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Mapping Agricultural Frozen Soil on the Watershed Scale Using Remote Sensing Data

机译:利用遥感数据在流域尺度上绘制农业冻土图

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摘要

This paper presents an empirical model for classifying frozen/unfrozen soils in the entire Bras d’Henri River watershed (167 km2) near Quebec City (Quebec, Canada). It was developed to produce frozen soil maps under snow cover using RADARSAT-1 fine mode images and in situ data during three winters. Twelve RADARSAT-1 images were analyzed from fall 2003 to spring 2006 to discern the intra- and interannual variability of frozen soil characteristics. Regression models were developed for each soil group (parent material-drainage-soil type) and land cover to establish a threshold for frozen soil from the backscattering coefficients (HH polarization). Tilled fields showed higher backscattering signal (+3 dB) than the untilled fields. The overall classification accuracy was 87% for frozen soils and 94% for unfrozen soils. With respect to land use, that is, tilled versus untilled fields, an overall accuracy of 89% was obtained for the tilled fields and 92% for the untilled fields. Results show that this new mapping approach using RADARSAT-1 images can provide estimates of surface soil status (frozen/unfrozen) at the watershed scale in agricultural areas.
机译:本文提出了一个经验模型,用于对魁北克市(加拿大魁北克市)附近的整个Bras d'Henri河流域(167平方千米)中的冻结/未冻结土壤进行分类。它被开发来使用RADARSAT-1精细模式图像和三个冬季的现场数据制作积雪下的冻土图。分析了2003年秋季至2006年春季的12幅RADARSAT-1图像,以识别冻土特征的年内和年际变化。针对每种土壤组(母本物质-排水-土壤类型)和土地覆被开发了回归模型,以根据反向散射系数(HH极化)建立冻结土壤的阈值。耕地的反向散射信号(+ 3dB)比耕地的高。冷冻土壤的总分类准确度为87%,未冷冻土壤的总分类准确度为94%。关于土地使用,即耕地对耕地,耕地的总体准确度为89%,耕地的准确度为92%。结果表明,使用RADARSAT-1图像的这种新的制图方法可以在农业地区的分水岭尺度上提供表层土壤状况(冻结/未冻结)的估计。

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