High-Resolution Remote Sensing Datasets for Land Surface Analysis: Calibration and Validation

Dear Colleagues,

High-resolution remote sensing datasets have become an indispensable data source for land surface analysis with the rapid development of various remote sensing platforms. Land surface information or land cover is generally extracted via classification or image analysis from remote sensing datasets. Particularly, a variety of satellite sensors have provided long time series and large-scale datasets for large scale and multitemporal representation of the land surface. However, the data quality of the remote sensing datasets may significantly affect the trustworthiness and reliability of the derived land cover information. The quality of the datasets involves several dimensions, such as positional accuracy, radiometric accuracy, thematic accuracy, segmentation/classification accuracy and analysis accuracy. Geometric or radiometric calibration are necessary operations to improve the accuracy of the remote sensing datasets. Furthermore, the data quality measures, spatial sampling methods, etc., provide significant data quality reference for the efficacy of the dataset’s applications. Therefore, the calibration and validation of high-resolution remote sensing datasets plays an important and fundamental role in remote sensing.

This Special Issue aims for studies covering calibration and validation of different high-resolution remote sensing datasets acquired by different sensors and platforms, such as multi-spectral and hyperspectral datasets, active and passive microwave dataset, Lidar and laser scanning datasets, spaceborne/airborne and terrestrial platforms. Topics may cover anything from the geometric or radiometric calibration technology for different remoting sensors, to quality assessment of various remote sensing datasets, derived products or analysis results. Hence, articles may address, but are not limited, to the following topics:

• Remote sensing datasets quality assessment;
• Accuracy assessment of land cover map;
• Spatial sampling;
• Multisource data integration;
• Long time series analysis;
• Geometric calibration;
• Radiometric calibration.

Prof. Dr. Xiaohua Tong
Prof. Dr. Huan Xie
Dr. Zhenhua Wang
Dr. Yanmin Jin
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• high-resolution remote sensing datasets
• land surface analysis
• land cover map
• data quality
• accuracy assessment
• sampling
• validation
• calibration