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Identification of Winter Land Use in Temperate Agricultural Landscapes based on Sentinel-1 and 2 Times-Series

机译:基于Sentinel-1和2 Times-Series的温带农业景观冬季土地利用识别

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Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km2. For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.
机译:土地覆盖和土地利用监测,特别是在冬季,仍然是农业领域的主要环境和科学问题。从环境的角度来看,冬季植被的存在和类型会影响污染物向水体的迁移。从方法学的角度来看,由于耕作策略和实践的多样性,表征田间规模的冬季土地覆盖和土地利用的时空动态仍然是一个挑战。这项研究的目的是评估光学和SAR时间序列对改善130 km区域冬季土地利用监测的潜力 2 。为此,使用SVM和RF算法对Sentinel-1和2时间序列进行了分类。根据Sentinel-1和2张图像的组合,冬季土地利用的总体准确度为81%,kappa指数为0.77。

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