Literature DB >> 33693422

Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product.

Patrick M Wurster1, Marco Maneta1,2, John S Kimball3, K Arthur Endsley3, Santiago Beguería4.   

Abstract

Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4-0.7) and matured (r: 0.6-0.9) and that the agreement was better in drier regions (r: 0.4-0.9) than in wetter regions (r: -0.8-0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research.
Copyright © 2021 Wurster, Maneta, Kimball, Endsley and Beguería.

Entities:  

Keywords:  GPP; SMAP; agriculture; crop condition; crop yield; drought; l4C

Year:  2021        PMID: 33693422      PMCID: PMC7931861          DOI: 10.3389/fdata.2020.597720

Source DB:  PubMed          Journal:  Front Big Data        ISSN: 2624-909X


  1 in total

1.  DroughtCast: A Machine Learning Forecast of the United States Drought Monitor.

Authors:  Colin Brust; John S Kimball; Marco P Maneta; Kelsey Jencso; Rolf H Reichle
Journal:  Front Big Data       Date:  2021-12-21
  1 in total

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