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 Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.
Highlights
Because agricultural productivity directly influences food prices, trade and greenhouse-gas emission policies, and human livelihood [1,2], accurate and timely yield estimation is of primary interest to the scientific community and governments [3]
The high R2 values obtained for both the corn and soybeans demonstrate that the crop yields estimated from MODerate Resolution Imaging Spectroradiometer (MODIS) data strongly correlate with the National Agricultural Statistics Service (NASS) survey data at the county level
This study proposes a production efficiency model-based algorithm for estimating crop yield in the Midwestern US
Summary
Because agricultural productivity directly influences food prices, trade and greenhouse-gas emission policies, and human livelihood [1,2], accurate and timely yield estimation is of primary interest to the scientific community and governments [3]. Because the regression relationship varies largely on a year-to-year basis due to inter-annual variations in climate, water availability, and management practices, the application of these models is limited to the studied regions and periods and is difficult under extreme conditions (e.g., flooding and drought) beyond historical records. Another approach applies satellite data to calibrate physiology-based crop models [19,20,21] that simulate the physical process of crop growth, where energy, water, carbon dioxide, and nutrients are converted into biomass [22,23]
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