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  • 2015-2019  (7)
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  • 1
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    Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine (2019)
    Elsevier
    Details
    Publication Date: 2019-04-01
    Print ISSN: 0303-2434
    Topics: Geography , Geosciences
    Published by Elsevier
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    PAPER CURRENT
  • 2
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    Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting (2018)
    In:  CASI
    Details
    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; Jul 22, 2018 - Jul 27, 2018; Valencia; Spain
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 3
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    Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine (2018)
    In:  Other Sources
    Details
    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
    Format: text
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  • 4
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    Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States (2018)
    In:  CASI
    Details
    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); Jul 22, 2018 - Jul 27, 2018; Valencia; Spain|IGARSS 2018 - IEEE International Geoscience and Remote Sensing Symposium; 8177-8180
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  • 5
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    Automatic Co-Registration of Multi-Temporal Landsat-8/OLI and Sentinel-2A/MSI Images (2017)
    In:  CASI
    Details
    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); Jul 23, 2017 - Jul 28, 2017; Fort Worth, TX; United States
    Format: application/pdf
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  • 6
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    Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine (2018)
    In:  CASI
    Details
    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
    Format: application/pdf
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  • 7
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    Incorporating Yearly Derived Winter Wheat Maps Into Winter Wheat Yield Forecasting Model (2016)
    In:  CASI
    Details
    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; Jul 10, 2016 - Jul 15, 2016; Beijing; China
    Format: application/pdf
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