Integration of satellite-based optical and synthetic aperture radar imagery to estimate winter cover crop performance in cereal grasses

Authors: Jennewein, Jyoti; LAMB, BRIAN - Us Geological Survey (USGS); HIVELY, W. DEAN - Us Geological Survey (USGS); THIEME, ALISON - University Of Maryland; THAPA, RESHAM - North Carolina State University; Goldsmith, Avi; Mirsky, Steven

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/20/2022
Publication Date: 2/28/2022
Citation: Jennewein, J.S., Lamb, B.T., Hively, W., Thieme, A., Thapa, R., Goldsmith, A.S., Mirsky, S.B. 2022. Integration of satellite-based optical and synthetic aperture radar imagery to estimate winter cover crop performance in cereal grasses. Remote Sensing. https://doi.org/10.3390/rs14092077.
DOI: https://doi.org/10.3390/rs14092077

Interpretive Summary: Winter cover crops provide numerous conservation benefits including decreased erosion and nutrient run off, but the magnitude of those benefits is tied to the quality and quantity of biomass produced. Remote sensing technologies, which measure reflected light, are commonly used to map plant ground cover across the landscape. However, these technologies saturate as ground cover increases, which limits reliable biomass estimations beyond ~1,500 kg ha-1. Newer remote sensing technologies include modern satellites with more spectral bands (“colors”), as well as active sensors such as radar. The ability of these newer technologies to measure cover crop biomass beyond the traditional point of saturation is unknown. We paired 573 cover crop biomass measurements across three seasons (2019-21) with imagery from multi-spectral and radar satellites. We found a modern spectral index using the “red-edge” spectral region improved prediction of cover crop biomass to ~1,900 kg ha-1. One radar metric (coherence) also modestly improved cover crop biomass estimations but did not help address saturation. This study is an important step toward increasing estimation of cover crop biomass past the previously defined saturation point. This will aid in making better estimations of cover crop performance for incentive programs and precision agriculture. To further improve our ability to estimate cover crop biomass over 2,000 kg ha-, will require use of machine learning, tractor mounted sensors, and or remote sensing physical-modeling hybridization. The ability to remotely sense cover crop biomass opens the door to tools that will allow farmers to make real-time management decisions to maximize the sustainability of their systems.

Technical Abstract: The magnitude of ecosystem services provided by winter cover crops is linked to their performance, though few studies quantify performance across the landscape. Remote sensing is capable of producing landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more resilient to saturation issues. Additionally, synthetic aperture radar (SAR) data has been effective at estimating crop biophysical characteristics, although not demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter-spring seasons (2018-2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 21 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A) combined with SAR interferometric (InSAR) coherence best estimated biomass of winter cereal grasses. However, these results were season and species-specific [R2 = 0.74, 0.81, and 0.34; MAE= 784, 553, 591 kg ha-1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively in spring (March-May)]. Compared to the optical-only model, InSAR coherence improved biomass estimated by 4% in wheat and triticale, and by 15% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1,900 kg ha-1, and thus more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work should consider the machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.