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Remote Sensing
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Advances in Remotely Sensed Soil Moisture Products
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Advances in Remotely Sensed Soil Moisture Products

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 8043

Special Issue Editor

Dr. Peyman Abbaszadeh

E-Mail Website
Guest Editor
Center for Complex Hydrosystems Research (CCHR), Department of Civil, Construction and Environmental Engineering, University of Alabama (UA), Tuscaloosa, AL 35487, USA
Interests: hydrologic modeling; data assimilation; soil moisture remote sensing and machine learning

Special Issue Information

Dear Colleagues,

Soil moisture has an important role in the global water and energy balance, affecting hydrological and atmospheric cycles, drought conditions, irrigation management, and so many other processes. Over the last decade, the development of remote sensing technologies has provided the possibility that this environmental variable is more accessible than before. Nowadays, remotely sensed satellite products have become the only feasible way to reach an unprecedented amount of soil moisture data on both spatial and temporal scales. This Special Issue aims to publish new ideas and findings in remotely sensed soil moisture products, the validation of different satellite soil moisture datasets (e.g., ASCAT/MetOp, SMAP, SMOPS, SMOS, CCI, AMSR-2/GCOM-W, MWRI/FY-3), and their use for scientific research or operational applications. Potential topics include but are not limited to the following:

  • Validation and evaluation of remotely sensed soil moisture products
  • Applications of remotely sensed soil moisture data including hydrologic and land surface modeling, data assimilation, Deep learning and Machine Learning, agricultural drought monitoring, flood forecasting, and irrigation management
  • Downscaling and fusion of remotely sensed soil moisture data

Dr. Peyman Abbaszadeh
Guest Editor

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.

Keywords

  • Soil Moisture
  • Validation
  • Downscaling
  • Hydrologic modeling
  • Data Assimilation
  • Deep Learning

Published Papers (2 papers)

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Research

17 pages, 5636 KiB  
Article
A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images
by Ehab H. Hegazi, Lingbo Yang and Jingfeng Huang
Remote Sens. 2021, 13(24), 4964; https://doi.org/10.3390/rs13244964 - 7 Dec 2021
Cited by 18 | Viewed by 5227
Abstract
Achieving the rational, optimal, and sustainable use of resources (water and soil) is vital to drink and feed 9.725 billion by 2050. Agriculture is the first source of food production, the biggest consumer of freshwater, and the natural filter of air purification. Hence, [...] Read more.
Achieving the rational, optimal, and sustainable use of resources (water and soil) is vital to drink and feed 9.725 billion by 2050. Agriculture is the first source of food production, the biggest consumer of freshwater, and the natural filter of air purification. Hence, smart agriculture is a “ray of hope” in regard to food, water, and environmental security. Satellites and artificial intelligence have the potential to help agriculture flourish. This research is an essential step towards achieving smart agriculture. Prediction of soil moisture is important for determining when to irrigate and how much water to apply, to avoid problems associated with over- and under-watering. This also contributes to an increase in the number of areas being cultivated and, hence, agricultural productivity and air purification. Soil moisture measurement techniques, in situ, are point measurements, tedious, time-consuming, expensive, and labor-intensive. Therefore, we aim to provide a new approach to detect moisture content in soil without actually being in contact with it. In this paper, we propose a convolutional neural network (CNN) architecture that can predict soil moisture content over agricultural areas from Sentinel-1 images. The dual-pol (VV–VH) Sentinel-1 SAR data have being utilized (V = vertical, H = horizontal). The CNN model is composed of six convolutional layers, one max-pooling layer, one flatten layer, and one fully connected layer. The total number of Sentinel-1 images used for running CNN is 17,325 images. The best values of the performance metrics (coefficient of determination (R2=0.8664), mean absolute error (MAE=0.0144), and root mean square error (RMSE=0.0274)) have been achieved due to the use of Sigma naught VH and Sigma naught VV as input data to the CNN architecture (C). Results show that VV polarization is better than VH polarization for soil moisture retrieval, and that Sigma naught, Gamma naught, and Beta naught have the same influence on soil moisture estimation. Full article
(This article belongs to the Special Issue Advances in Remotely Sensed Soil Moisture Products)
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22 pages, 7270 KiB  
Article
Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A
by Sumin Ryu, Young-Joo Kwon, Goo Kim and Sungwook Hong
Remote Sens. 2021, 13(15), 2990; https://doi.org/10.3390/rs13152990 - 29 Jul 2021
Cited by 7 | Viewed by 2100
Abstract
The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM [...] Read more.
The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) >0.75, bias <0.1%, and root mean square error (RMSE) <4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were <0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm. Full article
(This article belongs to the Special Issue Advances in Remotely Sensed Soil Moisture Products)
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