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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 August 2018) | Viewed by 90126

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Special Issue Editors

Dr. Nemesio Rodriguez-Fernandez

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Guest Editor

CESBIO, CNES/CNRS/IRD/UPS, UMR 5126, 31401 Toulouse, CEDEX 9, France
Interests: microwave remote sensing; soil moisture; vegetation optical depth; biomass; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmad Al Bitar

E-Mail Website
Guest Editor

Centre d'Etudes Spatiales de la Biosphère (CESBIO), CNRS-CNES-IRD-Université de Toulouse, CEDEX 9, 31401 Toulouse, France
Interests: soil moisture; hydrology; assimilation; microwave remote sensing; physical modeling; energy budget
Special Issues, Collections and Topics in MDPI journals

Dr. Andreas Colliander

E-Mail Website
Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: microwave remote sensing techniques and algorithms; global water cycle; cryosphere
Special Issues, Collections and Topics in MDPI journals

Dr. Tianjie Zhao

E-Mail Website
Guest Editor

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Interests: microwave remote sensing; soil moisture; soil freeze–thaw; application of satellite information in hydrology and water management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture plays an important role in the water, carbon and energy cycles. The amount of moisture in soil is an important variable to understand the coupling of the surface and the atmosphere. It is a key component in improving weather forecasting and climate models, and it has been recognized as an Essential Climate Variable. Assimilation of soil moisture to land surface models has resulted in increased understanding of processes controlling the energy exchange at the land-atmosphere interface. It is also used to improve rainfall estimations, drought monitoring, land slide prediction and flood forecasting.

Thanks to a number of sensors of different characteristics, it is possible to study the soil moisture content at different spatial scales from tens of kilometres (ASCAT/MetOp, SMAP, SMOS, AMSR-2/GCOM-W, MWRI/FY-3...) to tens/hundreds of meters (Sentinel-1, Radarsat-2, GF-3...), or even at sub-meter resolution using drones. By combining data from different instruments with sensors across the electromagnetic spectrum (different microwave bands, visible, infra-red, active and passive) it is possible to improve the quality of the soil moisture retrievals obtained with a single instrument. Using the currently-available satellites, soil moisture can be monitored at daily frequency and disseminated in near-real-time. Finally, a harmonised processing of observations from different sensors can provide long-term soil moisture records for climate studies.

In this Special Issue, we welcome studies on remote sensing of soil moisture across different spatial and temporal scales. We also welcome studies addressing new missions, such as the Water Cycle Observation Mission (carrying a one-dimensional synthetic-aperture microwave radiometer), possible SMOS and SMAP follow-up missions, and any other missions deploying L-Band instruments. The studies can deal with the retrieval of soil moisture, the validation of remote sensing measurements and their use for scientific research or operational applications.

Potential topics include but are not limited to the following:

Retrieval algorithms, in particular using multi-wavelength, active and passive data, both based on physical models and data-driven methods
Downscaling satellite soil moisture merging data from sensors with different spatial resolutions
Approaches for the harmonised processing of data coming from different sensors to construct longer, coherent, soil moisture records
Validation of satellite soil moisture products, in particular using new techniques for up-scaling and new measurements.
Applications of remotely sensed soil moisture data including data assimilation and disaster assessment

Dr. Nemesio Rodríguez-Fernández
Dr. Ahmad Al Bitar
Dr. Andreas Colliander
Dr. Tianjie Zhao
Guest Editors

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Published Papers (14 papers)

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Editorial

Jump to: Research

5 pages, 184 KiB

Open AccessEditorial

Soil Moisture Remote Sensing across Scales

by Nemesio Rodríguez-Fernández, Ahmad Al Bitar, Andreas Colliander and Tianjie Zhao

Remote Sens. 2019, 11(2), 190; https://doi.org/10.3390/rs11020190 - 19 Jan 2019

Cited by 18 | Viewed by 4465

Abstract

Soil moisture plays an important role in the water, carbon, and energy cycles. We summarize the 13 articles collected in this Special Issue on soil moisture remote sensing across scales in terms of the spatial, temporal, and frequency scales studied. We also review [...] Read more.

