Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms.

ABSTRACT: Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs

SUBMITTER: Levitan N 

PROVIDER: S-EPMC6349260 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature