Integration of Satellite-Based Optical and Synthetic Aperture Radar Imagery to Estimate Winter Cover Crop Performance in Cereal Grasses

Abstract

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce 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 robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been 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 27 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 the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 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 estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, 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.

Protecting Steppe Birds by Monitoring with Sentinel Data and Machine Learning under the Common Agricultural Policy

“…Paper [62] applies the use of time series on SAR S1 data, valid for the classification of several types of crops and determining that cereals have a different time signature than the rest of the crops. In paper [63] they demonstrated that estimation of cover crop biomass is feasible using only VIs, as well as integrating optical remote sensing and SAR. Finally, paper [64] presented a novel technique based on deep learning for crop type mapping, using NDVI time series for crop type mapping and compared with other advanced supervised learning techniques.…”

Assessing relationships of cover crop biomass and nitrogen content to multispectral imagery

“…been reported as high as 11 Mg ha −1 in some Maryland fields (Thieme et al, 2023). However, GDD, an important predictor of plant growth, will not explain biomass production in all instances (Jennewein et al, 2022).…”

“…For cover crops, red or red-edge saturation has translated into biomass levels between 1.5 and 1.9 Mg ha −1 (Jennewein et al, 2022;Prabhakara et al, 2015). These plateaus in VI are based on linear or quadratic plateau analyses of cover crop data, with Jennewein et al ( 2022) observing better success with natural log transformations.…”

Financial incentive programs and farm diversification with cover crops: assessing opportunities and challenges

“…To assess patterns of cover crop adoption on the ground, a growing number of studies use satellite data products to measure cover crop adoption at large spatio-temporal scales [29,30]. Satellite data can also be used to estimate area under 'successful' cover crop performance, measured as high levels of biomass [31][32][33], which is important because realizing the benefits from cover crops depends on their biomass production [10]. Studies have used satellite estimates of cover crop area to identify temporal trends and hotspots of adoption in the U.S. [17,29,30,34] and show that corn and soybean yields are reduced, on average, following winter cover crops [35], which is a wellknown barrier to their adoption [9].…”