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Special Issue "Satellite Remote Sensing for Water Resources in a Changing Climate"

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

Deadline for manuscript submissions: 31 January 2018

Special Issue Editors

Guest Editor
Dr. George P. Petropoulos

Department of Geography and Earth Sciences, University of Aberystwyth, Old College, King Street Llandinam Building, Room H4 Aberystwyth, Ceredigion SY23 3DB, UK
Website | E-Mail
Phone: +44-0-1970-621861
Interests: Earth Observation; GIS; multi- and hyper- spectral remote sensing; land use/cover mapping; change detection; natural hazards; fires; floods; land surface interactions; evapotranspiration; soil moisture; land surface temperature; land biosphere modelling; Soil Vegetation Atmosphere Transfer (SVAT) models; EO algorithms benchmarking; sensitivity analysis
Guest Editor
Dr. Simonetta Paloscia

Consiglio Nazionale delle Ricerche, Institute of Applied Physics, Rome, Italy
Website | E-Mail
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR; microwave radiometry
Guest Editor
Dr. Prashant K. Srivastava

Hydrological Sciences, NASA GSFC, Greenbelt, Maryland, USA and IESD, Banaras Hindu University, Varanasi, India
Website | E-Mail
Phone: +91-7571927744
Interests: microwave active and passive; optical/IR; hydrology; soil moisture; sensitivity and uncertainty analysis; artificial intelligence; geospatial technology; classification methods; simulation and modelling
Guest Editor
Prof. Guangsheng Zhou

State Key Laboratory of Vegetation and Environmental Change (LVEC), The Institute of Botany, the Chinese Academy of Sciences, No.20 Nanxincun, Xiangshan, No.46, Zhongguancun South Street, Beijing 100093, China
Website | E-Mail
Phone: +86-10-62836268
Interests: plant and crop ecology; crop drought and irrigation; vegetation water stress; net primary productivity; plant biodiversity; multi-temporal remote sensing; multi-spectral; hyper-spectral

Special Issue Information

Dear Colleagues,

Water is one of the most important substances on Earth. It is a key variable in Earth’s hydrological cycle for water and energy exchanges that occur at the land–surface/atmosphere interface, and is responsible for the evolution of weather and climate over continental regions. Information on our planet’s water resources is indispensable to a number of practical applications related to both society and ecosystems. Globally, the monitoring of the Earth’s water resources has developed into a very important and urgent research direction, especially in the face of climate change.

However, the amount of water available throughout the world is already limited, and demand will continue to rise as population grows. In this context, there is a growing need to monitor and obtain a better understanding of its use, which will provide information that can assist towards the development of effective water management strategies and infrastructures. This can be of crucial importance, particularly to regions on which the amount of water available is limited.

Water resource modeling and management includes the activity of planning, developing, distributing and managing the optimum use of water resources in a simplistic manner. As compared to other natural resources, modeling and management of water is complicated in practice. The use of satellites for the management of water can play an important role in the future of water resources. The launch of Earth Observation (EO) sensors from advanced satellites, such as SMOS, Landsat 8, Sentinel-2/3, GCOM-W1, SMAP, GPM, TRMM, etc. has the potential to reshape the water world. These instruments provide necessary data that can make up for the lack of on-the-ground monitoring of water resources around the world.

Therefore, the main aim of this Special Issue is to foster advances in EO technology for water resources management with the scope of flood, drought, irrigation, soil moisture retrieval, algorithms development, operational products benchmarking, precipitation, modelling and applications. In particular, submission of article exploring the use of New Earth Observation missions providing data at all the different regions of the electromagnetic spectrum are highly encouraged. Both applied EO technology to water resources management implementation and models’ or algorithms’ related scientific investigations are encouraged.

Consequently, topics of interest to the Special Issue may include, but are not limited to, the following:

Keywords:

  • Multi-spectral imagery
  • Hyper-spectral imagery
  • Thermal infrared imagery
  • SAR processing
  • Time series analysis
  • Soil moisture mapping
  • Vegetation Water stress
  • Crop Drought and irrigation
  • Water Use Efficiency
  • Yield mapping
  • Decision support systems
  • Meteorological disaster risk management
  • Early warning systems for agrometerological hazards.
  • Agro-informatics and agricultural water management
  • Groundwater level monitoring
  • Geo-informatic
  • Natural disasters management, e.g., floods and droughts

Authors are required to check and follow the specific Instructions to Authors, http://www.mdpi.com/journal/remotesensing/instructions.