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

Research

Jump to: Editorial

26 pages, 4688 KiB

Open AccessArticle

Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS

by Mousong Wu, Marko Scholze, Michael Voßbeck, Thomas Kaminski and Georg Hoffmann

Remote Sens. 2019, 11(1), 27; https://doi.org/10.3390/rs11010027 - 25 Dec 2018

Cited by 14 | Viewed by 4586

Abstract

The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO₂ fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO₂. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands. Full article

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

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29 pages, 5884 KiB

Open AccessArticle

Defining a Trade-off Between Spatial and Temporal Resolution of a Geosynchronous SAR Mission for Soil Moisture Monitoring

by Luca Cenci, Luca Pulvirenti, Giorgio Boni and Nazzareno Pierdicca

Remote Sens. 2018, 10(12), 1950; https://doi.org/10.3390/rs10121950 - 4 Dec 2018

Cited by 6 | Viewed by 6121

Abstract

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≤1 km) and temporal (≤12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture–data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps. Full article

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

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Graphical abstract

22 pages, 6257 KiB

Open AccessArticle

Estimating Soil Evaporation Using Drying Rates Determined from Satellite-Based Soil Moisture Records

by Eric E. Small, Andrew M. Badger, Ronnie Abolafia-Rosenzweig and Ben Livneh

Remote Sens. 2018, 10(12), 1945; https://doi.org/10.3390/rs10121945 - 4 Dec 2018

Cited by 11 | Viewed by 4921

Abstract

We describe an approach (ESMAP; Evaporation–Soil Moisture Active Passive) to estimate direct evaporation from soil, E_soil, by combining remotely-sensed soil drying rates with model calculations of the vertical fluxes in and out of the surface soil layer. Improved knowledge of E_soil can serve as a constraint in how total evapotranspiration is partitioned. The soil drying rates used here are based on SMAP data, but the method could be applied to data from other sensors. We present results corresponding to ten SMAP pixels in North America to evaluate the method. The ESMAP method was applied to intervals between successive SMAP overpasses with limited precipitation (<2 mm) to avoid uncertainty associated with precipitation, infiltration, and runoff. We used the Hydrus 1-D model to calculate the flux of water across the bottom boundary of the 0 to 50 mm soil layer sensed by SMAP, q_bot. During dry intervals, q_bot typically transfers water upwards into the surface soil layer from below, usually <0.5 mm day⁻¹. Based on a standard formulation, transpiration from the surface soil layer, E_{T_s}, is usually < 0.1 mm day⁻¹, and, thus, generally not an important flux. Soil drying rates (converted to equivalent water thickness) are typically between 0 and 1 mm day⁻¹. Evaporation is almost always greater than soil drying rates because q_bot is typically a source of water to the surface soil and E_{T_s} is negligible. Evaporation is typically between 0 and 1.5 mm day⁻¹, with the highest values following rainfall. Soil evaporation summed over SMAP overpass intervals with precipitation <2 mm (60% of days) accounts for 15% of total precipitation. If evaporation rates are similar during overpasses with substantial precipitation, then the total evaporation flux would account for ~25% of precipitation. ESMAP could be used over spatially continuous domains to provide constraints on E_soil, but model-based E_soil would be required during intervals with substantial precipitation. Full article

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

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24 pages, 2678 KiB

Open AccessFeature PaperArticle

“Tau-Omega”- and Two-Stream Emission Models Used for Passive L-Band Retrievals: Application to Close-Range Measurements over a Forest

by Mike Schwank, Reza Naderpour and Christian Mätzler

Remote Sens. 2018, 10(12), 1868; https://doi.org/10.3390/rs10121868 - 22 Nov 2018

Cited by 37 | Viewed by 4913

Abstract

Microwave Emission Models (EM) are used in retrieval algorithms to estimate geophysical state parameters such as soil Water Content (

W C

) and vegetation optical depth (

τ

), from brightness temperatures

T_{B}^{p, θ}

measured at nadir angles [...] Read more.

