Special Issue "Remote Sensing of Regional Soil Moisture"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Dr. Marion Pause Website E-Mail
Institute of Photogrammetry and Remote Sensing, Faculty of Environmental Science, Technical University of Dresden, 01062 Dresden, Germany
Interests: multi-sensor remote sensing; soil moisture remote sensing; environmental monitoring; in-situ/remote sensing integration; remote sensing higher education
Guest Editor
Dr. Thomas Wöhling Website E-Mail
Institute of Hydrology and Meteorology, Faculty of Environmental Science, Technical University of Dresden, 01062 Dresden, Germany
Interests: hydrological modelling; evaluation and optimisation of monitoring networks; inverse modelling; model calibration
Guest Editor
Prof. Dr. Karsten Schulz Website E-Mail
Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18 1190 Vienna, Austria
Interests: catchment hydrology; remote sensing; uncertainty estimation; soil-plant-atmosphere interactions; hydrological modelling

Special Issue Information

Dear Colleagues,

Land surface soil moisture conditions play a key role in controlling the water and energy cycle at the land surface. Soil moisture conditions strongly influence the partitioning of precipitation into surface runoff, infiltration and evapotranspiration and allows the partitioning of net radiation into ground, sensible, and latent heat fluxes. The availability of water also controls plant photosynthesis and thereby agricultural productivity, as well as carbon exchange processes between the land surface and the atmosphere. Therefore, soil moisture monitoring is important to obtain reliable information about the spatial distribution and temporal dynamics of land surface water content.

The demand for soil moisture observations to run hydrological simulation models and assess regional water scarcity is increasing at the regional management scale.

About 20 years of experience from remote sensing based research for soil moisture retrieval is available and recent advances in data science and web services path the way for innovations. Novel developments on in-situ sensor technologies and terrestrial monitoring networks provide an essential point-based component for satellite based product validation. In turn, this may be fundamental for innovations of satellite remote sensing based soil moisture retrieval approaches.

The aim of this Special Issue is to present novel approaches, case studies and review discussions of remote sensing based surface soil moisture retrieval at or transferable to the regional scale.

Contributions combining multi-sensor remote sensing observations, in-situ measurements and geographical data from multiple thematic scales to quantify spatial and temporal change pattern are also among our priorities.

Contributions include, but are not limited to, the following:

  • Advances in remote sensing techniques to provide (time series of) spatially distributed soil moisture data
  • Recently available and near future satellite data products
  • Airborne cal/val experiments to present future potential innovations
  • Case studies at regional scale
  • Approaches for remote sensing/in-situ observation integration
  • Studies using data assimilation, e.g., into hydrological models, plant growth models or discussing concepts

Dr. Marion Pause
Dr. Thomas Wöhling
Prof. Dr. Karsten Schulz
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 papers will be 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 1800 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

  • Remote sensing
  • Soil moisture
  • Hydrological modelling
  • Water scarcity
  • Regional scale
  • Microwave remote sensing
  • Optical remote sensing
  • Thermal remote sensing
  • Multi-sensor approach
  • Environmental monitoring

Published Papers (4 papers)

