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
Format:
application/pdf
