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Advances in Remote Sensing for Regional Soil Moisture Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (14 September 2024) | Viewed by 1332

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Special Issue Editors

Dr. Qi Gao

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Remote Sensing Department, Division of Geomatics, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Gauss, 7 E-08860 Castelldefels, Barcelona, Spain
Interests: SAR and SAR data processing; SAR interferometry; land deformation; hydrology; water management; hazard monitoring
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Dr. Mehrez Zribi

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CESBIO, University of Toulouse, CNRS/UT3/IRD/CNES/INRAE, 31400 Toulouse, France
Interests: remote sensing; synthetic aperture radar; GNSS-R; airborne microwave instrumentation; soil moisture; vegetation; hydrology; water resources
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Prof. Dr. Massimo Menenti

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1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 Delft, The Netherlands
Interests: land surface processes; terrestrial water cycle; water management; optical remote sensing
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Prof. Dr. Jian Peng

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Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Interests: quantitative retrieval of land surface parameters from remote sensing data; radiative transfer in soil–vegetation–atmosphere systems; process-based modeling and data-driven methods; hydroclimatic extremes
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Dr. Tianjie Zhao

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: soil moisture; passive microwave; hydrology
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Special Issue Information

Dear Colleagues,

Remote sensing technologies have revolutionized our ability to monitor soil moisture dynamics at a regional scale, facilitating effective water resource management and sustainable agriculture practices. By leveraging multi-sensor remote sensing observations, in situ measurements, geographical data from multiple thematic scales, and model-data fusion techniques, researchers can capture the spatiotemporal variations of soil moisture and provide valuable insights for decision-making.

This Special Issue focuses on advances in remote sensing for regional soil moisture monitoring. It aims to bring together cutting-edge research in the field, highlighting innovative approaches, case studies, and review discussions that enhance our ability to retrieve and understand soil moisture dynamics and their applications. We welcome submissions that explore the forefront of remote sensing techniques and methodologies specifically tailored to regional soil moisture monitoring.

Topics of interest include, but are not limited to, the following:

Unmanned aerial vehicle remote sensing for the high-resolution and near-real-time monitoring of soil moisture
Innovative remote sensing techniques for the retrieval of soil moisture
Analysis of recently available and near future satellite data products for regional soil moisture monitoring
Airborne calibration and validation experiments to showcase potential innovations for future remote sensing technologies
Case studies demonstrating the application of remote sensing in regional-scale soil moisture assessment
Approaches for integrating remote sensing and in situ observations to improve soil moisture monitoring accuracy
The impacts of soil moisture variability on hydrological processes, agricultural productivity, and climate dynamics
Uncertainty quantification and error propagation in remote-sensing-based soil moisture retrieval

Dr. Qi Gao
Dr. Mehrez Zribi
Prof. Dr. Massimo Menenti
Prof. Dr. Jian Peng
Dr. Tianjie Zhao
Guest Editors

Manuscript Submission Information

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Keywords

remote sensing
soil moisture
water resources management
regional scale
hydrological processes
data integration and interpretation
multi-sensor remote sensing observations

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Research

19 pages, 7218 KiB

Open AccessArticle

Relationship between Vegetation and Soil Moisture Anomalies Based on Remote Sensing Data: A Semiarid Rangeland Case

by Juan José Martín-Sotoca, Ernesto Sanz, Antonio Saa-Requejo, Rubén Moratiel, Andrés F. Almeida-Ñauñay and Ana M. Tarquis

Remote Sens. 2024, 16(18), 3369; https://doi.org/10.3390/rs16183369 - 11 Sep 2024

Viewed by 300

Abstract

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being

Z_{V C I}

and

Z_{W C I}

, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the

Z_{W C I}

had a strong probability of informing in advance, where the negative

Z_{V C I}

will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of

Z_{W C I}

to aid the prediction of

Z_{V C I}

. During these months, using

Z_{W C I}

as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Regional Soil Moisture Monitoring)

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16 pages, 6217 KiB

Open AccessArticle

Medium-Scale Soil Moisture Retrievals Using an ELBARA L-Band Radiometer Using Time-Dependent Parameters for Wetland-Meadow-Cropland Site

by Kamil Szewczak and Mateusz Łukowski

Remote Sens. 2024, 16(12), 2200; https://doi.org/10.3390/rs16122200 - 17 Jun 2024

Viewed by 439

Abstract

The soil moisture at the medium spatial scale is strongly desired in the context of satellite remote sensing data validation. The use of a ground-installed passive L-band radiometer ELBARA at the Bubnów-Sęków test site in the east of Poland gave a possibility to provide reference soil moisture data from the area with a radius of 100 m. In addition, the test site comprised three different land cover types that could be investigated continuously with one day resolution. The studies were focused on the evaluation of the ω-τ model coefficients for three types of land cover, including meadow, wetland, and cropland, to allow for the assessment of the soil moisture retrievals at a medium scale. Consequently, a set of reference time-dependent coefficients of effective scattering albedo, optical depth, and constant-in-time roughness parameters were estimated. The mean annual values of the effective scattering albedo including two polarisations were 0.45, 0.26, 0.14, and 0.54 for the meadow with lower organic matter, the meadow with higher organic matter, the wetland, and the cropland, respectively. The values of optical depth were in the range from 0.30 to 0.80 for the cropland, from 0.40 to 0.52 for the meadows (including the two investigated meadows), and from 0.60 to 0.70 for the wetland. Time-constant values of roughness parameters at the level of 0.45 were obtained. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Regional Soil Moisture Monitoring)

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