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Open AccessArticle
A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation
Remote Sens. 2019, 11(17), 1968; https://doi.org/10.3390/rs11171968 - 21 Aug 2019
Abstract
Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several [...] Read more.
Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time). Full article
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
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Open AccessArticle
Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution
Remote Sens. 2019, 11(16), 1863; https://doi.org/10.3390/rs11161863 - 09 Aug 2019
Abstract
Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM [...] Read more.
Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions. Full article
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
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Open AccessArticle
Evaluation and Analysis of AMSR2 and FY3B Soil Moisture Products by an In Situ Network in Cropland on Pixel Scale in the Northeast of China
Remote Sens. 2019, 11(7), 868; https://doi.org/10.3390/rs11070868 - 10 Apr 2019
Abstract
An in situ soil moisture observation network at pixel scale is constructed in cropland in the northeast of China for accurate regional soil moisture evaluations of satellite products. The soil moisture products are based on the Japan Aerospace Exploration Agency (JAXA) algorithm and [...] Read more.
An in situ soil moisture observation network at pixel scale is constructed in cropland in the northeast of China for accurate regional soil moisture evaluations of satellite products. The soil moisture products are based on the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the products from the FengYun-3B (FY3B) satellite are evaluated using synchronous in situ data collected by the EC-5 sensors at the surface in a typical cropland in the northeast of China during the crop-growing season from May to September 2017. The results show that the JAXA product provides an underestimation with a bias (b) of -0.094 cm3/cm3, and the LPRM soil moisture product generates an overestimation with a b of 0.156 cm3/cm3. However the LPRM product shows a better correlation with the in situ data, especially in the early experimental period when the correlation coefficient is 0.654, which means only the JAXA product in the early stage, with an unbiased root mean square error (ubRMSE) of 0.049 cm3/cm3 and a b of -0.043 cm3/cm3, reaches the goal accuracy (±0.05 cm3/cm3). The FY3B has consistently obtained microwave brightness temperature data, but its soil moisture product data in the study area is seriously missing during most of the experimental period. However, it recovers in the later period and is closer to the in situ data than the JAXA and LPRM products. The three products show totally different trends with vegetation cover, soil temperature, and actual soil moisture itself in different time periods. The LPRM product is more sensitive and correlated with the in situ data, and is less susceptible to interferences. The JAXA is numerically closer to the in situ data, but the results are still affected by temperature. Both will decrease in accuracy as the actual soil moisture increases. The FY3B seems to perform better at the end of the whole period after data recovery. Full article
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
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Open AccessTechnical Note
Development of a Multimode Field Deployable Lidar Instrument for Topographic Measurements of Unsaturated Soil Properties: Instrument Description
Remote Sens. 2019, 11(3), 289; https://doi.org/10.3390/rs11030289 - 01 Feb 2019
Cited by 1
Abstract
The hydrological and mechanical behavior of soil is determined by the moisture content, soil water (matric) potential, fines content, and plasticity. However, these parameters are often difficult or impractical to determine in the field. Remote characterization of soil parameters is a non-destructive data [...] Read more.
The hydrological and mechanical behavior of soil is determined by the moisture content, soil water (matric) potential, fines content, and plasticity. However, these parameters are often difficult or impractical to determine in the field. Remote characterization of soil parameters is a non-destructive data collection process well suited to large or otherwise inaccessible areas. A ground-based, field-deployable remote sensor, called the soil observation laser absorption spectrometer (SOLAS), was developed to collect measurements from the surface of bare soils and to assess the in-situ condition and essential parameters of the soil. The SOLAS instrument transmits coherent light at two wavelengths using two, continuous-wave, near-infrared diode lasers and the instrument receives backscattered light through a co-axial 203-mm diameter telescope aperture. The received light is split into a hyperspectral sensing channel and a laser absorption spectrometry (LAS) channel via a multi-channel optical receiver. The hyperspectral channel detects light in the visible to shortwave infrared wavelengths, while the LAS channel filters and directs near-infrared light into a pair of photodetectors. Atmospheric water vapor is inferred using the differential absorption of the on- and off-line laser wavelengths (823.20 nm and 847.00 nm, respectively). Range measurement is determined using a frequency-modulated, self-chirped, coherent, homodyne detection scheme. The development of the instrument (transmitter, receiver, data acquisition components) is described herein. The potential for rapid characterization of physical and hydro-mechanical soil properties, including volumetric water content, matric potential, fines content, and plasticity, using the SOLAS remote sensor is discussed. The envisioned applications for the instrument include assessing soils on unstable slopes, such as wildfire burn sites, or stacked mine tailings. Through the combination of spectroradiometry, differential absorption, and range altimetry methodologies, the SOLAS instrument is a novel approach to ground-based remote sensing of the natural environment. Full article
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
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