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Retrieval, Validation and Application of Satellite Soil Moisture Data

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

Deadline for manuscript submissions: closed (20 November 2017) | Viewed by 86737

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Special Issue Editors

Prof. Dr. Wolfgang Wagner

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Guest Editor

Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
Interests: remote sensing; geophysical parameter retrieval; airborne laser scanning; full-waveform lidar; radar remote sensing; soil moisture
Special Issues, Collections and Topics in MDPI journals

Dr. Claudia Notarnicola

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Guest Editor

EURAC Research – Institute for Earth Observation, Viale Druso 1, 39100 Bolzano, Italy
Interests: retrieval of bio-physical parameters from optical and radar data; multi-sensor data fusion; integrated approach for environmental monitoring in mountain areas
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. John J. Qu

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Guest Editor

Professor and Director, GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; earth system and climate science; soil moisture and drought monitoring; water-energy-food nexus; environment and fire science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dongryeol Ryu

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Guest Editor

Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Austrilia
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have seen a proliferation in the provision and use of satellite soil moisture data derived from active and passive microwave sensors (SMAP, SMOS, ASCAT, AMRS-2, Sentinel-1, etc.) and optical/thermal imagers. However, there are still many open scientific question related to the way of how soil moisture data are being retrieved, validated and applied. Additionally, there is a high need to develop novel approaches for improving the spatio-temporal sampling of the data and their accuracy. Therefore, the purpose of this Special Issue is to discuss and reconcile recent methodological advances in the development, validation and application of global satellite soil moisture data. This Special Issue is related to the 4^th Satellite Soil Moisture Validation and Application Workshop, which will place at the Vienna University of Technology (TU Wien) from 19–20 September 2017 (http://smw.geo.tuwien.ac.at/). Topics to be discussed at the workshop are:

What is the quality of the current satellite products and what can we expect in the near future?
What is information content at Level 1 and how to exploit the availability of multiple satellites?
Who is using satellite soil moisture data and for what purpose?
What are the best practices in validating soil moisture products?
What are the main limitations of satellite soil moisture data from a user’s perspective?
What is the future of satellite-based soil moisture remote sensing?

We invite in particular authors attending the workshop to submit their papers to this Special Issue of Remote Sensing.

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

Univ.-Prof. Wolfgang Wagner
Dr. Claudia Notarnicola
Univ.-Prof. John J. Qu
Univ.-Prof. Dongryeol Ryu
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 submissions that pass pre-check are 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 2700 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 (13 papers)

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Research

16 pages, 3208 KiB

Open AccessArticle

Estimating Vegetation Water Content and Soil Surface Roughness Using Physical Models of L-Band Radar Scattering for Soil Moisture Retrieval

by Seung-Bum Kim, Huanting Huang, Tien-Hao Liao and Andreas Colliander

Remote Sens. 2018, 10(4), 556; https://doi.org/10.3390/rs10040556 - 04 Apr 2018

Cited by 18 | Viewed by 5510

Abstract

Soil surface roughness and above-ground vegetation water content (VWC) are estimated by inverting physical models for L-band scattering and absorption at 40° incidence angle using ground, airborne and Soil Moisture Active Passive (SMAP) radar data. The spatial resolution varies from field scale (airborne and ground) to 3 km (SMAP). The temporal resolution is defined by the length and interval of observation time windows (weeks to three months for surface roughness, and three to seven days for VWC). The validation of the roughness estimates shows an accuracy of 25% (bare surface) and 29 to 46% (croplands and pasture). The correlation degrades as vegetation becomes thicker, indicating the stronger scattering and absorption by thicker vegetation. The roughness retrievals with the SMAP data are within the physical range of 0.5 cm to 4 cm. They show larger values in croplands than in natural terrain. The VWC estimate modifies a ‘first guess’ (in situ values for the airborne experiment; and 16-daily climatology for SMAP). The VWC retrievals correctly follow the full growth of crops and the RMSE is smaller than 20% in the airborne retrievals: the correlation ranges from 0.57 to 0.91. These results demonstrate that the forward model inversion has a potential to retrieve VWC for the four major crops over the entire phase of the crop growth. The VWC retrievals from the SMAP data revised the climatology first guess more in the croplands, where the climatology is more likely to depart from the contemporaneous condition than in natural landcover. The value of this work lies in the fact that the surface roughness at the footprint scale is difficult to characterize and a global VWC product at SMAP’s spatial scale from microwave observations is rare, and that this paper presents a plausible pathway towards such products. The estimates at these temporal and spatial scales derived from microwave observations will be useful for studies of climate, agriculture, and soil moisture. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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18 pages, 4302 KiB

