Surface Soil Moisture Products Research Articles

Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain)

The growing interest in low-frequency SAR for soil parameter retrieval has led to the development of new active L-band satellites, that will provide novel surface soil moisture products and retrieval possibilities; however, due to data unavailability so far, limited applications have investigated the use of change detection models using L-band satellite SAR data. Since July 2020, high revisit time, high-resolution acquisitions by the Satélite Argentino de Observación COn Microondas (SAOCOM) Argentinian-Italian constellation have become accessible over Europe. Therefore, this research presents an investigation of the potential of multi-temporal L-band SAOCOM-1 for monitoring soil moisture variations underneath low and sparse agricultural vegetation. Moreover, it proposes a procedure for the mitigation of roughness contribution, by exploiting the entropy parameter derived from the dual-polarimetric decomposition. L-band sensitivity to soil moisture has been jointly evaluated in respect of Sentinel-1 C-band data by (1) comparing the temporal profiles of the backscattering coefficient, γ0, at VV and VH polarization, with the support of decomposition parameters (entropy and ᾱ), NDVI and precipitation data; (2) regression analysis with in situ soil moisture measurements, obtained by the REMEDHUS network in the Douro River basin (Spain); 3) evaluating the soil moisture retrievals obtained at C- and L- band using a change detection method. Finally, the effectiveness of the roughness normalization procedure for SAOCOM data has been validated using in situ data. L-band co-polarized γ0 has proved to be the best configuration for soil moisture inversion, being relatively insensitive to vegetation, as demonstrated by decomposition results and trend interpretation. Overall, regressions detected an R2 22% higher at L-band than C-band, with values up to 0.74 for VV (R̄2=0.32) and up to 0.47 for the VH band (R̄2=0.14). Co-polarized data obtained R2 on average 62.1% and 74.7% higher for SAOCOM and Sentinel-1. The retrieval models show an ubRMSD of 7.1% for SAOCOM data and 8.3% for Sentinel-1. The application of the proposed roughness normalization procedure to SAOCOM led to an ubRMSD of 6.7% improving the retrieved soil moisture trend by 7.9%. This exploratory analysis demonstrated SAOCOM data potential for soil moisture mapping and would serve as a foundation for more advanced retrieval procedures.

Soil moisture mapping from SMOS: evaluating the accuracy of the operational product in a Mediterranean setting

ABSTRACT The present study provides a thorough evaluation of the SMOS surface soil moisture (SSM) product in a typical Mediterranean setting in Greece. For this purpose, a total of 4 agricultural sites were used for which co-orbital in-situ measurements from ground SSM sensors were available for year 2020. In this context, the effect of topographical and geomorphological features, land use/cover and the satellite orbit type and the Radio Frequency Interference (RFI) were also examined. A series of statistical metrics were computed, which allowed evaluating the agreement between the 2 datasets. In overall, results showed a reasonable agreement in specific land use/cover types between the SMOS product and the corresponding in-situ measurements obtained from the 0–5 cm soil moisture layer. In most cases, Root Mean Square Difference (RMSD) was close to 0.15 m3 m−3 (minimum 0.126 m3 m−3). Tomato and vineyard showed a satisfactory agreement compared to walnut and cotton crops. The autumn period had the highest agreement for tomato crop. The effect of RFI was also quite noticeable, as after the exclusion of pixels with high RFI, statistical agreement was noticeably improved. This study is, to our knowledge, one of the few that investigates in a Greek setting the accuracy of the SMOS product. The study results can contribute to the understanding of the practical value of the SMOS product in agricultural and arid/semi-arid Mediterranean environments while support efforts ongoing globally aiming at improving the SMOS SSM product accuracy.

Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe

ABSTRACT The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1’s Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution.