Dr. George P. Petropoulos
Dr. Guangsheng Zhou
Dr. Prashant Srivastava
Dr. Simonetta Paloscia
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 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 1600 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.

Published Papers (1 paper)

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Research

Open AccessArticle A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging
Remote Sens. 2017, 9(8), 870; doi:10.3390/rs9080870
Received: 25 July 2017 / Revised: 11 August 2017 / Accepted: 19 August 2017 / Published: 22 August 2017
PDF Full-text (53630 KB) | HTML Full-text | XML Full-text
Abstract
This study attempts to estimate spatial soil moisture in South Korea (99,000 km2) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation
[...] Read more.
This study attempts to estimate spatial soil moisture in South Korea (99,000 km2) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R2) of the original LST and observed LST was 0.71, and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R2 values were between 0.28 and 0.67. The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evolvement of the Standardized Drought Vulnerability Index towards Spatiotemporal Drought Vulnerability Propagation Combining Satellite and in Situ Data
Authors: Panagiotis D. Oikonomou a*, Demetrios E. Tsesmelis b, Reagan M. Waskom a, Konstantinos G. Arvanitis b & Christos A. Karavitis b
Affiliations: a Colorado Water Institute, Colorado State University, Campus Delivery 1033, Fort Collins, Colorado 80523-1033, USA
b Department of Natural Resources Development & Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
* Corresponding author’s email: panagiotis.oikonomou@colostate.edu
Abstract: Drought is a complex natural hazard with its adverse multifaceted impacts cascading in every physical and human system. The vulnerability magnitude of various areas to drought mostly depends on their exposure to water deficiency and to the existing water management policy framework. Further on, a system’s vulnerability to drought is strongly influenced by a plethora of factors with the precipitation patterns, the supply and demand trends, and the socioeconomic background to be among the most important ones. The Standardized Drought Vulnerability Index (SDVI) is an integrated attempt towards characterizing drought vulnerability based on a comparative classification system. The present work attempts to evolve the SDVI on the next level incorporating a more rigorous method of index estimation by proposing a methodology surpassing previous limitations in temporal and spatial propagation of the vulnerability concept and remote sensing techniques in an effort to further minimize the paucity of drought related data. The new framework in applied in the South Platte Basin. Colorado, USA and the studied event is the 2012 drought (July-September). The evolved transformation of the index may convey drought information better to decision makers and in a more holistic manner, offering a more complete understanding of the contribution of each index component and at the same time avoiding existing practices of broken linkages and fragmentation of reported impacts through the use of satellite remote sensing. Then, it is believed that SDVI could serve as an additional tool to guide decisions and target mitigation and adaptation actions thus allowing for an integrated management approach.
Keywords: Drought; Drought Vulnerability; Satellite Remote Sensing; Drought impacts; Drought management; Standardized Drought Vulnerability Index; South Platte; Water resources

Type of Paper Article
Tentative Title:
Soil water content retrieval based on Sentinel-1 and Landsat 8 data using a modified water-cloud model
Authors: Yansong Bao, Libin Lin, Shanyu Wu, Deng Khidir Abdalla Kwal, George P. Petropoulos
Affiliations: 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China;2. School of Atmospheric physics, Nanjing University of Information Science and Technology, Nanjing 210044, China; 3. Department of Geography and Earth Sciences, University of Aberystwyth, Wales, United Kingdom.
Abstract: Soil water content is an important parameter of land surface model, which plays a crucial role in the exchange of substances and energy between land and atmosphere. In addition, soil water is also a key variable for vegetation growth. This study aims to soil moisture retrieval from Sentinel-1 SAR and Landsat Oli data. In order to remove vegetation water content effects, the Landsat Oli spectral index was applied to establish the inversion model of vegetation water content. The model was combined with the original water-cloud model, and a modified water-cloud model with spectral index was build. And then, the soil moisture retrieval model was developed based on the modified water-cloud model. Finally, the validation of the soil moisture retrieval model was conducted in British and Spanish. The results show that: (1) a nonlinear model with Normalized Difference Water Index (NDWI) based on the Landsat 5th and 7 th band reflectance is more suitable to estimate the vegetation water content; (2) compared with the Sentinel-1 VH polarization data, the backscattering coefficient of VV polarization is more suitable for soil moisture retrieval; (3) the inversion model can acquire desired accuracy for soil moisture estimation with R2 = 0.8865 and RMSE = 4.88%.

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