A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Abstract

Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

“…On the other hand, some large-scale modeling studies identified overestimations of crop productivity in comparison with statistical inventory data when applying field-derived ε * GPP values (Lobell et al, 2002;Ruimy et al, 1994;Turner et al, 2006). However, two recent studies that incorporate fine-resolution land use maps and coarse-resolution MODIS data recommend applying field-estimated LUE values for large-scale cropland modeling (Bandaru et al, 2013;Xin et al, 2013).…”

Mapping Crop Cycles in China Using MODIS-EVI Time Series

“…Cropping intensity, which we define here as the number of cropping cycles per year, is an important dimension of land use that is strongly influences water demand and agricultural production [18][19][20][21], but has received relatively little attention. In areas, such as Asia, which have limited lands available for arable expansion, crop production is commonly increased by planting crops more than once a year in the same field [22][23][24][25].…”

Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products

“…de Jong et al, 2012). Several agro-ecosystem modeling applications fall into these categories, including agro-climate forecasting (Funk and Brown, 2006), drought monitoring (Karnieli et al, 2006), and crop yield estimation (Xin et al, 2013). Although NDVI is widely used, it is sensitive to atmospheric effects, soil background, and saturates at high LAI.…”