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

REMOTE SENSING OF ENVIRONMENT

卷:243

Article

Chauhan, Sugandh¹  Darvishzadeh, Roshanak¹  Lu, Yi¹  Boschetti, Mirco²  Nelson, Andrew¹ 

[1] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands

[2] CNR, Inst Electromagnet Sensing Environm, CNR, IREA, I-20133 Milan, Italy

关键词: Sentinel-1;    Sentinel-2;    Lodging severity;    Wheat;    Sustainable agriculture;    Remote sensing;   

DOI  :  10.1016/j.rse.2020.111804

来源: Elsevier

【 摘 要 】

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 complementarily of 1/V 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.

【 授权许可】

Free