Remote Sensing for Soil Moisture and Vegetation Parameters Retrieval (Second Edition)

Dear Colleagues,

Soil moisture and vegetation parameters (leaf area index, biomass, etc.) are fundamental environmental variables in the global energy, carbon and water exchange, and have great relevance for crop yield estimation, drought monitoring, evapotranspiration and agricultural management. Remote sensing can provide non-destructive and cost-efficient measurements and data to understand and estimate soil moisture and vegetation parameters over local to regional spatial scales. Over the years, various remote-sensing-based methods have been developed for soil moisture and vegetation parameters estimation, especially with the development of advanced technology in GNSS-R, SAR, passive microwave, multispectral/hyperspectral and thermal imaging, and some methods with theoretical models. Therefore, the main goal of this Special Issue is to summarize the development achievements of soil moisture and vegetation parameters estimation using remote sensing, provide insight into extensive progress in agricultural regions, and promote the rapid application of relative products in different fields.

We encourage the submission of novel techniques/approaches for retrieving and estimating soil moisture and vegetation parameters at various spatial and temporal scales, using any form of remote sensing data (proximal, airborne, and satellite). Original research contributions, exhaustive reviews, remote-sensing methodologies, and relevant applications in soil moisture and vegetation parameters retrieval are welcome. In addition to the points above, topics may include but are not limited to:

• Retrieval of soil moisture and vegetation parameters (leaf area index, biomass, etc.);
• Validation of remote sensing estimates with ground observations;
• Application of new sensors/algorithms and in practice monitoring systems;
• Comparison and evaluation of different remote sensing methods (statistical, physical and hybrid models) in agriculture and drought monitoring;
• Efforts to improve the accuracy of remotely sensed products in different spatial scales.

Dr. Liangliang Tao
Prof. Dr. Dongryeol Ryu
Dr. Hao Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• soil moisture
• agricultural monitoring
• microwave remote sensing
• machine learning
• vegetation dynamics estimates
• modeling
• drought assessment

• Remote Sensing for Soil Moisture and Vegetation Parameters Retrieval in Remote Sensing (13 articles)