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Agronomy
Special Issues
Monitoring Soil Moisture Content through Earth Observation
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Monitoring Soil Moisture Content through Earth Observation

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 4616

Special Issue Editor

Dr. Dimitrios D. Alexakis

E-Mail Website
Guest Editor
Institute for Mediterranean Studies, Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: remote sensing; GIS; geomorphology; landscape ecology; landscape archaeology; soil erosion; land cover/land use change; natural hazards monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

The assessment of soil moisture content (SMC) is indispensable for various disciplines, such as meteorology, hydrology and agriculture, finding applications in evapotranspiration estimation, flood-risk prediction and the assessment of irrigation requirements. The most accurate approach for SMC estimation is that of the gravimetric method; however, large-scale SMC ground measurements are time- and labour - intensive. Remote sensing provides a fast alternative to mapping SMC and its temporal distribution. The advent of satellite-based remote sensing has led to a considerable amount of scientific literature on identifying the potential of such sensors to provide explicit SMC maps from space.

The main aim of this Special Issue is to create a dialogue between remote sensing experts regarding the use, perspectives, and current limitations of EO and the associated geospatial science and technology in monitoring and modelling SMC both at the local and regional scale. In addition, this Special Issue will cover topics related to soil loss and erosion as a result of climate change, land degradation, current and future land use, and agricultural practices, as well as the associated educational aspects. Authors are encouraged to submit articles concerning, but not limited to, the following subjects related to the monitoring of SMC:

  • Remote sensing (both optical and SAR);
  • UAVs;
  • LiDAR;
  • Climate change;
  • Land use;
  • Geomorphology;
  • Hydrology;
  • Landscape ecology;
  • Land degradation;
  • Conservation practices.

Dr. Dimitrios Alexakis
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. Agronomy is an international peer-reviewed open access monthly 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 2600 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 content
  • earth observation
  • SAR
  • optical sensors

Published Papers (1 paper)

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Research

18 pages, 5109 KiB  
Article
Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN)
by Ehab H. Hegazi, Abdellateif A. Samak, Lingbo Yang, Ran Huang and Jingfeng Huang
Agronomy 2023, 13(3), 656; https://doi.org/10.3390/agronomy13030656 - 24 Feb 2023
Cited by 5 | Viewed by 4018
Abstract
Agriculture is closely associated with food and water. Agriculture is the first source of food but the biggest consumer of freshwater. The population is constantly increasing. Smart agriculture is one of the means of achieving food and water security. Smart agriculture can help [...] Read more.
Agriculture is closely associated with food and water. Agriculture is the first source of food but the biggest consumer of freshwater. The population is constantly increasing. Smart agriculture is one of the means of achieving food and water security. Smart agriculture can help improve water management and increase agricultural production, thus counteracting rapid population growth requirements. Soil moisture estimation is a critical step in agricultural water management. Soil moisture measurement techniques in situ are point measurements, labor-intensive, time-consuming, tedious, and expensive. We propose, in this research, a new approach to predict soil moisture over vegetation-covered areas from Sentinel-2 images based on a convolutional neural network (CNN). CNN architecture (3) consisting of six convolutional layers, one pooling layer, and two fully connected layers has achieved the highest prediction accuracy. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the proposed algorithm. The Red Edge 3, NIR, and SWIR 1 are the most appropriate Sentinel-2 bands for retrieving soil moisture in vegetation-covered areas. Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) are the best indicators. The use of the indicator is more proper than the use of the single Sentinel-2 band as input data for the proposed CNN architecture for predicting soil moisture. However, using combinations “that consist of some number of Sentinel-2 bands” as input data for CNN architecture is better than using each indicator separately or all of them as a group. The best values of the performance metrics were achieved using the sixth combination (R2=0.7094, MAE=0.0277, RMSE=0.0418) composed of the Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, and Red Edge 4 bands as input data to the CNN architecture (3), as well as by using the fifth combination (R2=0.7015, MAE=0.0287, RMSE=0.0424) composed of the Red Edge 3, NIR, Red Edge 4, and SWIR 1 bands. Full article
(This article belongs to the Special Issue Monitoring Soil Moisture Content through Earth Observation)
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