Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States

Franch, B ; Vermote, E ; Skakun, S ; Roger, J-C ; Becker-Reshef, I ; Justice, C

关键词: MODIS (RADIOMETRY);    YIELD;    FORECASTING;    WINTER;    WHEAT;    MODELS;    FARM CROPS;    NORMALIZED DIFFERENCE VEGETATION INDEX;    CALIBRATING;    DATA CORRELATION;    ERRORS;    REMOTE SENSING;    UKRAINE;    UNITED STATES;   

RP-ID  :  GSFC-E-DAA-TN65497

学科分类：地球科学(综合)

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

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.