Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data

Abstract

Crop lodging assessment is essential for evaluating yield damage and informing crop management decisions for sustainable agricultural production. While a few studies have demonstrated the potential of optical and SAR data for crop lodging assessment, large-scale crop lodging assessment has been hampered by the unavailability of dense satellite time series data. The unprecedented availability of free Sentinel-1 and Sentinel-2 data may provide a basis for operational detection and monitoring of crop lodging. In this context, this study aims to understand the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data and to detect lodging incidence in wheat using time-series analysis. Crop biophysical parameters were measured in the field for both healthy and lodged plots from March to June 2018 in a study site in Ferrara, Italy, and the corresponding Sentinel images were downloaded and processed. The lodged plots were further categorised into different lodging severity classes (moderate, severe and very severe). Temporal profiles of backscatter, coherence, reflectance and continuum removed spectra were studied for healthy and lodging severity classes throughout the stem elongation to ripening growth stages. The Kruskal Wallis and posthoc Tukey tests were used to test for significant differences between different classes. Our results for Sentinel-2 showed that red edge (740 nm) and NIR (865 nm) bands could best distinguish healthy from lodged wheat (particularly healthy and very severe). For Sentinel-1, the analysis revealed the potential of VH backscatter and the complementarity of VV and VH/VV backscatter in distinguishing a maximum number of classes. Our findings demonstrate the potential of Sentinel data for near real-time detection of the incidence and severity of lodging in wheat. To the best of our knowledge, there is no study that has contributed to this application.

Plant phenotyping relevance

Sentinel-1/2の時系列センサーデータからコムギの倒伏発生と重症度を推定する手法が研究の中心であり、圃場測定との対応や重症度クラスの識別を評価している。

Code and data availability

The supplied blocks describe field-measured crop biophysical parameters (LAI, biomass, CAI, PWC, crop height, SPAD) and Sentinel-1/2 time-series analysis for wheat lodging in Ferrara, Italy, but contain no data availability statement, deposit, or author code URL. No paper-specific public phenotype dataset, imagery, or

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