Developing soil indices based on brightness,darkness, and greenness to improve land surface mapping accuracy |
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Authors: | Bingwen Qiu Ke Zhang Zhenghong Tang Chongcheng Chen Zhuangzhuang Wang |
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Affiliation: | 1. Key Laboratory of Spatial Data Mining &2. Information Sharing of Ministry of Education, National Engineering Research Centre of Geospatial Information Technology, Spatial Information Research Centre of Fujian Province, Fuzhou University, Fuzhou, Fujian, 350116 China;3. Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68558, USA |
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Abstract: | Soil, as one of the three basic biophysical components, has been understudied using remote sensing techniques compared to vegetation and impervious surface areas (ISA). This study characterized land surfaces based on the brightness–darkness–greenness model. These three dimensions, brightness, darkness, and greenness, were represented by the first Tasseled Cap Transformation (TC1), Normalize Difference Snow Index (NDSI), and Normalized Difference Vegetation Index (NDVI), respectively. The Ratio Index for Bright Soil (RIBS) was developed based on TC1 and NDSI, and the Product Index for Dark Soil (PIDS) was established by TC1 and NDVI. Their applications to the Landsat 8 Operational Land Imager images and 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in China revealed the efficiency. The two soil indices proficiently highlighted soil covers with consistently the smallest values, due to larger TC1 and smaller NDSI values in bright soil, and smaller NDVI and TC1 values in dark soil. The RIBS is capable of distinguishing bright soil from ISA without masking vegetation and water body. The spectral separability bright soil and ISA were perfect, with a Jeffries–Matusita distance of 1.916. And the PIDS was the only soil index that could discriminate dark soil from other land covers including ISA. The soil areas in China were classified using a simple threshold method based on MODIS images. An overall accuracy of 94.00% was obtained, with the kappa index of 0.8789. This study provided valuable insights into developing indices for characterizing land surfaces from different perspectives. |
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Keywords: | Soil index brightness–darkness–greenness (B–D–G) model Vegetation–impervious–soil model Tasseled Cap Transformation Separability |
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