Pedosphere 22(3): 394--403, 2012
ISSN 1002-0160/CN 32-1315/P
©2012 Soil Science Society of China
Published by Elsevier B.V. and Science Press
Mapping soil texture of a plain area using fuzzy-c-means clustering method based on land surface diurnal temperature difference
WANG De-Cai1,2, ZHANG Gan-Lin1,2, PAN Xian-Zhang1, ZHAO Yu-Guo1, ZHAO Ming-Song1,2 and WANG Gai-Fen1,2
1 State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 (China)
2 Graduate University of Chinese Academy of Sciences, Beijing 100049 (China)
ABSTRACT
      The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions. A new method was implemented to map regional soil texture (in terms of sand, silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means (FCM) clustering. Six classes were generated, and for each class, the sand (> 0.05 mm), silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.
Key Words:  digital soil mapping, land surface temperature, low relief area, MODIS, remote sensing
Citation: Wang, D. C., Zhang, G. L., Pan, X. Z., Zhao, Y. G., Zhao, M. S. and Wang, G. F. 2012. Mapping soil texture of a plain area using fuzzy-c-means clustering method based on land surface diurnal temperature difference. Pedosphere. 22(3): 394-403.
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