Prior knowledge-based retrieval and validation of information from remote-sensing data at various scales

Abstract

This is the preface to the special issue on the use of prior knowledge for quantitative remote sensing and validation of results from quantitative remote sensing at different spatial scales. Quantitative remote sensing is the inverse problem of retrieval of geophysical and biophysical parameters using remote-sensing data. This is usually a non-linear ill-posed problem. To overcome the ill-posed problems of retrieval, prior knowledge is normally used. Validation is a general scientific issue for the remote-sensing community. Frequent validation of remote-sensing products is necessary to ensure their quality and accuracy. This special issue includes articles on in situ measurements from a field campaign, the accuracy and precision of calibration, validation methods, and evaluation of remote-sensing quantitative retrieval information modelling. Because of the insufficient study of the validation of quantitative remote-sensing products and the lack of validation theories and practical methods, in particular, a scaling theory for heterogeneous land surface variables, further applications of remote-sensing data and products are limited.

Acknowledgement

This work was jointly supported by the MOST, China, under grant nos. 2008AA12Z109, 2010CB950802 and 2007CB714407 and by the State Key Laboratory, under grant no. O8Y01556KZ.