scholarly journals An improved perspective in the representation of soil moisture: potential added value of SMOS disaggregated 1 km resolution product

2018 ◽  
Author(s):  
Samiro Khodayar ◽  
Amparo Coll ◽  
Ernesto Lopez-Baeza
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Temporal Distribution ◽  
Added Value ◽  
Point Scale ◽  
Spatio Temporal ◽  
Analysis System ◽  
June July ◽  
Realistic Information

Abstract. This study uses the synergy of multiresolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a Soil Vegetation Atmosphere Transfer (SVAT) model, SURFEX (Externalized Surface) – module ISBA (Interactions between Soil-Biosphere-Atmosphere), to examine, i) the comparison and suitability of different operational SMOS SM products to provide realistic information on the water content of the soil as well as the added value of the newly released SMOS Level 4 3.0 all weather disaggregated ~ 1 km SM (SMOS_L43.0), and ii) its potential impact for improving uncertainty associated to SM initialization in land surface modelling. Three different data products from SMOS-L3 (~ 25 km), L2 (~ 15 km), and disaggregated L4 3.0 (~ 1 km) are investigated. In situ SM observations over the Valencia Anchor Station (VAS; SMOS Calibration/Validation (Cal/Val) site in Europe) are used for comparison. The SURFEX-ISBA model is used to simulate point-scale surface SM (SSM) and, in combination with high-quality atmospheric information data, namely ECMWF and the SAFRAN meteorological analysis system, to obtain a representative SSM mapping over the VAS. The sensitivity to SSM initialization, particularly to realistic initialization with SMOS_L43.0 to simulate the spatial and temporal distribution of SSM is assessed. Results demonstrate: (a) all SMOS products correctly capture the temporal patterns, but, the spatial patterns are not accurately reproduced by the coarser resolutions probably in relation to the contrast with point-scale in situ measurements. (b) The potential of SMOS-L43.0 product is pointed out to adequately characterize SM spatio-temporal variability reflecting patterns consistent with intensive point scale SSM samples on a daily time scale. The restricted temporal availability of this product dictated by the revisit period of the SMOS satellite compromises the averaged SSM representation for longer periods than a day. (c) A seasonal analysis points out improved consistency during December-January-February and September-October-November in contrast to significantly worse correlations in March-April-May (in relation to the growing vegetation) and June-July-August (in relation to low SSM values

scholarly journals An improved perspective in the spatial representation of soil moisture: potential added value of SMOS disaggregated 1 km resolution “all weather” product

2019 ◽  
Vol 23 (1) ◽  
pp. 255-275 ◽  
Author(s):  
Samiro Khodayar ◽  
Amparo Coll ◽  
Ernesto Lopez-Baeza
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Temporal Distribution ◽  
Added Value ◽  
Point Scale ◽  
Spatio Temporal ◽  
Analysis System ◽  
June July ◽  
Svat Model

Abstract. This study uses the synergy of multi-resolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a soil–vegetation–atmosphere transfer (SVAT) model, SURFEX (externalized surface), module ISBA (interactions between soil, biosphere and atmosphere), to examine the benefits of the SMOS level 4 (SMOS-L4) version 3.0, or “all weather” high-resolution soil moisture disaggregated product (SMOS-L43.0; ∼1 km). The added value compared to SMOS level 3 (SMOS-L3; ∼25 km) and SMOS level 2 (SMOS-L2; ∼15 km) is investigated. In situ SM observations over the Valencia anchor station (VAS; SMOS calibration and validation – Cal/Val – site in Europe) are used for comparison. The SURFEX (ISBA) model is used to simulate point-scale surface SM (SSM) and, in combination with high-quality atmospheric information data, namely from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) meteorological analysis system, to obtain a representative SSM mapping over the VAS. The sensitivity to realistic initialization with SMOS-L43.0 is assessed to simulate the spatial and temporal distribution of SSM. Results demonstrate the following: (a) All SMOS products correctly capture the temporal patterns, but the spatial patterns are not accurately reproduced by the coarser resolutions, probably in relation to the contrast with point-scale in situ measurements. (b) The potential of the SMOS-L43.0 product is pointed out to adequately characterize SM spatio-temporal variability, reflecting patterns consistent with intensive point-scale SSM samples on a daily timescale. The restricted temporal availability of this product dictated by the revisit period of the SMOS satellite compromises the averaged SSM representation for longer periods than a day. (c) A seasonal analysis points out improved consistency during December–January–February and September–October–November, in contrast to significantly worse correlations in March–April–May (in relation to the growing vegetation) and June–July–August (in relation to low SSM values < 0.1 m3 m−3 and low spatial variability). (d) The combined use of the SURFEX (ISBA) SVAT model with the SAFRAN system, initialized with SMOS-L43.0 1 km disaggregated data, is proven to be a suitable tool for producing regional SM maps with high accuracy, which could be used as initial conditions for model simulations, flood forecasting, crop monitoring and crop development strategies, among others.