Retrieval and evaluation of surface soil moisture from CYGNSS using blended microwave soil moisture products

The Global Navigation Satellite System-Reflectometry (GNSS-R) can estimate land surface soil moisture (SSM) as a viable and promising approach. However, it has some large uncertainty in retrieving SSM. In this study, the SSM is retrieved from different Cyclone GNSS (CYGNSS) SSM retrieval models formed with different SSM reference data products, including two blended microwave SSM products from the European Space Agency's Climate Change Initiative (CCI) and the National Oceanic and Atmospheric Administration's Soil Moisture Operational Product System (SMOPS), and a single microwave sensor-derived Soil Moisture Active Passive (SMAP) Level-3 product. The performance of the developed retrieval models, characterized by spatial resolutions of 36 km × 36 km and 0.25° × 0.25°, is evaluated using K-fold cross-validation. Furthermore, the accuracy of these models is validated against ground measurements acquired from Chinese automated soil moisture observation network. In order to alleviate the impact of spatial mismatching between the predicted gridded SSM and the point-scale in-situ measurements, a cumulative distribution function (CDF) rescaling strategy is applied. The results indicated that all models are effective at capturing spatial variations in SSM, and the SMOPS-based model achieves the highest correlation coefficient (0.930) and the lowest root mean square error (RMSD, 0.028 cm3/cm3), followed by the CCI-based model (0.906 and 0.042 cm3/cm3). The SMAP-based model performs poorly in the comparison. The suboptimal performance of models evaluated with Chinese automated soil moisture measurements is largely attributed to the insufficient calibration of the original reference data in the region.

Soil moisture at 30 m from multiple satellite datasets fused by random forest

As a key variable used to characterize the climate process between the land surface and atmosphere, the surface soil moisture (SM) plays an irreplaceable role in the fields of hydrology, meteorology and agriculture. However, the relatively coarse spatial resolution of SM products currently limit the application in water resource management at the field scale. In this study, we proposed a high-spatial-resolution SM retrieval framework based on the random forest algorithm (RF-SM) to integrate in situ SM dataset from in the International Soil Moisture Network (ISMN), Landsat 8 optical and thermal observations, soil properties from SoilGrids V2.0, meteorological variables from the fifth generation of the European ReAnalysis (ERA5) dataset and four coarse-scale SM products including the Soil Moisture Active/Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), European Space Agency Climate Change Initiative (ESA CCI) and U.S. National Climate Assessment Land Data Assimilation System (NCA-LDAS). Compared to three other machine learning (ML) algorithms [extremely randomized trees (Extra-Trees), gradient boosting regression tree (GBRT) and extreme gradient boosting (XGBoost)], the random forest (RF) algorithm exhibited the best performance against a subset of 100 validation sites with a Kling–Gupta efficiency (KGE) of 0.69 and root-mean-square-error (RMSE) of 0.063 m3/m3. In terms of different land cover types and typical sites, RF-SM also showed a better accuracy than any of the individual SM product. Finally, the retrieval framework was applied to map the 30-m resolution SM spatial distributions in five substudy areas in the U.S. The results suggest that it is feasible to retrieve accurate SM at a 30-m spatial resolution from multiple satellite datasets based on the RF algorithm, which has important practical significance for agricultural drought monitoring at the field scale.

Assessment of 9 km SMAP soil moisture: Evidence of narrowing the gap between satellite retrievals and model-based reanalysis

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (∼10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(±0.15) for ERA5-Land vs. 0.60(±0.17) for SMAP-IB9 and 0.50(±0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(±0.18) for SMAP-IB9 vs. 0.66(±0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(±0.02) m3/m3 for both SMAP-E and ERA5-Land) and R (0.59(±0.16) for SMAP-IB9 vs. 0.61(±0.15) for ERA5-Land), respectively.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product

This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.

Evaluation of GLDAS soil moisture seasonality in arid climates

ABSTRACT We evaluated the Global Land Data Assimilation System surface soil moisture product (GLDAS v. 2.1) against in situ soil moisture networks in arid climates in Australia and the United States, using common statistical metrics and seasonality metrics. Our results showed that GLDAS performed well (root mean square error (RMSE) = 0.100 m3/m3; unbiased RMSE (ubRMSE) = 0.060 m3/m3; correlation coefficient (R) = 0.555 on average) but systematically overestimated the soil moisture values (Bias = 0.067 m3/m3). The performance was better in Australian Oznet and the U.S. Climate Reference Network (USCRN), compared to the US Soil Climate Analysis Network (SCAN) network. In terms of seasonality, GLDAS soil moisture seasons were biased to start earlier; on average, drying and wetting transitions started 28 and 16 days earlier than in situ data, respectively. The end dates of GLDAS seasonal transitions showed good agreement with in situ data; the errors in transition timings were limited to within a week. This tendency is stronger in the US networks compared to the Australian network.

Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3/m3, and Bias between −0.012 and 0.008 m3/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3/m3), ubRMSE (0.045 m3/m3) and Bias (0.031 m3/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.

Soil moisture estimates at 1 km resolution making a synergistic use of Sentinel data

Abstract. Very high-resolution (∼10–100 m) surface soil moisture (SM) observations are important for applications in agriculture, among other purposes. This is the original goal of the S2MP (Sentinel-1/Sentinel-2-Derived Soil Moisture Product) algorithm, which was designed to retrieve surface SM at the agricultural plot scale by simultaneously using Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including hydrology and climate impact assessment at regional level, large maps with a high resolution (HR) of around 1 km are already a significant improvement with respect to most of the publicly available SM datasets, which have resolutions of about 25 km. In this study, the S2MP algorithm was adapted to work at 1 km resolution and extended from croplands to herbaceous vegetation types. A target resolution of 1 km also allows the evaluation of the interest in using NDVI derived from Sentinel-3 (S3) instead of S2. Two sets of SM maps at 1 km resolution were produced with S2MP over six regions of ∼104 km2 in Spain, Tunisia, North America, Australia, and the southwest and southeast regions of France for the whole year of 2019. The first set was derived from the combination of S1 and S2 data (S1 + S2 maps), while the second one was derived from the combination of S1 and S3 (S1 + S3 maps). S1 + S2 and S1 + S3 SM maps were compared to each other, to those of the 1 km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) datasets, and to those of the Soil Moisture Active Passive (SMAP) + S1 product. The S2MP S1 + S2 and S1 + S3 SM maps are in very good agreement in terms of correlation (R≥0.9), bias (≤0.04 m3 m−3), and standard deviation of the difference (SDD≤0.03 m3 m−3) over the six domains investigated in this study. In a second step, the S1 + S3 S2MP maps were compared to the other HR maps. S1 + S3 SM maps are well correlated to the CGLS SM maps (R∼0.7–0.8), but the correlations with respect to the other HR maps (CGLS SWI and SMAP + S1) drop significantly over many areas of the six domains investigated in this study. The highest correlations between the HR maps were found over croplands and when the 1 km pixels have a very homogeneous land cover. The bias among the different maps was found to be significant over some areas of the six domains, reaching values of ±0.1 m3 m−3. The S1 + S3 maps show a lower SDD with respect to CGLS maps (≤0.06 m3 m−3) than with respect to the SMAP + S1 maps (≤0.1 m3 m−3) for all the six domains. Finally, all the HR datasets (S1 + S2, S1 + S3, CGLS, and SMAP + S1) were also compared to in situ measurements from five networks across five countries, along with coarse-resolution (CR) SM products from SMAP, SMOS, and the European Space Agency Climate Change Initiative (CCI). While all the CR and HR products show different bias and SDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. The discrepancies in between the different HR datasets, except for the more simple land cover conditions (homogeneous pixels with croplands) and the lower performances with respect to in situ measurement than coarse-resolution datasets, show the remaining challenges for large-scale HR SM mapping.

A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution

Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m3/m3) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.

A global daily soil moisture dataset derived from Chinese FengYun Microwave Radiation Imager (MWRI)(2010–2019)

Surface soil moisture (SSM) is an important variable in drought monitoring, floods predicting, weather forecasting, etc. and plays a critical role in water and heat exchanges between land and atmosphere. SSM products from L-band observations, such as the Soil Moisture Active Passive (SMAP) Mission, have proven to be optimal global estimations. Although X-band has a lower sensitivity to soil moisture than that of L-band, Chinese FengYun-3 series satellites (FY-3A/B/C/D) have provided sustainable and daily multiple SSM products from X-band since 2008. This research developed a new global SSM product (NNsm-FY) from FY-3B MWRI from 2010 to 2019, transferred high accuracy of SMAP L-band to FY-3B X-band. The NNsm-FY shows good agreement with in-situ observations and SMAP product and has a higher accuracy than that of official FY-3B product. With this new dataset, Chinese FY-3 satellites may play a larger role and provide opportunities of sustainable and longer-term soil moisture data record for hydrological study.

Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases.

Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions

Since the launch of Soil Moisture Active Passive (SMAP) mission in 2015, several advanced global surface soil moisture (SM) products derived from different retrieval schemes have been released. However, the factors contributing to the performance differences of these SMAP products have not been fully investigated. This study tries to fill the gap by assessing and intercomparing five SMAP products, namely, the single channel algorithm V/H-pol (SCA-V/H), dual channel algorithm (DCA), multi-temporal dual channel algorithm (MT-DCA) and the new SMAP-INRAE-BORDEAUX (SMAP-IB), using the International Soil Moisture Network (ISMN) ground measurements worldwide during 2016–2020. After reviewing the retrieval algorithms, the impact of five potential perturbing factors on the skills of all products is reported; these factors including mean surface soil temperature (MSST), surface roughness (log(SR)), vegetation density (characterized by vegetation optical depth (VOD)), mean surface soil wetness (MSSM) and soil organic matter (SOM). Results shows that the SMAP-IB product has the highest overall R value of 0.75 with ISMN ground SM, and the smallest unbiased root mean square difference (ubRMSD) values same as SCA-V and DCA (∼0.058 m3/m3). The performance of all five products in terms of R and ubRMSD rapidly degrades over the regions with high VOD, rough surface and low MSST. In contradiction with the fact that product performance degrades with increasing roughness, the ubRMSD of all five products increases with decreasing log(SR) on a relatively smooth surface (log(SR) < 6) with larger growth rates for SMAP-IB, SCA-H and MT-DCA. Regarding characterizing the temporal variations of in situ SM anomalies (Ranom), Ranom increases as the ground becomes wet for all products within a certain range (MSSM < 0.3 m3/m3). In particular, SMAP-IB and MT-DCA present higher Ranom values than the other three products under wet soil surfaces (MSSM > 0.3 m3/m3). The two metrics R and Ranom decrease significantly with increasing SOM for SMAP-IB compared to the other products, and SMAP-IB obtains the lowest values for both metrics when SOM > 20%. These findings are expected to provide useful information for appropriate product selection and improvement.

Feasibility of satellite-based rainfall and soil moisture data in determining the triggering conditions of debris flow: The Jiangjia Gully (China) case study

Heavy precipitation is the main trigger for debris-flow hazards. The antecedent moisture condition, which is usually represented by antecedent precipitation, is another hydrometeorological contributing factor to debris flows that originate from shallow landslides. Satellite techniques are an economical and effective way to access rainfall and soil wetness information to determine the triggering conditions of debris flow. However, satellite-based thresholds need to be compared with ground-based thresholds and adjusted to the data source prior to their application. In this study, rainfall intensity-duration (I-D) thresholds were derived from the early and final run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG-E and IMERG-F) and gauge measurements using logistic regression for debris flows in the Jiangjia Gully, Yunnan Province, southern China, which has a dense rain gauge network. Data from both the IMERG-E and IMERG-F covered the entire study period. Our evaluation revealed that the I-D threshold derived from the IMERG-E deviated from the gauge-based thresholds. Although the threshold determined by the IMERG-F was comparable to the ground-based ones, the presence of substantial false positives and false negatives indicated that its performance was weaker than that of the gauge-based ones. Furthermore, the IMERG-F was suitable if the nearest available gauge was farther than 10 km from the debris-flow initiation zone. We evaluated the feasibility of the surface soil moisture product provided by the Climate Change Initiative program of the European Space Agency (CCI-SM), root-zone soil moisture derived from the CCI-SM using an exponential filter (SM-RZ), and antecedent precipitation for improving the performance of the thresholds by separately using them as the third explanatory variable in addition to rainfall intensity and duration in logistic regression. Satellite soil moisture data were available for 54% of the study period. The results suggested that including antecedent precipitation effectively improved the performance of the thresholds. In contrast, the performance of the thresholds increased only slightly when the CCI-SM or SM-RZ was included. Although these findings are valid only for this study area and need to be assessed for other regions, they present new insights for using satellite rainfall and soil moisture estimates to define thresholds for debris flow.

