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


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 OPEN ACCESS 5927 Midwestern US with low errors for both corn (R 2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R 2 = 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.

DOI: 10.3390/rs5115926

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Available online: https://lpdaac.usgs.gov

  • 2013

Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data

  • P Gong, J Wang, +6 authors S Liu
  • 2013

Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics

  • D K Bolton, M A Friedl
  • 2013

National Agricultural Statistics Service (NASS) Quick Stats Database Available online

  • 2013

USGS Earth Resources Observation and Science (EROS) Center. Available

  • 2013
Showing 1-9 of 9 extracted citations