
    Description: Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (1518%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN63689 , International Journal of Applied Earth Observation and Geoinformation (ISSN 0303-2434); 76; 112-127
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