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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 73313

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Prof. Dr. Alexander Löw

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Department of Geography, University of Munich (LMU), Luisenstr. 37, 80333, Munich, Germany
Interests: global energy fluxes; remote sensing of land surface parameters; climate modeling; microwave remote sensing

Dr. Jian Peng

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Guest Editor

Max-Planck-Institute for Meteorology, Bundesstr. 53, 20146, Hamburg, Germany
Interests: remote sensing of soil moisture; surface water end engergy fluxes; hydrological and meteorological applications with remote sensing;
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Dear Colleagues,

Monitoring of soil moisture dynamics from local to global scales is essential for a multitude of applications. The field of Remote Sensing of Soil Moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) have been launched, and new missions, such as SENTINEL-1 provide longterm perspectives for land surface monitoring.

This Special Issue aims to summarize the current state-of-the-art in soil moisture remote sensing, including retrieval algorithms, validation, uncertainty assessment, and applications of remote sensing of soil moisture. Contributions using active and passive microwave remote sensing data, as well as thermal infrared information, are encouraged. The validation of satellite soil moisture products can be very challenging due to the different spatial scales of in situ measurements and satellite data. Papers presenting validation studies or methodologies to quantify soil moisture validation uncertainties are therefore invited. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Studies using Remote Sensing soil moisture data for different applications are highly welcome.

algorithm development for the estimation of soil moisture
soil moisture estimation from multiple sensors (e.g., passive, active, thermal)
validation of soil moisture products with new techniques
spatial downscaling of soil moisture
soil moisture products inter-comparison and error quantification
applications of soil moisture:
- agriculture monitoring
- drought monitoring
- meteorological forecasting
- model evaluation
- land-atmosphere interaction
- other applications

Prof. Dr. Alexander Löw
Dr. Jian Peng
Guest Editors

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Keywords

algorithm development
validation activities
satellite missions
downscaling
application

Published Papers (9 papers)

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Editorial

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184 KiB

Open AccessEditorial

Recent Advances in Soil Moisture Estimation from Remote Sensing

by Jian Peng and Alexander Loew

Water 2017, 9(7), 530; https://doi.org/10.3390/w9070530 - 16 Jul 2017

Cited by 49 | Viewed by 9674

Abstract

Monitoring soil moisture dynamics from local to global scales is essential for a wide range of applications. The field of remote sensing of soil moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) were launched, and new missions, such as SENTINEL-1 provide long-term perspectives for land surface monitoring. This special issue aims to summarize the recent advances in soil moisture estimation from remote sensing, including recent advances in retrieval algorithms, validation, and applications of satellite-based soil moisture products. Contributions in this special issue exploit the estimation of soil moisture from both microwave remote sensing data and thermal infrared information. The validation of satellite soil moisture products can be very challenging, due to the different spatial scales of in situ measurements and satellite data. Some papers present validation studies to quantify soil moisture uncertainties. On the other hand, soil moisture downscaling schemes and new methods for soil moisture retrieval from GPS are also addressed by some contributions. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Several studies explore the use of soil moisture data for hydrological application such as runoff prediction. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

Research

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9808 KiB

Open AccessArticle

Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S.

by Ram L. Ray, Ali Fares, Yiping He and Marouane Temimi

Water 2017, 9(6), 372; https://doi.org/10.3390/w9060372 - 25 May 2017

Cited by 27 | Viewed by 8006

Abstract

The main goal of this study was to evaluate four major remote sensing soil moisture (SM) products over the state of Texas. These remote sensing products are: (i) the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) (2002–September 2011); (ii) the Soil Moisture Ocean Salinity system (SMOS, 2010–present); (iii) AMSR2 (2012–present); and (iv) the Soil Moisture Active Passive system (SMAP, 2015–present). The quality of the generated SM data is influenced by the accuracy and precision of the sensors and the retrieval algorithms used in processing raw data. Therefore, it is important to evaluate the quality of these satellite SM products using in situ measurements and/or by inter-comparing their data during overlapping periods. In this study, these two approaches were used where we compared each satellite SM product to in situ soil moisture measurements and we also conducted an inter-comparison of the four satellite SM products at 15 different locations in Texas over six major land cover types (cropland, shrub, grassland, forest, pasture and developed) and eight climate zones along with in situ SM data from 15 Mesonet, USCRN and USDA-NRCS Scan stations. Results show that SM data from SMAP had the best correlation coefficients range from 0.37 to 0.92 with in situ measurements among the four tested satellite surface SM products. On the other hand, SM data from SMOS, AMSR2 and AMSR-E had moderate to low correlation coefficients ranges with in situ data, respectively, from 0.24–0.78, 0.07–0.62 and 0.05–0.52. During the overlapping periods, average root mean square errors (RMSEs) of the correlations between in situ and each satellite data were 0.13 (AMSR-E) and 0.13 (SMOS) cm³/cm³ (2010–2011), 0.16 (AMSR2) and 0.14 (SMOS) cm³/cm³ (2012–2016) and 0.13, 0.16, 0.14 (SMAP, AMSR2, SMOS) cm³/cm³ (2015–2016), respectively. Despite the coarser spatial resolution of all four satellite products (25–36 km), their SM measurements are considered reasonable and can be effectively used for different applications, e.g., flood forecasting, and drought prediction; however, further evaluation of each satellite product is recommended prior to its use in practical applications. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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4162 KiB

Open AccessArticle

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

by Seonyoung Park, Sumin Park, Jungho Im, Jinyoung Rhee, Jinho Shin and Jun Dong Park