scholarly journals Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks

2018 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatio Temporal

Abstract. Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3 days repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite estimates soil moisture at two different spatial scales of 36 km and 9 km since April 2015. In this study, we develop a neural networks-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses mean monthly Normalized Differenced Vegetation Index (NDVI) as an ancillary data to quantify sub-pixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

scholarly journals Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Nitu Ojha ◽  
Olivier Merlin ◽  
Christophe Suere ◽  
Maria José Escorihuela
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arid Regions ◽  
Data Sets ◽  
Sparse Vegetation ◽  
Thermal Data ◽  
Spatio Temporal ◽  
Temporal Coverage ◽  
Semi Arid

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

scholarly journals Development of observation-based global multilayer soil moisture products for 1970 to 2016

2021 ◽  
Vol 13 (9) ◽  
pp. 4385-4405
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Layer ◽  
Added Value ◽  
Surface Model ◽  
Mean Square ◽  
Wide Range ◽  
Mean Square Errors

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals Disaggregation of SMAP Soil Moisture at 20 m Resolution: Validation and Sub-Field Scale Analysis

2021 ◽  
Vol 14 (1) ◽  
pp. 167
Author(s):  
Giovanni Paolini ◽  
Maria Jose Escorihuela ◽  
Joaquim Bellvert ◽  
Olivier Merlin
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Temporal Resolution ◽  
Land Surface ◽  
Spatial Scales ◽  
Field Scale ◽  
In Situ Data ◽  
Novel Approach ◽  
Spatio Temporal

This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson’s correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management.

Synergistic use of SMOS and Sentinel-3 for retrieving spatiotemporally estimates of surface soil moisture and evaporative fraction

2021 ◽  
Author(s):  
Maria Piles ◽  
Miriam Pablos Hernandez ◽  
Mercè Vall-llossera ◽  
Gerard Portal ◽  
Ionut Sandric ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Surface Soil ◽  
Added Value ◽  
The European Union ◽  
Surface Soil Moisture ◽  
Evaporative Fraction ◽  
In Situ Data

&lt;p&gt;Earth Observation (EO) makes it possible to obtain information on key parameters characterizing interactions among Earth&amp;#8217;s system components, such as evaporative fraction (EF) and surface soil moisture (SSM). Notably, techniques utilizing EO data of land surface temperature (Ts) and vegetation index (VI) have shown promise in this regard. The present study presents an implementation of a downscaling method that combined the soil moisture product from SMOS and the Fractional Vegetation Cover provided by Sentinel 3 ESA platform.&lt;/p&gt;&lt;p&gt;The applicability of the investigated technique is demonstrated for a period of two years (2017-2018) using in-situ data acquired from five CarboEurope sites and from all the sites available in the REMEDHUS soil moisture monitoring network, representing a variety of climatic, topographic and environmental conditions. Predicted parameters were compared against co-orbital ground measurements acquired from several European sites belonging to the CarboEurope ground observational network.&lt;/p&gt;&lt;p&gt;Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM maps predicted from the &amp;#8220;triangle&amp;#8221;&amp;#160; SSM showed a small bias,&lt;sup&gt;&lt;/sup&gt;&amp;#160;but a large scatter. The results of this study provide strong supportive evidence of the potential value of the investigated herein methodology in accurately deriving estimates of key parameters characterising land surface interactions that can meet the needs of fine-scale hydrological applications. Moreover, the applicability of the presented approach demonstrates the added value of the synergy between ESA&amp;#8217;s operational products acquired from different satellite sensors, namely in this case SMOS &amp; Sentienl-3. As it is not tight to any particular sensor can also be implemented with technologically advanced EO sensors launched recently or planned to be launched.&lt;/p&gt;&lt;p&gt;In the present work Dr Petropoulos participation has received funding from the European Union&amp;#8217;s Horizon 2020 research and innovation programme ENViSIoN under the Marie Sk&amp;#322;odowska-Curie grant agreement No 752094.&lt;/p&gt;

scholarly journals In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

2021 ◽  
Vol 13 (23) ◽  
pp. 4893
Author(s):  
Lijie Zhang ◽  
Yijian Zeng ◽  
Ruodan Zhuang ◽  
Brigitta Szabó ◽  
Salvatore Manfreda ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Land Surface Temperature ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Soil ◽  
Vegetation Indices ◽  
Surface Soil Moisture ◽  
Spatio Temporal

The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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