Soil Moisture Observation Using Remote Sensing and Artificial Intelligence

Dear Colleagues,

Soil moisture (SM) is a key state variable that plays an important role in linking energy and carbon cycles, as well as terrestrial water in various hydrological and meteorological applications. The impact of SM in evapotranspiration, photosynthesis, runoff, soil respiration, flood events, surface heat flux partitioning, and droughts is very prominent. SM has a direct impact on soil dryness and can indirectly influence the atmospheric vapor pressure deficit. Thus, the seasonal variability of SM is a key element for land capacity to act as a carbon sink. The systematic monitoring of SM trends around the globe is vital for a better understanding of future climate change situations. Remotely sensed data offers the derivation of SM data on a global scale. Additionally, remotely sensed data for SM has advanced enormously in recent years. Artificial intelligence (AI) techniques have been integral in every field, including the processing of remote sensing (RS) data. AI achieves high performance, high accuracy, and is correlated with low statistical errors as a rapid decision tool under changing climate conditions.

This Special Issue aims to showcase studies covering different applications of different AI techniques on different types of remote sensing data from a variety of sensors for soil moisture. This Special Issue may aid in soil moisture for agriculture, the relationship between climate and soil moisture, soil moisture from different sensors with different levels of spatial and temporal resolution, and more comprehensive aims and scales. Multiscale studies and studies related to ecosystem services are welcome. Articles may address, but are not limited to, the following topic:

• Soil characteristics using remote sensing and artificial intelligence;
• Soil moisture mapping using remote sensing;
• Satellite-based soil moisture estimation, calibration, and evaluation;
• Soil moisture product downscaling;
• The gap filling of remotely sensed soil moisture products;
• Soil moisture data assimilation into hydrological models;
• The relationship between climate change and soil moisture using artificial intelligence
• Spatio-temporal trends for soil moisture using remote sensing data and artificial intelligence techniques;
• The use of remote sensing data for soil moisture in precision agriculture.

We hope you will contribute your high-quality research, and we look forward to reading your valuable results.

Dr. Yuanyuan Zha
Dr. Liujun Zhu
Dr. Hongtao Shi
Dr. Ignacio Melendez-Pastor
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.

• artificial intelligence
• model performance
• soil moisture
• sustainability
• evapotranspiration
• remote sensing
• SMAP
• data assimilation
• downscaling