Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product
| Autor: | Patrick M. Wurster, Marco Maneta, John S. Kimball, K. Arthur Endsley, Santiago Beguería |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Big Data
010504 meteorology & atmospheric sciences 0207 environmental engineering crop condition 02 engineering and technology drought 01 natural sciences Crop Condition index Artificial Intelligence Yield (wine) Computer Science (miscellaneous) 020701 environmental engineering Water content 0105 earth and related environmental sciences Original Research agriculture lcsh:T58.5-58.64 business.industry lcsh:Information technology Crop yield fungi Primary production food and beverages Vegetation SMAP crop yield Agronomy Agriculture l4C Environmental science GPP business Information Systems |
| Zdroj: | Frontiers in Big Data Frontiers in Big Data, Vol 3 (2021) |
| ISSN: | 2624-909X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|