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A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database

机译：基于光谱聚类卢卡斯土壤数据库的土壤有机碳预测遥感方法

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Kathrin J. Ward; Sabine Chabrillat; Carsten Neumann; Saskia Foerster;
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1. A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database [J] . Geoderma: An International Journal of Soil Science . 2019,第期

机译：基于光谱簇绒土壤数据库的土壤有机碳预测遥感适应性方法
2. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study [J] . Gomez Cecile, Rossel Raphael A. Viscarra, McBratney Alex B. Geoderma: An International Journal of Soil Science . 2008,第3a4期

机译：利用高光谱遥感和近场近红外光谱技术预测土壤有机碳：澳大利亚案例研究
3. Soil organic carbon prediction using remotely sensed data at Lighvan watershed, Northwest of Iran [J] . Mehdi Rahmati, Mohammad Reza Neyshabouri, Majid Mohammady Oskouei, Azarian Journal of Agriculture . 2016,第2期

机译：利用遥感数据预测伊朗西北部利格文河流域的土壤有机碳
4. Using hyperspectral remote sensing data for the assessment of topsoil organic carbon from agricultural soils [C] . Bastian Siegmann, Thomas Jarmer, Thomas Selige, Remote sensing for agriculture, ecosystems, and hydrology XIV . 2012

机译：利用高光谱遥感数据评估农业土壤表层土壤有机碳
5. Soil organic carbon simulation in cropland: A combined remote sensing, GIS, and modeling approach. [D] . Niu, Xianzeng. 2004

机译：农田土壤有机碳模拟：遥感，GIS和建模方法的组合。
6. Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries [O] . Hongyang Li, Shengyao Jia, Zichun Le 2020

机译：近红外光谱预测新靶区域中的土壤有机碳：尖峰对不同规模土光谱文库的影响
7. High-Spectral Resolution Remote Sensing of Soil Organic Carbon Dynamics [O] . Bas van Wesemael, Sabine Chabrillat, Florian Wilken 2021

机译：土壤有机碳动力学的高光谱分辨率遥感
8. Aerial Photography and Multispectral Remote Sensing for Engineering Soils Mapping. June 1969 [R] . Tanguay, M. G. 1969

机译：工程土壤制图的航空摄影与多光谱遥感。 1969年6月

A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database

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