Abstract

In this study, the potential of phenological indicators derived from Sentinel-2A (S2) time series were evaluated to explore the key variables that allow identifying both cropland and crop types. Based on the derived S2 phenological metrics and fitted vegetation indices (VI), 10 feature sets were developed and assessed to discriminate different crop types via Random Forest (RF) classifier. The comparison between VI data-based classifications has shown that NDVI and EVI2 phenological sets could delineate and identify crop types more accurately compared to RENDVI data. Overall, the combined use of fitted VI and phenological features rather than being used separately achieved the best performances. Further, the result of using optimum features was the most accurate among 10 feature sets, with an overall accuracy of 88% and kappa of 0.84. This study constitutes a substantial improvement in crop type identification, which gives a valuable tool to monitor agricultural areas.

摘要

在本研究中，评估了源自 Sentinel-2A (S2) 时间序列的物候指标的潜力，以探索能够识别农田和作物类型的关键变量。基于派生的 S2 物候指标和拟合植被指数 (VI)，开发并评估了 10 个特征集，以通过随机森林 (RF) 分类器区分不同的作物类型。基于 VI 数据的分类之间的比较表明，与 RENDVI 数据相比，NDVI 和 EVI2 物候集可以更准确地描绘和识别作物类型。总体而言，结合使用拟合的 VI 和物候特征而不是单独使用获得了最佳性能。此外，使用最优特征的结果是 10 个特征集中最准确的，总体准确率为 88%，kappa 为 0.84。