Abstract
Validation is a crucial technique used to strengthen the application capabilities of earthobservation satellite data and solve the quality problems of remote-sensing products. Observing land-surface parameters in the field is one of the key steps of validation. Therefore, the demand for long-term stable validation stations has gradually increased. However, the current location-selection procedure of validation stations lacks a systematic and objective evaluation system. In this research, a data-driven selection of a land product validation station (DSS-LPV) based on Machine Learning is proposed. Firstly, we construct an evaluation indicator system in which all factors affecting the location of validation stations are divided into surface characteristics, atmospheric conditions and the social environment. Then, multi-scale evaluation grids are constructed and indicators are allocated for spatial evaluation. Finally, four Machine Learning (ML) methods are used to learn the established reliable stations, and different data-driven scoring models are constructed to explore the intrinsic relationship between evaluation indicators and station locations. In this article, the reliability of DSS-LPV is effectively validated by the example of China using the national-level land product validation station that has been established. After a comparison between the four ML models, the random forest (RF) with the highest accuracy was selected as the modeling method of DSS-LPV. The correlation between the regression value of test stations and the target value is 0.9133. The average score of test stations is 0.8304. The test stations are generally located within the calculated hot-spot area of the score density map, which means that it is highly consistent with the location of the built stations. Research results indicate that DSS-LPV is an effective method that can provide a reasonable geographical distribution of the stations. The location-selection results can provide scientific decision-making support for the construction of land product validation stations.
Highlights
Introduction iationsModern remote sensing, while providing radiance data, gradually tends to provide end users with a series of high-level standard data products [1]
We propose the data-driven location selection technique for validation station
It is consistent with the existing research results that Random Forest is more suitable for the current data set and can effectively prevent overfitting [62,63]
Summary
While providing radiance data, gradually tends to provide end users with a series of high-level standard data products [1]. With the promotion of openness, sharing, interconnection and other services [2], multi-source and multi-temporal quantitative remote-sensing products provide better data support for resources and environmental monitoring, global change and sustainable development [3]. The quality of remote-sensing products is the key to restrict their application ability [4]. The importance of accurately evaluating remote-sensing products has been generally recognized [3–8]. According to the definition of the Working Group on Calibration and Validation (WGCV). Of the International Committee on Earth Observation Satellites (CEOS), validation refers to the process of independently evaluating the accuracy and uncertainty by the comparative analysis of remote-sensing products and reference data (relative truth values) that can Licensee MDPI, Basel, Switzerland.
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