Developing and applying novel spectral feature parameters for classifying soil salt types in arid land |
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Affiliation: | 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;2. Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China;3. Department of Agricultural & Biological Engineering, University of Florida, Gainesville, FL 32611, United States;1. Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing 100048, China;2. School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL 32611, USA;3. School of Forest Resources and Conservation – Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL 32611-0565, USA;4. Pedometrics, Landscape Analysis and GIS Laboratory, Soil and Water Sciences Department, University of Florida, 2181 McCarty Hall, PO Box 110290, Gainesville, FL 32611, USA;5. Gulf Coast REC/School of Forest Resources and Conservation – Geomatics Program, University of Florida, 1200 N. Park Road, Plant City, FL 33563, USA;6. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324 Hyderabad, India;7. Soil and Water Sciences Department, 2181 McCarty Hall, PO Box 110290, University of Florida, Gainesville, FL 32611, USA |
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Abstract: | Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), in-situ field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multiple-type salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data. |
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Keywords: | Arid land Soil salinity Salt type Hyperspectral Feature parameter Classification algorithm |
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