A production efficiency model-based method for satellite estimates of corn and soybean yields in the midwestern US

Qinchuan Xin, Peng Gong, Chaoqing Yu, Le Yu, Mark Broich, Andrew E. Suyker, Ranga B. Myneni

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)5926-5943
Number of pages18
JournalRemote Sensing
Volume5
Issue number11
DOIs
StatePublished - Nov 2013

Keywords

  • Crop yield
  • Modis gpp
  • Radiation use efficiency
  • Remote sensing
  • Spatial heterogeneity

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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