Monitoring winter wheat crop traits change during crop development using Sentinel-1 backscatters

Abstract

It is important for farmers to monitor the development changes in winter wheat (Triticum aestivum L.) crop structure to assess plant response to crop management and environmental changes. Even in cloudy conditions, radar satellite imagery has the potential to provide near real-time and reliable information about crop development that surpasses optical satellite imagery. In this study, Sentinel-1A/1B backscattering parameters for co-polarization (VV), cross-polarization (VH), differences of dual polarizations (VH-VV), and combination of dual polarizations (VH+VV) were evaluated to estimate changes in shoot density (SD), green area index (GAI), aboveground dry biomass (AGDB), plant height (PH), and leaf nitrogen content (LNC) throughout the winter wheat growing season. In order to eliminate noise caused by inverse scattering, local weighted scatterplot smoothing (LOWESS) was applied to backscatter parameters during post-processing. The correlation of backscatter parameters to the in-situ winter wheat plant traits in the fields were assessed in the Republic of Ireland and the United Kingdom over two crop growing cycles. The Sentinel-1 backscatter parameters were correlated to SD, GAI, AGDB, PH, and LNC using Support Vector Regression (SVR), Random Forest Regression (RFR), and K-Nearest Neighbors Regression (KNNR). The results presented good prediction of GAI, AGDB, PH, and LNC were possible with R2 between 0.72 to 0.95 when modelled using datasets from specific growth stages indicative of morphological changes in plant and R2 between 0.69 to 0.94 when modelled using datasets from full growth stages. It was concluded that it would be possible to develop continuous monitoring of winter wheat throughout the growing season that would not be influenced by cloud cover. Subsequent work will examine the best way to use these data for optimum crop husbandry.

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ORCID for Bing-Bing Goh: orcid.org/0000-0001-7108-991X

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