ENHANCING REMOTE SENSING BASED YIELD FORECASTING: APPLICATION TO WINTER WHEAT IN UNITED STATES

被引：0
|
作者：
Franch, B. [1 ,2 ]
Vermote, E. [2 ]
Skakun, S. [1 ,2 ]
Roger, J. -C. [1 ,2 ]
Becker-Reshef, I. [1 ]
Justice, C. [1 ]
机构：
[1] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[2] NASA, Goddard Space Flight Ctr, Code 619,8800 Greenbelt Rd, Greenbelt, MD 20771 USA
来源：
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年
关键词：
Agriculture; wheat; yield; MODIS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
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% of wheat within the pixel) from MODIS data at 1km 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.
引用
收藏
页码：8177 / 8180
页数：4
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