A Stratified Temporal Spectral Mixture Analysis Model for Mapping Cropland Distribution through MODIS Time-Series Data

Jinshui Zhang, Fenghua Wei, Peijun Sun, Yaozhong Pan, Zhoumiqi Yuan, Ya Yun

Abstract


The aim of study was to develop a stratified temporal spectral mixture analysis (STSMA) for cropland area estimation using MODIS time-series data to address the mixed pixel problem caused from coarse resolution. The proposed method used thematic map from MODIS classification as prior knowledge to determine the endmember set for each sub-region input into SMA model. The results indicated the STSMA method performing better in estimating the cropland land. RMSEs (from 0.25 to 010), R2s (from 0.65 to 0.89) and bias (0.02), used as three accuracy assessment parameters, and STSMA obtained higher overall accuracy in the entire study area, at individual pixel scale to 0.10 at 10×10 pixels scale, representing higher performance compared to the conventional spectral mixture analysis (SMA) method at each pixel scale. In single-season crop, dual-season crop, natural vegetation and non-vegetation dominated landscape, the similar success from STSMA is also achieved because the suitable endmember set was set for the proposed model to ensure the accuracy of cropland estimation to address the conventional SMA colinearity problem at some degree.


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DOI: https://doi.org/10.5539/jas.v7n8p95

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Journal of Agricultural Science   ISSN 1916-9752 (Print)   ISSN 1916-9760 (Online)  E-mail: jas@ccsenet.org

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