Open AccessArticle

The Spatiotemporal Response of Soil Moisture to Precipitation and Temperature Changes in an Arid Region, China

by Yunqian Wang, Jing Yang, Yaning Chen, Anqian Wang and Philippe De Maeyer

Remote Sens. 2018, 10(3), 468; https://doi.org/10.3390/rs10030468 - 16 Mar 2018

Cited by 57 | Viewed by 6066

Abstract

Soil moisture plays a crucial role in the hydrological cycle and climate system. The reliable estimation of soil moisture in space and time is important to monitor and even predict hydrological and meteorological disasters. Here we studied the spatiotemporal variations of soil moisture and explored the effects of precipitation and temperature on soil moisture in different land cover types within the Tarim River Basin from 2001 to 2015, based on high-spatial-resolution soil moisture data downscaled from the European Space Agency’s (ESA) Climate Change Initiative (CCI) soil moisture data. The results show that the spatial average soil moisture increased slightly from 2001 to 2015, and the soil moisture variation in summer contributed most to regional soil moisture change. For the land cover, the highest soil moisture occurred in the forest and the lowest value was found in bare land, and soil moisture showed significant increasing trends in grassland and bare land during 2001~2015. Both partial correlation analysis and multiple linear regression analysis demonstrate that in the study area precipitation had positive effects on soil moisture, while temperature had negative effects, and precipitation made greater contributions to soil moisture variations than temperature. The results of this study can be used for decision making for water management and allocation. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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25 pages, 5363 KiB

Open AccessArticle

A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability

by Carsten Montzka, Kathrina Rötzer, Heye R. Bogena, Nilda Sanchez and Harry Vereecken

Remote Sens. 2018, 10(3), 427; https://doi.org/10.3390/rs10030427 - 09 Mar 2018

Cited by 48 | Viewed by 6987

Abstract

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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19 pages, 5803 KiB

Open AccessArticle

Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002

by Matteo Zampieri, Gema Carmona Garcia, Frank Dentener, Murali Krishna Gumma, Peter Salamon, Lorenzo Seguini and Andrea Toreti

Remote Sens. 2018, 10(2), 244; https://doi.org/10.3390/rs10020244 - 06 Feb 2018

Cited by 25 | Viewed by 5062

Abstract

India is the second-most populous country and the second-most important producer of rice of the world. Most Indian rice production depends on monsoon timing and dynamics. In 2002, the lowest monsoon precipitation of the last 130+ years was observed. It coincided with the worst rice production anomaly recorded by FAOSTAT from 1961 to 2014. In that year, freshwater limitation was blamed as responsible for the yield losses in the southeastern coastal regions. Given the important implication for local food security and international market stability, we here investigate the specific mechanisms behind the effects of this extreme meteorological drought on rice yield at the national and regional levels. To this purpose, we integrate output from the hydrological model, surface, and satellite observations for the different rice cropping cycles into state-of-the-art and novel climate indicators. In particular, we adopt the standardized precipitation evapotranspiration index (SPEI) as an indicator of drought due to the local surface water balance anomalies (i.e., precipitation and evapotranspiration). We propose a new indicator of the renewable surface freshwater availability due to non-local sources, i.e., the standardized river discharge index (SDI) based on the anomalies of modelled river discharge data. We compare these indicators to the soil moisture observations retrieved from satellites. We link all diagnostics to the recorded yields at the national and regional level, quantifying the long-term correlations and the best match of the 2002 anomaly. Our findings highlight the need for integrating non-local surface freshwater dynamics with local rainfall variability to determine the soil moisture conditions in rice fields for yields assessment, modeling, and forecasting. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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16 pages, 2903 KiB

Open AccessArticle

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

by Chenyang Xu, John J. Qu, Xianjun Hao, Michael H. Cosh, John H. Prueger, Zhiliang Zhu and Laurel Gutenberg

Remote Sens. 2018, 10(2), 210; https://doi.org/10.3390/rs10020210 - 01 Feb 2018

Cited by 55 | Viewed by 7598

Abstract

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: −93.6°~−93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R² of 0.5766, and RMSE from 0.0302 to 0.1124 m³/m³. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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23 pages, 3381 KiB

