Abstract Crop type information such as its location and spatial distribution is relevant for agricultural planning and decision making about food sustainability and security. This information can be obtained through the analysis of images obtained through optical satellite remote sensing. Activities such as accurate discrimination of crops require dense time-series of satellite data which can capture the diverse crop phenology. However, given the presence of clouds at important periods of crops’ development, the required time-series is impossible to obtain from just one optical satellite sensor. The Harmonized Landsat and Sentinel-2 (HLS) project by NASA provides fused data from both Operational Land Imager and Multispectral Instrument optical sensors of Landsat and Sentinel systems respectively. The present study used a multi-temporal HLS data and a target-oriented cross-validation (TOV) modelling approach with random forest algorithm to discriminate 13 crop types. 15 phenological metrics derived from time-series HLS data, together with 48 spectral and 2 topographic information were used as predictors in the model. A forward feature selection (FFS) procedure of the TOV was used to improve the classification model. 16 predictors comprising of spectral, phenological and topographic information were selected as useful for the crop discrimination. An independent accuracy assessment of the final model based on the selected predictors by the FFS procedure resulted in an overall accuracy of 76%. While most of the crop classes, achieved higher producer’s and user’s accuracies (>80%), the discrimination accuracies of potato, summer oat and winter triticale were comparatively low (

中文翻译：

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基于多时相多传感器卫星数据的异质低山地区农作物判​​别

摘要 作物类型信息，例如其位置和空间分布，与有关粮食可持续性和安全的农业规划和决策相关。这些信息可以通过对光学卫星遥感获得的图像进行分析来获得。诸如准确区分作物之类的活动需要密集的时间序列卫星数据，这些数据可以捕获不同的作物物候。然而，鉴于在作物生长的重要时期存在云，仅从一个光学卫星传感器就无法获得所需的时间序列。NASA 的协调陆地卫星和哨兵 2 (HLS) 项目分别提供来自陆地卫星和哨兵系统的操作陆地成像仪和多光谱仪器光学传感器的融合数据。本研究使用多时相 HLS 数据和面向目标的交叉验证 (TOV) 建模方法和随机森林算法来区分 13 种作物类型。来自时间序列 HLS 数据的 15 个物候指标以及 48 个光谱和 2 个地形信息被用作模型中的预测因子。TOV 的前向特征选择 (FFS) 程序用于改进分类模型。包含光谱、物候和地形信息的 16 个预测因子被选为对作物区分有用。基于 FFS 程序选择的预测变量对最终模型进行的独立准确度评估得出 76% 的整体准确度。虽然大多数作物类别，实现了更高的生产者和用户的准确度 (>80%)，但马铃薯的识别准确度，