Assessment of High-Resolution Surface Soil Moisture Products over the Qinghai–Tibet Plateau for 2009–2017

The surface soil moisture over the Qinghai–Tibet Plateau (QTP) has an important impact on the weather and climate of East Asia. Under climate warming, the imbalance of solid–liquid water of QTP has become a research hotspot, but the surface soil moisture dynamics over QTP are not clear owing to the lack of precise measurements over a large scale. In this paper, the quality of gridded surface soil moisture products including CSSPv2 high-resolution (6 km) simulation, ESA CCI satellite retrieval, ERA5 land-atmosphere coupled reanalysis, and GLDAS2.1 land reanalysis products (Noah, Catchment, VIC) is analyzed over QTP by comparison with the in situ measurements at 140 stations during 2009–2017. We find that the CSSPv2 product shows a higher correlation than the global satellite and reanalysis products, with correlation increased by 7.7%–115.6%. The root mean squared error of the CSSPv2 product is lower than that of other products, with the error decreased by 13.4%–46.3%. The triple collocation analysis using high-resolution simulation, global reanalysis, and satellite retrieval products over the entire plateau shows that the error of CSSPv2 is the lowest, followed by ESA CCI, while ERA5 is the highest. The soil moisture products of ESA CCI, ERA5, and CSSPv2 all show an increasing trend from April to September of 2009 to 2017, with wetting in the west and drying in the east. This study indicates that the CSSPv2 high-resolution surface soil moisture product has better performance over QTP than other global products, and the global satellite and reanalysis products may overestimate the surface soil moisture dynamics.

Development of High-Resolution Soil Hydraulic Parameters with Use of Earth Observations for Enhancing Root Zone Soil Moisture Product

Regional quantification of energy and water balance fluxes depends inevitably on the estimation of surface and rootzone soil moisture. The simulation of soil moisture depends on the soil retention characteristics, which are difficult to estimate at a regional scale. Thus, the present study proposes a new method to estimate high-resolution Soil Hydraulic Parameters (SHPs) which in turn help to provide high-resolution (spatial and temporal) rootzone soil moisture (RZSM) products. The study is divided into three phases—(I) involves the estimation of finer surface soil moisture (1 km) from the coarse resolution satellite soil moisture. The algorithm utilizes MODIS 1 km Land Surface Temperature (LST) and 1 km Normalized difference vegetation Index (NDVI) for downscaling 25 km C-band derived soil moisture from AMSR-2 to 1 km surface soil moisture product. At one of the test sites, soil moisture is continuously monitored at 5, 20, and 50 cm depth, while at 44 test sites data were collected randomly for validation. The temporal and spatial correlation for the downscaled product was 70% and 83%, respectively. (II) In the second phase, downscaled soil moisture product is utilized to inversely estimate the SHPs for the van Genuchten model (1980) at 1 km resolution. The numerical experiments were conducted to understand the impact of homogeneous SHPs as compared to the three-layered parameterization of the soil profile. It was seen that the SHPs estimated using the downscaled soil moisture (I-d experiment) performed with similar efficiency as compared to SHPs estimated from the in-situ soil moisture data (I-b experiment) in simulating the soil moisture. The normalized root mean square error (nRMSE) for the two treatments was 0.37 and 0.34, respectively. It was also noted that nRMSE for the treatment with the utilization of default SHPs (I-a) and AMSR-2 soil moisture (I-c) were found to be 0.50 and 0.43, respectively. (III) Finally, the derived SHPs were used to simulate both surface soil moisture and RZSM. The final product, RZSM which is the daily 1 km product also showed a nearly 80% correlation at the test site. The estimated SHPs are seen to improve the mean NSE from 0.10 (I-a experiment) to 0.50 (I-d experiment) for the surface soil moisture simulation. The mean nRMSE for the same was found to improve from 0.50 to 0.31.

Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index

ABSTRACT This study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local characteristics of soil moisture development. An advanced antecedent precipitation index (API) is utilized to generate a global soil moisture product of high temporal resolution with the Global Precipitation Measurement (GPM) Missions Integrated Multi-Satellite Retrievals for GPM (IMERG) as main driver. The resulting global GPM API data set is compared against in situ measurements from the International Soil Moisture Network (ISMN) and is also evaluated against the soil moisture data set from the European Space Agency’s Climate Change Initiative (ESA CCI SM). The study shows that with empirically derived dampening factors the GPM API achieves a mean ubRMSD across the utilized in situ stations in different climates and vegetation zones of 4.68 and a bias of 0.88 . The data set clearly represents the local soil moisture schemes with seasonal variations. When comparing with ESA CCI SM, the GPM API does perform better at the measurement sites concerning bias, correlation and error values. The data set is in most parts negatively biased compared to the ESA CCI SM, however better matches the mean soil moisture at ISMN stations. Overall, the GPM API delivers a very promising global, hourly surface soil moisture product at 0.1 0.1 spatial resolution.

A method for estimating surface soil moisture from diurnal land surface temperature observations over vegetated regions: A preliminary result over an AmeriFlux site and the REMEDHUS network

Most current optical/thermal-based surface soil moisture (SSM) retrieval methods only utilize the instantaneous measurements made by onboard polar-orbit satellites, and they require soil texture as prior knowledge to obtain the volumetric SSM content. A recent study developed a diurnal land surface temperature (LST)-based SSM retrieval method to directly estimate the volumetric SSM content without the knowledge of soil texture. However, most subsequent studies have only examined the use of this method on bare soils. Therefore, the present study aims to investigate the feasibility of the method over vegetated regions with both experimental data and satellite observations. First, the impacts of vegetation on SSM retrieval were investigated through physics-based Common Land Model simulations and high accuracy with an overall root mean square error (RMSE) of 0.010 m3m−3 was achieved when the underlying surface was covered by moderate fractional vegetation cover from 0 to 0.6 with a given soil texture. Based on the simulated results, the SSM was then estimated using data from an AmeriFlux site dominated by crops during the growing season in 2018; the results were further verified using in-situ measurements, and this showed that the estimated SSM correlated well with in-situ measurements (with an RMSE of 0.078 m3m−3). A further evaluation of the Meteosat Second Generation (MSG)-derived SSM at the pixel scale was made using the proposed method, and the results revealed an overall acceptable accuracy (RMSE of 0.074 m3m−3 and bias of 0.071 m3m−3) against the SMAP/Sentinel-1 SSM product at the same spatial resolution of 3 km.

An Examination of the SMAP Operational Soil Moisture Products Accuracy at the Tibetan Plateau

Surface soil moisture (SSM) plays an essential role in the Earth’s water cycle and land surface processes as well as in vegetative growth, ecological health, and ecosystem properties. Particularly, information on this parameter’s spatiotemporal variability at the Tibetan Plateau is of key importance to the study of climate and the impact of climate change due to it is distinctive characteristics in this area. The present study assesses the operational SSM products provided by the SMAP (Soil Moisture Active and Passive) satellite at the Tibetan Plateau, Naqu observational station, China. In particular, the globally distributed Level 3 operational products, SPL3SMP_36km and the Enhanced Passive SSM Product SPL3SMP_9km, are evaluated in two-phases. SSM and the surface temperature estimates by SPL3SMP_36km and SPL3SMP_9km are compared against corresponding ground data available at the Naqu observation network. All in all, the examined products captured the SSM dynamics in the studied area. The results showed that precipitation is the key driving source of SSM variability. SSM fluctuated significantly and was dependent on precipitation in the studied region. Statistical metrics, such as the root mean square error (RMSE), varied for SPL3SMP_36km and SPL3SMP_9km in the ranges of 0.036–0.083 m3/m3 and 0.074–0.097 m3/m3, respectively. The unbiased RMSE (ubRMSE) was higher than the SMAP uncertainty limit (0.04 m3/m3) in most cases. This study establishes some of the causes for the different performances of SMAP products, mainly, the ancillary input dataset parameterizations, and, specifically, the surface temperature parameterization schemes of SMAP retrieval algorithm is analyzed and discussed. Our research findings highlight, among others, the usefulness of those SSM products from SMAP, particularly in mesoscale studies, providing additional useful insights into the use of those products in practice in China and globally.