Microwave Emission Models (EM) are used in retrieval algorithms to estimate geophysical state parameters such as soil Water Content (

W C

) and vegetation optical depth (

τ

), from brightness temperatures

T_{B}^{p, θ}

measured at nadir angles

θ

at Horizontal and Vertical polarizations

p = {H, V}

. An EM adequate for implementation in a retrieval algorithm must capture the responses of

T_{B}^{p, θ}

to the retrieval parameters, and the EM parameters must be experimentally accessible and representative of the measurement footprint. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the “Tau-Omega” (TO) EM considered as the “reference” to retrieve

W C

and

τ

from L-band

T_{B}^{p, θ}

. For sparse and low-scattering vegetation

T_{B, E M}^{p, θ}

simulated with

E M = {TO, 2 S}

converge. This is not the case for dense and strongly scattering vegetation. Two-Parameter (2P) retrievals

2 P_{R C} = (W C_{R C}, τ_{R C})

are computed from elevation scans

T_{B}^{p, θ_{j}} = T_{B, TO}^{p, θ_{j}}

synthesized with TO EM and from

T_{B}^{p, θ_{j}}

measured from a tower within a deciduous forest. Retrieval Configurations (

R C

) employ either

E M = TO

E M = 2 S

and assume fixed scattering albedos.

W C_{R C}

achieved with the 2S RC is marginally lower (

~ 1 m^{3} m^{- 3}

) than if achieved with the “reference” TO RC, while

τ_{R C}

is reduced considerably when using 2S EM instead of TO EM. Our study outlines a number of advantages of the 2S EM over the TO EM currently implemented in the operational SMOS and SMAP retrieval algorithms. Full article

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

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26 pages, 3779 KiB

Open AccessArticle

Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

by Christof Lorenz, Carsten Montzka, Thomas Jagdhuber, Patrick Laux and Harald Kunstmann

Remote Sens. 2018, 10(11), 1842; https://doi.org/10.3390/rs10111842 - 20 Nov 2018

Cited by 15 | Viewed by 4911

Abstract

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture. Full article

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

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28 pages, 5690 KiB

Open AccessArticle

Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

by Thomas Meyer, Lutz Weihermüller, Harry Vereecken and François Jonard

Remote Sens. 2018, 10(10), 1637; https://doi.org/10.3390/rs10101637 - 15 Oct 2018

Cited by 19 | Viewed by 5442

Abstract

L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (T_B) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from T_B observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured T_B. Full article

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

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22 pages, 4019 KiB

Open AccessArticle

Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.

by Hongzhang Xu, Qiangqiang Yuan, Tongwen Li, Huanfeng Shen, Liangpei Zhang and Hongtao Jiang

Remote Sens. 2018, 10(9), 1351; https://doi.org/10.3390/rs10091351 - 24 Aug 2018

Cited by 32 | Viewed by 6011

Abstract

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm³ cm⁻³. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM. Full article

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

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19 pages, 9744 KiB

Open AccessArticle

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

by Alzira G. S. S. Souza, Alfredo Ribeiro Neto, Luciana Rossato, Regina C. S. Alvalá and Laio L. Souza

Remote Sens. 2018, 10(8), 1314; https://doi.org/10.3390/rs10081314 - 20 Aug 2018

Cited by 31 | Viewed by 8081

Abstract

The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m³m⁻³ and 0.04 m³m⁻³, respectively. Those values are near to the expected 0.04 m³m⁻³ accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers. Full article

