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 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 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 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.

Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions

2022 ◽  
Vol 260 ◽  
pp. 107290
Author(s):  
Abdelhakim Amazirh ◽  
Salah Er-Raki ◽  
Nitu Ojha ◽  
El houssaine Bouras ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Arid Regions ◽  
Semi Arid

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

2015 ◽  
Vol 36 (19-20) ◽  
pp. 5015-5030 ◽  
Author(s):  
Dianjun Zhang ◽  
Zhao-Liang Li ◽  
Ronglin Tang ◽  
Bo-Hui Tang ◽  
Hua Wu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Regions ◽  
In Situ Measurements ◽  
Soil Moisture Model ◽  
Semi Arid

scholarly journals A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7406
Author(s):  
Nitu Ojha ◽  
Olivier Merlin ◽  
Abdelhakim Amazirh ◽  
Nadia Ouaadi ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Radar Data ◽  
Passive Microwave ◽  
Field Scale ◽  
Data Set ◽  
Thermal Data ◽  
Spatio Temporal

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

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 Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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.

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