Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery  

作　　者：Changming Zhu Xin Zhang Qiaohua Huang 

机构地区：[1]School of Geography and Geomatics,Jiangsu Normal University,Xuzhou,Jiangsu 221116,China [2]State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China

出　　处：《International Journal of Agricultural and Biological Engineering》2019年第1期192-200,共9页国际农业与生物工程学报（英文）

基　　金：The study is funded by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20020101);the National R&D Program of China(Granted No.2017YFB0504201);the Natural Science Foundation of China(Grant No.61473286 and 41201460).

摘　　要：MODIS time-series imagery is promising for generating regional and global land cover products.For Brazil,however,accurate fractional cropland covers(FCC)information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem.To address these problems,this study developed an innovative approach to mapping the FCC of the Mato Grosso State,Brazil through integrating Linear Spectral Mixture Analysis(LSMA)and Seasonal Dynamic Index(SDI)models.With MOD13Q1 time-series EVI imagery,a SDI was developed to represent the phenology of croplands.Furthermore,fractional land covers(e.g.,vegetation,soil,and low albedo components)were derived with the LSMA algorithms.A stepwise regression model was established to estimate the FCC at the regional scale.Finally,ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method.Results indicated that the combination of multiple feature variables produced better results when compared with individual variables.Through cross-validation and comparative analysis,the coefficient of determination(R^(2))between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error(RMSE)of 0.13.This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping.When compared to the traditional per-pixel“hard”classification,the sub-pixel level maps illustrated detailed cropland spatial distribution patterns.

关 键 词：fractional cropland covers(FCC) MODIS enhanced vegetation index(EVI) subpixel mapping remote sensing 

分 类 号：O17[理学—数学]