Remote Sensing Method Of Pure Crop Pixel Identification & Planting Area Estimation Based On Spectral Library

The crop identification and area estimation are an important domain in agricultural remotely sensing at all times. However, LANDS AT TM images are one of the most important remotely sensing information sources for development of agricultural information extraction methods in a long period. At present, the accuracy, efficiency and cost are still main problems facing the planting area monitoring by wideband TM satellite remote sensing in China. The pure crop pixel identification is of special significance in remotely sensing scaling transformation and inversion of crop biophysical parameters while the estimation of planting area by remotely sensing has great practical meaning for state agriculture structure adjusting at present stage. The convenient and quick pure crop pixel identification and area estimation approach may improve the acquirement ability of agriculture information. In this study spectral library based image-reference spectrum distance (IRSD) combined with spectral angle mapper(SAM), multi-temporal satellite data(MSD) were examined to address the need for pure, spatially accurate maize crop pixel identification. As reference spectra of maize are from in-situ measurement, which less were influenced by other elements such as atmosphere and man's eye in training sample area election etc, they better present the crop average eigenvector in N dimension space than the statistical parameter of training sample area by traditional classification method. Maize crop pixels where IRSD of 6 sites in experimental region were combined were identified with an overall accuracy level of more than 92%, compared to 51.3% explained by 6 site training sample method of traditional Maximum Likelihood classification alone. Further, the classification accuracy can attain more than 95% if SAM and MSD were combined into the IRSD method. This result of this study support the use of moderate resolution spectral imagery combined with TM images to image scaling and crop biophysical parameter extraction. Also, the SAM combined linear spectral mixture analysis (LSMA) was investigated to meet the demand for mapping wheat and litchi planting area in endmember level. Landsat 5 TM image, in-situ crop spectra combined band values from 2 scenes acquired on March 23, 2004 and November 2, 2000 were independently...