Water 2017, 9(5), 332; https://doi.org/10.3390/w9050332 - 07 May 2017

Cited by 32 | Viewed by 8020

Abstract

Soil moisture is a key part of Earth’s climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m³·m⁻³, and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m³·m⁻³) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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3741 KiB

Open AccessArticle

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

by Wooyeon Sunwoo and Minha Choi

Water 2017, 9(2), 77; https://doi.org/10.3390/w9020077 - 27 Jan 2017

Cited by 13 | Viewed by 5206

Abstract

Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC) prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN) method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API₅), ground-based soil moisture (SSM_insitu), remotely sensed surface soil moisture (SSM), and soil water index (SWI) provided by the advanced scatterometer (ASCAT). To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI) and ground based soil moisture data (SSM_insitu). In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API₅ by 60% and 66%, respectively. Moreover, the proposed IWC framework with remotely sensed soil moisture indicates an improved Nash–Sutcliffe efficiency from 0.48 to 0.74 for the four catchments in the Korean Peninsula. It can be concluded that the SCS-CN method extended with remotely sensed soil moisture for reducing uncertainty in the runoff prediction and the proxy variables obtained from the soil moisture data provided by the ASCAT can be useful in enhancing the accuracy of runoff prediction over a range of spatial scales. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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9665 KiB

Open AccessArticle

Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements

by Mohammad Choker, Nicolas Baghdadi, Mehrez Zribi, Mohammad El Hajj, Simonetta Paloscia, Niko E. C. Verhoest, Hans Lievens and Francesco Mattia

Water 2017, 9(1), 38; https://doi.org/10.3390/w9010038 - 11 Jan 2017

Cited by 67 | Viewed by 8084

Abstract

The aim of this paper is to evaluate the most used radar backscattering models (Integral Equation Model “IEM”, Oh, Dubois, and Advanced Integral Equation Model “AIEM”) using a wide dataset of SAR (Synthetic Aperture Radar) data and experimental soil measurements. These forward models [...] Read more.

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) from soil surface characteristics (dielectric constant, roughness) and SAR sensor parameters (radar wavelength, incidence angle, polarization). The analysis dataset is composed of AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR, ERS, RADARSAT, ASAR and TerraSAR-X data and in situ measurements (soil moisture and surface roughness). Results show that Oh model version developed in 1992 gives the best fitting of the backscattering coefficients in HH and VV polarizations with RMSE values of 2.6 dB and 2.4 dB, respectively. Simulations performed with the Dubois model show a poor correlation between real data and model simulations in HH polarization (RMSE = 4.0 dB) and better correlation with real data in VV polarization (RMSE = 2.9 dB). The IEM and the AIEM simulate the backscattering coefficient with high RMSE when using a Gaussian correlation function. However, better simulations are performed with IEM and AIEM by using an exponential correlation function (slightly better fitting with AIEM than IEM). Good agreement was found between the radar data and the simulations using the calibrated version of the IEM modified by Baghdadi (IEM_B) with bias less than 1.0 dB and RMSE less than 2.0 dB. These results confirm that, up to date, the IEM modified by Baghdadi (IEM_B) is the most adequate to estimate soil moisture and roughness from SAR data. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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2881 KiB

Open AccessArticle

Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture

by Yongchul Shin, Kyoung Jae Lim, Kyungwon Park and Younghun Jung

Water 2016, 8(7), 311; https://doi.org/10.3390/w8070311 - 22 Jul 2016

Cited by 3 | Viewed by 5771

Abstract

Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θ_res, θ_sat, K_sat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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4671 KiB

Open AccessArticle

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

by Franziska Koch, Florian Schlenz, Monika Prasch, Florian Appel, Tobias Ruf and Wolfram Mauser

Water 2016, 8(7), 276; https://doi.org/10.3390/w8070276 - 02 Jul 2016

Cited by 23 | Viewed by 8803

Abstract

Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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6768 KiB

Open AccessArticle

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

by Alfonso F. Torres-Rua, Andres M. Ticlavilca, Roula Bachour and Mac McKee

Water 2016, 8(4), 167; https://doi.org/10.3390/w8040167 - 22 Apr 2016

Cited by 30 | Viewed by 9840

Abstract

Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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2952 KiB

Open AccessArticle

Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes

by Thomas K. Alexandridis, Ines Cherif, George Bilas, Waldenio G. Almeida, Isnaeni M. Hartanto, Schalk Jan Van Andel and Antonio Araujo

Water 2016, 8(1), 32; https://doi.org/10.3390/w8010032 - 21 Jan 2016

Cited by 14 | Viewed by 6410

Abstract

Despite playing a critical role in the division of precipitation between runoff and infiltration, soil moisture (SM) is difficult to estimate at the catchment scale and at frequent time steps, as is required by many hydrological, erosion and flood simulation models. In this work, an integrated methodology is described to estimate SM at the root zone, based on the remotely-sensed evaporative fraction (Λ) and ancillary information on soil and meteorology. A time series of Terra MODIS satellite images was used to estimate SM maps with an eight-day time step at a 250-m spatial resolution for three diverse catchments in Europe. The study of the resulting SM maps shows that their spatial variability follows the pattern of land cover types and the main geomorphological features of the catchment, and their temporal pattern follows the distribution of rain events, with the exception of irrigated land. Field surveys provided in situ measurements to validate the SM maps’ accuracy, which proved to be variable according to site and season. In addition, several factors were analyzed in order to explain the variation in the accuracy, and it was shown that the land cover type, the soil texture class, the temporal difference between the datasets’ acquisition and the presence of rain events during the measurements played a significant role, rather than the often referred to scale difference between in situ and satellite observations. Therefore, the proposed methodology can be used operationally to estimate SM maps at the catchment scale, with a 250-m spatial resolution and an eight-day time step. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture)

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