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. RMSE s (from 0.25 to 010), R 2 s (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.
Highlights
And accurate cropland distribution detection and area estimation is of great significance to a wide group of end-users who can manage crop planting pattern or harmonize agricultural economic market (Potgieter, Apan, Hammer, & Dunn, 2010; Pan, Li, Zhang, Liang, Zhu, & Sulla-Menashe, 2012)
The aim of this paper is to develop a stratified temporal spectral mixture analysis (STSMA) model to determine endmember set from agricultural planting pattern for mapping cropland distribution using 16-day composited 250m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data
In both season crop (SSC) and dual season crop (DSC) region, the result of the proposed method exhibits satisfactory accuracy to SMA, and about 3% of root mean square error (RMSE) was decreased because the suitable endmember set was introduced to STSMA in order to eliminate the influence of natural vegetation (NV), like trees
Summary
And accurate cropland distribution detection and area estimation is of great significance to a wide group of end-users who can manage crop planting pattern or harmonize agricultural economic market (Potgieter, Apan, Hammer, & Dunn, 2010; Pan, Li, Zhang, Liang, Zhu, & Sulla-Menashe, 2012). This is vital variable for researcher to analyze global carbon, water and nutrient cycles (Bondeau et al, 2007). It is common that the mixture of several land covers exists within one pixel
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