Soil Moisture (SM) is a vital element in the hydrological cycle and land–atmosphere interactions. Quantification of SM and its spatiotemporal variability is valuable for understanding water availability in agriculture, ecosystem states, river basin hydrology, and water resources management, with different requirements of scales and spatial or temporal resolution. Thus, the precise quantification of SM and the spatial–temporal variability at different scales are always receiving considerable attention.
In recent years, with the explosion of geospatial data (from remote sensing, modelling, etc.), and the development of big data processing techniques (machine learning, etc.), the monitoring and estimation of soil moisture at multiple scales can be beneficial. Some efforts have been done to fill the scaling and resolution gaps of soil moisture, as it is always worth taking a deep look into the comprehensive usage of all available data and integrating the usage of them (Su et al., 2020).
For this Special Issue, we are interested in contributions that integrate remote sensing and geospatial big data for soil moisture estimation, through either empirical research or conceptual/theoretical works including, but not limited to:
- Remote sensing of soil moisture (satellites or UAS);
- Soil moisture data fusion and assimilation;
- Machine learning algorithms assessment;
- Construction of soil moisture database;
- Gap filling of soil moisture data;
- Novel tools for geospatial data processing (GEE et al.).
Contributions to remote sensing and geospatial big data of soil moisture are especially welcome, but contributions from other natural sciences at the forefront of soil moisture estimation are also highly welcome. Machine learning and imagery/data processing in contributions are also desired.
Proposed titles and abstracts (250 words) can be submitted by 15 November 2023 to the guest editors, at firstname.lastname@example.org, for possible feedback, if prospective authors want some feedback before preparing their manuscripts.
Prof. Dr. Salvatore Manfreda
Dr. Yijian Zeng
Dr. Ruodan Zhuang
#remotesensing #soilmoisture #Machinelearning #UAS #bigdata