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

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29 pages, 6253 KiB

Open AccessArticle

Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil

by Franklin Paredes-Trejo, Humberto Alves Barbosa and Luciana Rossato Spatafora

Remote Sens. 2018, 10(7), 1093; https://doi.org/10.3390/rs10071093 - 9 Jul 2018

Cited by 27 | Viewed by 4435

Abstract

Microwave-based satellite rainfall products offer an opportunity to assess rainfall-related events for regions where rain-gauge stations are sparse, such as in Northeast Brazil (NEB). Accurate measurement of rainfall is vital for water resource managers in this semiarid region. In this work, the SM2RAIN-CCI rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI), and ones from three state-of-the-art rainfall products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center Morphing Technique (CMORPH), and Multi-SourceWeighted-Ensemble Precipitation (MSWEP)) were evaluated against in situ rainfall observations under different bioclimatic conditions at the NEB (e.g., AMZ, Amazônia; CER, Cerrado; MAT, Mata Atlântica; and CAAT, Caatinga). Comparisons were made at daily, 5-day, and 0.25° scales, during the time-span of 1998 to 2015. It was found that 5-day SM2RAIN-CCI has a reasonably good performance in terms of the correlation coefficient over the CER biome (R median: 0.75). In terms of the root mean square error (RMSE), it exhibits better performance in the CAAT biome (RMSE median: 12.57 mm). In terms of bias (B), the MSWEP, SM2RAIN-CCI, and CHIRPS datasets show the best performance in MAT (B median: −8.50%), AMZ (B median: −0.65%), and CER (B median: 0.30%), respectively. Conversely, CMORPH poorly represents the rainfall variability in all biomes, particularly in the MAT biome (R median: 0.43; B median: −67.50%). In terms of detection of rainfall events, all products show good performance (Probability of detection (POD) median > 0.90). The performance of SM2RAIN-CCI suggests that the SM2RAIN algorithm fails to estimate the amount of rainfall under very dry or very wet conditions. Overall, results highlight the feasibility of SM2RAIN-CCI in those poorly gauged regions in the semiarid region of NEB. Full article

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

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17 pages, 3044 KiB

Open AccessFeature PaperArticle

Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France

by Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi, Nemesio Rodríguez-Fernández, Jean Pierre Wigneron, Amen Al-Yaari, Ahmad Al Bitar, Clément Albergel and Jean-Christophe Calvet

Remote Sens. 2018, 10(4), 569; https://doi.org/10.3390/rs10040569 - 7 Apr 2018

Cited by 77 | Viewed by 9996

Abstract

This study evaluates the accuracy of several recent remote sensing Surface Soil Moisture (SSM) products at sites in southwestern France. The products used are Soil Moisture Active Passive “SMAP” (level 3: 36 km × 36 km, level 3 enhanced: 9 km × 9 km, and Level 2 SMAP/Sentinel-1: 1 km × 1km), Advanced Scatterometer “ASCAT” (level 2 with three spatial resolution 25 km × 25 km, 12.5 km × 12.5 km, and 1 km × 1 km), Soil Moisture and Ocean Salinity “SMOS” (SMOS INRA-CESBIO “SMOS-IC”, SMOS Near-Real-Time “SMOS-NRT”, SMOS Centre Aval de Traitement des Données SMOS level 3 “SMOS-CATDS”, 25 km × 25 km) and Sentinel-1(S1) (25 km × 25 km, 9 km × 9 km, and 1 km × 1 km). The accuracy of SSM products was computed using in situ measurements of SSM observed at a depth of 5 cm. In situ measurements were obtained from the SMOSMANIA ThetaProbe (Time Domaine reflectometry) network (7 stations between 1 January 2016 and 30 June 2017) and additional field campaigns (near Montpellier city in France, between 1 January 2017 and 31 May 2017) in southwestern France. For our study sites, results showed that (i) the accuracy of the Level 2 SMAP/Sentinel-1 was lower than that of SMAP-36 km and SMAP-9 km; (ii) the SMAP-36 km and SMAP-9 km products provide more precise SSM estimates than SMOS products (SMOS-IC, SMOS-NRT, and SMOS-CATDS), mainly due to higher sensitivity of SMOS to RFI (Radio Frequency Interference) noise; and (iii) the accuracy of SMAP-36 km and SMAP-9 km products was similar to that of ASCAT (ASCAT-25 km, ASCAT-12.5 km and ASCAT-1 km) and S1 (S1-25 km, S1-9 km, and S1-1 km) products. The accuracy of SMAP, Sentinel-1 and ASCAT SSM products calculated using the average of statistics obtained on each site is defined by a bias of about −3.2 vol. %, RMSD (Root Mean Square Difference) about 7.6 vol. %, ubRMSD (unbiased Root Mean Square Difference)about 5.6 vol. %, and R coefficient about 0.57. For SMOS products, the station average bias, RMSD, ubRMSD, and R coefficient were about −10.6 vol. %, 12.7 vol. %, 5.9 vol. %, and 0.49, respectively. Full article

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

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16 pages, 41040 KiB

Open AccessEditor’s ChoiceArticle

The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution Model Simulations and In-Situ Observations on the Tibetan Plateau

by Chengwei Li, Hui Lu, Kun Yang, Menglei Han, Jonathon S. Wright, Yingying Chen, Le Yu, Shiming Xu, Xiaomeng Huang and Wei Gong