Open AccessEditor’s ChoiceArticle

The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

by Robin Van der Schalie, Richard De Jeu, Robert Parinussa, Nemesio Rodríguez-Fernández, Yann Kerr, Amen Al-Yaari, Jean-Pierre Wigneron and Matthias Drusch

Remote Sens. 2018, 10(1), 107; https://doi.org/10.3390/rs10010107 - 13 Jan 2018

Cited by 23 | Viewed by 7033

Abstract

Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003–2013) from two different satellite sensors, i.e., SMOS (2010–2013) and AMSR-E (2003–2011), and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the R_value), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land–atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The R_value and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil moisture datasets based on a combination of these sources are a valuable source of information for climate research. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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13120 KiB

Open AccessArticle

Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

by Yangxiaoyue Liu, Yaping Yang, Wenlong Jing and Xiafang Yue

Remote Sens. 2018, 10(1), 31; https://doi.org/10.3390/rs10010031 - 25 Dec 2017

Cited by 58 | Viewed by 7037

Abstract

Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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12474 KiB

Open AccessArticle

Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone

by Yufang Zhang, Jianya Gong, Kun Sun, Jianmin Yin and Xiaoling Chen

Remote Sens. 2018, 10(1), 12; https://doi.org/10.3390/rs10010012 - 22 Dec 2017

Cited by 20 | Viewed by 7611

Abstract

The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. The model assumed a linear relationship that varied with both time and space between soil moisture and backscattering coefficient (SM-

σ^{0}

). Results showed that three LME models developed with different polarized

σ^{0}

images all meet the European Space Agency (ESA) accuracy requirement for GMES soil moisture product (≤5% in volume), with the vertical transmit and vertical receive (VV) polarized model achieving the best performance. However, the SM-

σ^{0}

relationship was found to depend strongly on space, making it difficult to predict absolute soil moisture for each grid. Therefore, a relative soil moisture index was then proposed to correct for site effect. When compared with those of the linear fixed effects model, the soil moisture indices predicted by the LME model captured the temporal dynamics of measured soil moisture better, with the overall R² and cross-validated R² being 0.68 and 0.64, respectively. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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10582 KiB

Open AccessArticle

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

by Jana Kolassa, Rolf H. Reichle, Qing Liu, Michael Cosh, David D. Bosch, Todd G. Caldwell, Andreas Colliander, Chandra Holifield Collins, Thomas J. Jackson, Stan J. Livingston, Mahta Moghaddam and Patrick J. Starks

Remote Sens. 2017, 9(11), 1179; https://doi.org/10.3390/rs9111179 - 17 Nov 2017

Cited by 33 | Viewed by 6514

Abstract

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m

^{3}

^{- 3}

and 0.001 m

^{3}

^{- 3}

, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m

^{3}

^{- 3}

, but increased the root zone bias by 0.014 m

^{3}

^{- 3}

. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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6762 KiB

Open AccessEditor’s ChoiceArticle

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

by Lanhui Zhang, Chansheng He and Mingmin Zhang

Remote Sens. 2017, 9(11), 1111; https://doi.org/10.3390/rs9111111 - 02 Nov 2017

Cited by 21 | Viewed by 5238

Abstract

As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature T_Bp and effective temperature T_eff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m³/m³ in mountainous area. These uncertainties also result in the “dry bias” of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m³/m³ and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient b_p result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature T_Bp and effective temperature T_eff; determine coefficient b_p and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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4862 KiB

Open AccessArticle

Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data

by Pierre Baguis and Emmanuel Roulin

Remote Sens. 2017, 9(8), 820; https://doi.org/10.3390/rs9080820 - 10 Aug 2017

Cited by 19 | Viewed by 6484

Abstract

In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF), where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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7640 KiB

Open AccessArticle

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

by Xiaojing Bai, Binbin He, Xing Li, Jiangyuan Zeng, Xin Wang, Zuoliang Wang, Yijian Zeng and Zhongbo Su

Remote Sens. 2017, 9(7), 714; https://doi.org/10.3390/rs9070714 - 12 Jul 2017

Cited by 79 | Viewed by 7727

Abstract

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m³/m³ for the ascending data, and are 0.012, 0.026, and 0.055 m³/m³ for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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13813 KiB

Open AccessArticle

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

by Zheng Lu, Linna Chai, Shaomin Liu, Huizhen Cui, Yanghua Zhang, Lingmei Jiang, Rui Jin and Ziwei Xu

Remote Sens. 2017, 9(6), 574; https://doi.org/10.3390/rs9060574 - 08 Jun 2017

Cited by 17 | Viewed by 5683

Abstract

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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