Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
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Satellite remote sensing data with high spatial and temporal resolutions are required in the dynamic monitoring of agricultural information. However, currently it is not yet possible to embrace the advantages of both high spatial resolution and high temporal resolution in a single sensor. Combining the merits from different remote sensing data will greatly benefit agricultural applications. In this paper, a patch spectral purification (PSP) method is developed to estimate the pure reflectance of field patches by combining high and moderate spatial resolution image data. The PSP method adopts the linear mixing model to build equations from moderate spatial resolution images, estimates initial values of field patch reflectance by statistical means to provide constraints to the solution and solves the equation sets based on Bayes' theorem. We chose one farm in Xinjiang, China as the study area and took 250 m MODIS image data as an example to test the performance of the method. The sensitivity analysis and the validation indicate that the PSP method is capable of giving a good estimation of pure field patch reflectance from mixed pixels of 250 m MODIS images in the study area.