Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-08, Vol.12 (15), p.2385
• Main Authors: Vavlas, Nikolaos-Christos, Waine, Toby W., Meersmans, Jeroen, Burgess, Paul J., Fontanelli, Giacomo, Richter, Goetz M.
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Agricultural production; Agriculture; Automation; Backscattering; Biotechnologie; Biotechnology; Cereal crops; Cereals; Correlation analysis; Correlation matrix; Crop development; Crop production; Crop yield; Crops; Cross-polarisation; Curve fitting; Earth observation data; Farms; Growing season; Growth dynamics; Indicators; Life sciences; Mathematical analysis; Maturation; Maturation periods; Productivity; Productivity indicators; Quantitative indicators; Remote sensing; SAR; Satellites; Sciences du vivant; Seasonal variation; Seasonal variations; Second derivatives; Sentinel-1; Synthetic aperture radar; Time series; Vegetation; Wheat; Winter wheat; Winter wheat field
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12152385

High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystems.