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Remote Sensing of Near-Surface Soil properties with the Airborne Terrestrial Applications Sensor

机译:机载地面应用传感器对近地表土壤特性的遥感

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Evaluation of near-surface soil properties via remote sensing (RS) could facilitate soil survey mapping, erosion prediction, fertilization regimes, and allocation of agrochemicals. The objective of this study was to evaluate the relationship between soil spectral signature and near surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor (ATLAS) multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil water content, soil organic carbon (SOC), particle size distribution (PSD), and citrate dithionite extractable iron (Fe_d) content. Results showed that covariance among soil properties combined with mixed signatures limited our ability to identify discrete spectral response patterns for near-surface soil attributes. Dry, sandy epipedons at the Coastal Plain site provided ideal conditions and allowed for better discrimination among soil properties. Using ATLAS thermal infrared (TIR) bands, this study provides evidence that thermal spectra are more sensitive to small changes in near -surface mineral, organic and water content.
机译:通过遥感(RS)评估近地表土壤特性可以促进土壤调查图,侵蚀预测,施肥制度和农药的分配。这项研究的目的是评估常规管理的大田作物系统中土壤光谱特征与近地表土壤特性之间的关系。使用机载陆地应用传感器(ATLAS)多光谱扫描仪,在阿拉巴马州沿海平原,阿巴拉契亚高原以及里奇和瓦利省的裸露土地上采集高分辨率RS数据。土壤的范围从沙质的Kandududults到细纹的Rhodudults。从163个采样点收集了地表土壤样品(0-1厘米),以获取土壤含水量,土壤有机碳(SOC),粒度分布(PSD)和柠檬酸连二亚铁可提取铁(Fe_d)含量。结果表明,土壤特性之间的协方差与混合特征限制了我们识别近地表土壤属性的离散光谱响应模式的能力。沿海平原站点的干燥,沙质的表皮提供了理想的条件,并可以更好地区分土壤性质。使用ATLAS热红外(TIR)波段,这项研究提供了证据,表明热光谱对近地表矿物,有机物和水含量的微小变化更为敏感。

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