Location: Hydrology and Remote Sensing LaboratoryTitle: Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities
|ZHANG, X. - South Dakota State University|
Submitted to: Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/23/2021
Publication Date: 3/24/2021
Citation: Gao, F.N., Zhang, X. 2021. Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities. Journal of Remote Sensing. 2021:14. https://doi.org/10.34133/2021/8379391.
Interpretive Summary: Crop phenology is critical for agricultural management and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, but making ground observations is time-consuming and provides little information about variability in space. Remote sensing has been used to map land surface phenology and relate to crop growth stages. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper reviews different remote sensing phenology approaches and focuses on near-real-time mapping. Recent remote sensing approaches have shown the capability to map crop emergence and cover crop termination in 1-3 weeks of these events, which is useful information for crop management and agroecosystem monitoring.
Technical Abstract: Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) timeseries have been used to map Land Surface Phenology (LSP) and relate to crop growth stages generally after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes different remote sensing phenology approaches and focuses on two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time-series shapes and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches detected crop emergence in 2-4 weeks in the Midwestern United States and are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time-series and confirm the trends using the changing momentum and VI thresholds. Crop emergence and cover crop termination were detected in 1-3 weeks after emergence or termination in Maryland. Results from the curve-based and the trendbased approaches are promising. Nevertheless, mapping crop phenology in near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology mapping depends on the frequency and availability of cloudfree observations within the growing season.