ABSTRACT

The characterization of intra- and interannual variations in optical satellite observations has proven to be effective to differentiate between land use classes. However, there is relatively little knowledge of the accuracy and usefulness of vario us satellite-based phenology variables and metrics. This letter provides quick insights into the processing of vegetation seasonality and phenology data, applied to the European Space Agency (ESA) Sentinel-2 satellite observations. The following time-series data methods were compared for their potential to enhance land use mapping in two study areas in Kenya and Tanzania: a) monthly maximum Normalized Difference Vegetation Index (NDVI) composites, b) NDVI-based time-series statistics, c) NDVI-based harmonic regressions, and d) threshold-based NDVI-based phenological metrics. When using the maximum NDVI composites, as classification inputs, the overall accuracies were found to be 7–18% higher than when using the other methods (predictors). Overall, the Muranga site (Kenya) showed a higher classification accuracy for the best performing predictor set (overall accuracy = 86.5%) than the Kilimanjaro site (Tanzania) (overall accuracy = 80.9%). These findings confirm the potential of phenology-based compositing methods for large-scale mapping of agro-ecological farming systems.

摘要

光学卫星观测的年内和年际变化特征已被证明可有效区分土地利用类别。然而，对于各种基于卫星的物候变量和指标的准确性和实用性知之甚少。这封信提供了对应用于欧洲航天局 (ESA) Sentinel-2 卫星观测的植被季节性和物候数据处理的快速见解。比较了以下时间序列数据方法在增强肯尼亚和坦桑尼亚两个研究区土地利用制图方面的潜力：a) 月最大归一化差异植被指数 (NDVI) 复合材料，b) 基于 NDVI 的时间序列统计数据，c ) 基于 NDVI 的谐波回归，和 d) 基于阈值的基于 NDVI 的物候指标。当使用最大 NDVI 组合作为分类输入时，发现总体准确度比使用其他方法（预测变量）时高 7-18%。总体而言，Muranga 站点（肯尼亚）表现出比乞力马扎罗站点（坦桑尼亚）更高的分类准确度（总体准确度 = 86.5%）。这些发现证实了基于物候学的合成方法在农业生态农业系统大规模绘图中的潜力。