A robust method for mapping soybean by phenological aligning of Sentinel-2 time series

作者：Xin Huang, Anton Vrieling, Yue Dou, Mariana Belgiu, Andrew S. Nelson · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2024 · DOI：10.1016/j.isprsjprs.2024.10.015 · 被引用次数：31 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Smart Agriculture and AI

Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as t...