1. The Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130012, China 2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China
Abstract：As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.
[1] XU Bin-bin(徐彬彬). Soils(土壤), 2000, 32(6): 281. [2] Brown D J. Geoderma, 2007, 140(4): 444. [3] BAO Yi-dan, HE Yong, FANG Hui, et al(鲍一丹, 何 勇, 方 慧, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(1): 62. [4] LIU Huan-jun, ZHANG Bai, ZHANG Yuan-zhi, et al(刘焕军, 张 柏, 张渊智, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(3): 624. [5] LUO Hong-xia, GONG Jian-ya, JIAN Dai-jun(罗红霞, 龚健雅, 蹇代君). Resources and Environment in the Yangtza Basin(长江流域资源与环境), 2006, 15(1): 41. [6] Murphy R J, Wadge G. International Journal of Remote Sensing, 1994, 15: 63. [7] Kornblau M L, Cipra J E. Remote Sensing of Environment, 1983, 13(2): 103. [8] Thompson D R, Haas R H, Milford M H. Soil Science Society of America Journal, 1981, 45: 91. [9] McBratneya A B, Mendonca Santos M L, Minasny B. Geoderma, 2003, 117: 3. [10] Dobos E, Micheli E, Baumgardner M F, et al. Geoderma, 2000, 97: 367. [11] Dobos E, Montanarella L, Nègre T, et al. International Journal of Applied Earth Observation and Geoinformation, 2001, 3(1): 30. [12] Kruse F A, Lefkoff A B, Boardman J W, et al. Remote Sensing of Environment, 1993, 44: 145. [13] AN Bin, CHEN Shu-hai, YAN Wei-dong(安 斌, 陈书海, 严卫东). Chinese Journal of Stereology and Image Analysis(中国体视学与图像分析), 2005, 10(1): 5560.
