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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #378441

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Detecting cover crop termination within the season using VENµS and Sentinel-2

Author
item Gao, Feng
item Anderson, Martha
item Hively, Wells - Dean

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/26/2020
Publication Date: 10/28/2020
Citation: Gao, F.N., Anderson, M.C., Hively, W.D. 2020. Detecting cover crop termination within the season using VENµS and Sentinel-2. Remote Sensing. 12(21):3524. https://doi.org/10.3390/rs12213524.
DOI: https://doi.org/10.3390/rs12213524

Interpretive Summary: Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. Cost-sharing programs encouraging associated agroecosystem services require that winter cover crops be planted and terminated within the specified time window. Usually, cover crop planting and termination dates are obtained through field surveys, which is labor-intensive. Detection of cover crop termination using remote sensing has had limited success due to the lack of high spatial and temporal resolution observations and proper methods. In this paper, we proposed a new within-season termination (WIST) algorithm to map cover crop termination dates using remote sensing. Termination dates from remote sensing data were compared to the field operation records in the Beltsville Agricultural Research Center (BARC) experimental fields in 2019 and 2020. Results show that cover crop termination dates can be reliably detected and may become routine in the future. Detecting cover crop termination within the season using remote sensing provides a quick and economical way to support agroecosystem services.

Technical Abstract: Cover crops are planted during the off-season to protect the soil for the benefit of the agroecosystem, and can reduce soil erosion, increase soil fertility, and improve watershed management. Cover crops are terminated via mowing, tilling, or herbicide prior to planting of the primary crop, often while they are still in a period of green growth. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying agroecosystem services of this conservation practice, enabling estimation of biomass accumulation during the active cover period. While remote sensing data can be used to monitor cover crop biomass and condition, detection of cover crop termination has been limited by the lack of high spatial and temporal resolution satellite observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New MicroSatellite (VENµS) imagery (5 m, 2-day revisit) and Sentinel-2 (10 m, 4-5-day). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time series and then refines the estimate to the two dates with the most significant rate of decrease in NDVI during the senescent period. The WIST algorithm estimates both the termination date and its uncertainty at a sub-field scale. The WIST algorithm was assessed using the farm operation records in the Beltsville Agricultural Research Center (BARC) experimental fields. The crop cover termination dates extracted from the VENµS and Sentinel-2 time series in 2019 and 2020 were compared to the recorded termination operation dates across different cover crop types and times. Results show that the termination dates detected from the VENµS time series (2-day revisit) agree with the recorded harvest dates with a mean absolute difference (MAD) of two days and uncertainty of four days. The operational Sentinel-2 time series (4-5-day revisit) also detected termination dates in BARC but had 7% missing and 10% false detections. The MAD from Sentinel-2 (four days) is slightly higher than VENµS (two days). The average uncertainty in termination date from Sentinel-2 (six days) is also higher than from VENµS (four days) due to less frequent temporal observations. Near-real-time simulation using the VENµS time series shows that the average lag times of termination detection are about four days for VENµS and eight days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data, conceivably within two weeks after termination accounting for data latency and algorithm lag time.