Remote Sens. 2018, 10(4), 535; https://doi.org/10.3390/rs10040535 - 31 Mar 2018

Cited by 45 | Viewed by 8594

Abstract

The Soil Moisture Active Passive (SMAP) mission was designed to provide a global mapping of soil moisture (SM) measured by L-band passive and active microwave sensors. In this study, we evaluate the newly released SMAP enhanced SM products over the Tibetan Plateau by performing comparisons among SMAP standard products, in-situ observations and Community Land Model (CLM) simulations driven by high-resolution meteorological forcing. At local scales, the enhanced SMAP products, the standard products and CLM simulations all generally compare well with the in-situ observations. The SMAP products show stronger correlations (0.64–0.88) but slightly larger unbiased root mean square errors (ubRMSE, ~0.06) relative to the CLM simulations (0.58–0.79 and 0.037–0.047, for correlation and ubRMSE, respectively). At the regional scale, both SMAP products show similar spatial distributions of SM on the TP (Tibetan Plateau), although, as expected, the enhanced product provides more fine details. The SMAP enhanced product is in good agreement with model simulations with respect to temporal and spatial variations in SM over most of the TP. Regions with low correlation between SMAP enhanced products and model simulations are mainly located in the northwestern TP and regions of complex topography, where meteorological stations are sparse and non-existent or elevation is highly variable. In such remote regions, CLM simulations may be problematic due to inaccurate land cover maps and/or uncertainties in meteorological forcing. The independent, high-resolution observations provided by SMAP could help to constrain the model simulation and, ultimately, improve the skill of models in these problematic regions. Full article

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

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19 pages, 1385 KiB

Open AccessArticle

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

by Mutian Han, Yunlong Zhu, Dongkai Yang, Xuebao Hong and Shuhui Song

Remote Sens. 2018, 10(2), 280; https://doi.org/10.3390/rs10020280 - 11 Feb 2018

Cited by 30 | Viewed by 5491

Abstract

The Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR) technique on soil moisture remote sensing was studied. A semi-empirical Signal-to-Noise Ratio (SNR) model was proposed as a curve-fitting model for SNR data routinely collected by a GNSS receiver. This model aims at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This is achieved empirically through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al., T. Yang et al.), this model can improve the Quality of Fit (QoF) with little prior knowledge needed and can allow soil permittivity to be estimated from the reconstructed signals. In developing this model, we showed how noise affects the receiver SNR estimation and thus the model performance through simulations under the bare soil assumption. Results showed that the reconstructed signals with a grazing angle of 5°–15° were better for soil moisture retrieval. The QoF was improved by around 45%, which resulted in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected. Experimental data collected at Lamasquère, France, were also used to validate the proposed model. The results were compared with the simulation and previous works. It was found that the model could ensure good fitting quality even in the case of irregular SNR variation. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements. A deeper insight into the Larson model and the proposed model was given at this stage, which formed a possible explanation of this fact. Furthermore, frequency and phase information extracted using this model were also studied for their capability to monitor soil moisture variation. Finally, phenomena such as retrieval ambiguity and error sensitivity were stated and discussed. Full article

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

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5399 KiB

Open AccessEditor’s ChoiceArticle

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

by Chenyang Cui, Jia Xu, Jiangyuan Zeng, Kun-Shan Chen, Xiaojing Bai, Hui Lu, Quan Chen and Tianjie Zhao

Remote Sens. 2018, 10(1), 33; https://doi.org/10.3390/rs10010033 - 25 Dec 2017

Cited by 149 | Viewed by 10495

Abstract

A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of ~0.25°, and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of ~0.1°. The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m³ m⁻³, whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m³ m⁻³. The JAXA product performed much better at 0.25° than at 0.1°, but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m³ m⁻³). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m³ m⁻³. The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m³ m⁻³ for LWW and 0.016 m³ m⁻³ for REMEDHUS) and SMOS (−0.004 m³ m⁻³ for LWW and −0.012 m³ m⁻³ for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m³ m⁻³), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the potential in integrating them into the existing long-term ESA CCI product, in order to form a more reliable and useful product. Full article

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

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