Timely assessment of maize lodging severity with limited samples using multi-temporal Sentinel-1 and Sentinel-2 data across large spatial extents

Abstract

Typhoons are a primary cause of maize lodging, significantly reducing crop yield and resilience. Rapid and accurate assessment of lodging severity is essential for processing agricultural decision and implementing effective cropland management strategies. However, the crop lodging monitoring methods based on remote sensing require a large number of ground survey samples, which are time-consuming and labor-intensive, limiting their applicability for large-scale assessments during typhoon events. To address this challenge, this study proposed a Two-Step Augmentation Strategy (TSAS) that integrates Sentinel-2 and Sentinel-1 satellite data with limited field samples to automatically generate representative samples for different lodging severity. The TSAS framework includes two steps: first step, we use eXtreme Gradient Boosting (XGBoost) to classify random points in overlapping regions (RegionOₗ, referring to areas covered by both Sentinel-1 and Sentinel-2 images) based on lodging-sensitive optical and radar features identified through correlation analysis and J-M distance, generating Sample Set L. Second step, we retrain the XGBoost model with radar features from Sample Set L to classify random points in non-overlapping regions (RegionNₒₗ, referring to areas covered only by Sentinel-1 images and not by Sentinel-2 images). Finally, a purification strategy is applied to remove erroneous samples and improve accuracy. This approach was tested in Jilin Province, significantly affected by a severe typhoon in 2020. Results show that TSAS effectively generates reliable lodging samples, achieving high spectral correlation similarity (mean of SCS > 0.72) and low Euclidean distance (mean of ED < 0.75) compared to field survey samples. These samples were applied to three classifiers, with Support Vector Machine (SVM) achieving the highest accuracy (OA = 82.36 %, F1 = 0.81) compared to Random Forest (RF) and Minimum Distance (MD) classification methods. The results of this study show that TSAS is able to efficiently and accurately generate high-accuracy maize lodging samples on a regional scale with limited field surveys, significantly improves the efficiency of large-scale agricultural disaster assessment and provides valuable support for disaster management and agricultural planning.

Plant phenotyping relevance

Sentinel-1/2データとXGBoostを用いて、トウモロコシの倒伏重症度サンプルを限られた現地調査から自動生成するTSAS手法が研究の中心であり、植物状態の推定と精度評価を実施している。

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