AMSR-E Soil Moisture Research Articles

Temperature Effects in AMSR2 Soil Moisture Products and Development of a Removal Method Using Data at Ascending and Descending Overpasses

Soil moisture is among the most essential variables in hydrology and earth science. Many satellite missions, such as AMSR-E/2, have been launched to observe it in broader spatial coverage to overcome the shortage of in situ observations. However, the satellite soil moisture products have been reported to comprise errors caused by the so-called “temperature effects” widely observed in dielectrically measured in situ volumetric soil water content (SWC). In this work, we confirmed the existence of these errors in AMSR2 soil moisture products. A new algorithm was developed to remove these errors using satellite data at ascending and descending overpasses. The application of this algorithm to both satellite and in situ data of SWC and soil temperature at the Mongolia site shows that the difference between SWC values at ascending and descending overpasses caused by temperature effects is effectively removed. We assess the impact of this removal method on satellite data by comparing it with in situ data, utilizing metrics such as the correlation coefficient and other widely adopted evaluation methods. It is shown that the difference between the original and corrected in situ SWC is much smaller than that between AMSR2 and in situ SWC, either corrected or not. The results indicate that the metric values between the corrected AMSR2 and in situ SWC, after removing apparent differences caused by temperature effects, slightly improved compared to those between the original AMSR2 and in situ SWC. Though these findings imply that the removed errors may not be the most dominant, considering the current significant difference between AMSR2 and in situ SWC, the removal makes the ascending and descending data have close characteristics. It may allow using data at both ascending and descending overpasses and double the temporal resolution of AMSR2 SWC data.

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.

Refinement of NOAA AMSR-2 Soil Moisture Data Product—Part 2: Development With the Optimal Machine Learning Model

Advanced Microwave Scanning Radiometer-2 (AMSR2) is a successor of AMSR for Earth-Observation System (AMSR-E), while the third generation of AMSR (AMSR3) will be launched in the near future. The AMSR2 soil moisture product is also an important component of the Soil Moisture Operational Products System (SMOPS) datasets that are operationally produced by National Oceanic and Atmospheric Administration (NOAA). Refinement of the NOAA AMSR2 soil moisture data product can not only benefit the past AMSR-E and the upcoming AMSR3, but also improve the SMOPS data quality. In this second paper of the two-part series, the Extreme Gradient Boosting (XGB) model was trained using the AMSR2 6.925 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz brightness temperature (Tb) measurements in dual polarizations, ancillary maps and the vegetation index datasets, and in turn used to predict the daily global AMSR2 soil moisture retrievals from 2012 to 2021. Validation results show that the refined AMSR2 soil moisture retrievals (AMSRr) show an overwhelming advantage in data accuracy over the currently operational AMSR2 product (AMSRc). Comapred to the AMSRc, the developed AMSRr presents a significant improvement on data availability. Results also indicate that the refined AMSR2 datasets are comparable with the latest version Soil Moisture Active Passive soil moisture product. Based on this study, higher quality AMSRr soil moisture data product will be operationally produced in the NOAA, and will eventually benefit the NOAA SMOPS blended soil moisture product and its users.

Refinement of NOAA AMSR-2 Soil Moisture Data Product: 1. Intercomparisons of the Commonly Used Machine-Learning Models

Soil Moisture is an important variable for hydrological, meteorological and agricultural studies and applications. The Soil Moisture Operational Products System (SMOPS) was developed by the National Oceanic and Atmospheric Administration (NOAA)-National Environmental Satellite, Data, and Information Service (NESDIS) to operationally provide an integrated satellite soil moisture data product. The Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture retrieval is an important component of the currently operational SMOPS. This study is proposed to refine the AMSR2 data product using an optimal machine learning model, and this first paper of the two-part series is to intercompare the six commonly-used machine learning models including multiple linear regression (MLR), Regression Tree (RRT), Random Forest (RFT), Gradient Boosting (GBR), Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN). Results indicate that all of the six approaches can preserve the reference data information beyond the training time period, which ensures them to predict past and future satellite retrievals without a new training procedure. Relative to other models, the XGB method is more successful to respect to the reference data Soil Moisture Active Passive (SMAP) and the in-situ observations from the U. S. Department of Agriculture Soil Climate Analysis Network (SCAN). It has a good implication on the implementation of the XGB model to refine the AMSR2 soil moisture retrievals in the second paper.

The Validation of Soil Moisture from Various Sources and Its Influence Factors in the Tibetan Plateau

The tempo-spatial continuous soil moisture (SM) datasets of satellite remote sensing, land surface models, and reanalysis products are very important for correlational research in the Tibetan Plateau (TP) meteorology. Based on the in situ observed SM, AMSR2, SMAP, GLDAS-Noah, and ERA5 SM are assessed at regional and site scales in the TP during the non-frozen period from 2015 to 2016. The results indicate that SMAP and ERA5 SM (AMSR2 and GLDAS-Noah SM) present an overestimation (underestimation) of the TP regional average. Specifically, SMAP (ERA5) SM performs best in Maqu and south-central TP (Naqu, Pali, and southeast TP), with a Spearman’s rank correlation (ρ) greater than 0.57 and an unbiased root mean square error (ubRMSE) less than 0.05 m3/m3. In Shiquanhe, GLDAS-Noah SM performs best among the four SM products. At the site scale, SMAP SM has relatively high ρ and low ubRMSE values at the most sites, except the sites at the Karakoram Mountains and Himalayan Mountains. The four SM products show underestimation in different degrees at Shiquanhe. The ρ values between AMSR2 SM and rainfall are the highest in most study subregions, especially in Naqu and Pali. For the other SM products, they have the highest positive correlations with a normalized difference vegetation index (NDVI). Besides, land surface temperature (LST) has significant negative (positive) correlations with SM products in the summer (other seasons). Through the multiple linear stepwise regression analysis, NDVI has negative (positive) impacts on SM products in the spring (other seasons), while LST shows the opposite conditions. NDVI (rainfall) is identified as the main influencing factor on the in situ observed, SMAP, GLDAS-Noah, and ERA5 (AMSR2) SM in this study. Compared to previous studies, these results comprehensively present the applicability of SM products in the TP and further reveal their main influencing factors.

Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data.

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

CROP SUITABILITY ANALYSIS FOR CEREAL CROPS OF UTTAR PRADESH, INDIA

Abstract. The crop suitability is the process of assessing the appropriateness or ability of a given type of land on the basis of growing conditions of a particular crop. The study focused on the crop suitability analysis of cereal crops for their production in Uttar Pradesh. Information about crop suitability is essential for proper management of agriculture in the study area. Remote sensing and GIS data provide a reliable information and technique to find suitable land for crops. The research was based on GIS based Multi-Criteria Decision Approach. The AMSR-2 (Advance Microwave Scanning Radiometer) soil moisture data, Carto-DEM, soil chemical and physical properties and climate data were used to identify the crop suitability in the study area. Weightage of different factors was arrived at based on input and feedback from experts. An Analytical Hierarchical Process (AHP) was used in ArcGIS environment to generate suitability map for the cereals crop. The suitability map has been categorised in the form of highly suitable, moderately suitable, low suitable and non-agricultural/ non-suitable region of the study area for Wheat, Rice, Sorghum, Maize and Pearl Millet/Bajra.The overall study indicates that the study area has a huge potential of cereal crop production. Therefore, improved levels of agricultural production can be achieved by cultivating crop in highly and moderately suitable areas; and practicing diversification of marginally suitable areas to crops other than that for which it is low suitable.

An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products

Discriminating between surface soil freeze/thaw states with the use of passive microwave brightness temperature has been an effective approach so far. However, soil moisture has a direct impact on the brightness temperature of passive microwave remote sensing, which may result in uncertainties in the widely used dual-index algorithm (DIA). In this study, an improved algorithm is proposed to identify the surface soil freeze/thaw states based on the original DIA in association with the AMSR-E and AMSR2 soil moisture products to avoid the impact of soil moisture on the brightness temperature derived from passive microwave remotely-sensed soil moisture products. The local variance of soil moisture (LVSM) with a 25-day interval was introduced into this algorithm as an effective indicator for selecting a threshold to update and modify the original DIA to identify surface soil freeze/thaw states. The improved algorithm was validated against in-situ observations of the Soil Moisture/Temperature Monitoring Network (SMTMN). The results suggest that the temporal and spatial variation characteristics of LVSM can significantly discriminate between surface soil freeze/thaw states. The overall discrimination accuracy of the improved algorithm was approximately 89% over a remote area near the town of Naqu on the East-Central Tibetan Plateau, which demonstrated an obvious improvement compared with the accuracy of 82% derived with the original DIA. More importantly, the correct classification rate for the modified pixels was over 96%.

Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation–temperature condition index covariates.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

Thermal Inertia Approach Using a Heat Budget Model to Estimate the Spatial Distribution of Surface Soil Moisture over a Semiarid Grassland in Central Mongolia

Abstract Thermal inertia is a physical parameter that evaluates soil thermal properties with an emphasis on the stability of the temperature when the soil is affected by heating/cooling. Thermal inertia can be retrieved from a heat budget formulation as a parameter when the time series of Earth surface temperature and forcing variables, such as insolation and air temperature, are given. In this study, a two-layer, linearized heat budget model was employed for the retrieval of thermal inertia over a grassland in a semiarid region. Application of different formulations to the aerodynamic conductance with respect to atmospheric stability significantly improved the accuracy of the thermal inertia retrieval. The retrieved values of thermal inertia were well correlated with in situ surface soil moisture at multiple ground stations. The daily time series of thermal inertia–derived soil moisture qualitatively agreed well with in situ soil moisture after antecedent rainfalls, which was found after fitting the time series to an exponentially decaying function. On the contrary, AMSR2 soil moisture mostly did not agree with in situ soil moisture. The results of the estimation showed high accuracy: the root-mean-square error was 0.038 m3 m−3 compared to the in situ data and was applied to an area of 2° × 2° in which the in situ observation locations were included. The spatiotemporal distribution of surface soil moisture was mapped at a 0.03° × 0.03° spatial resolution in the study area as 10- or 11-day averages over a vegetation growth period of 2012.

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Soil moisture is placed at the interface between land and atmosphere which influences water and energy flux. However, soil moisture information has a significant importance in hydrological modelling and environmental processes. Recent advances in acquiring soil moisture from the satellite and its effective utilization provide an alternative to the conventional soil moisture methods. In this study, an attempt is made to apply physically based, distributed-parameter, Soil and Water Assessment Tool (SWAT) to validate Advanced Microwave Scanning Radiometer (AMSR2) soil moisture in parts of Puerto Rico. For this, calibration is performed for the years 2010 to 2012 with known observed discharge sites, Rio Gunajibo and Rio Grande de Anasco in Puerto Rico and validation, with the observed stream flow for the year 2013 using the AMSR2 soil moisture. Moreover, the SWAT and AMSR2 soil moisture outcome are compared on a monthly basis. The model capability and performance in simulating the stream flow are evaluated utilizing the statistical method. The results indicated a negligible difference in SWAT soil moisture and AMSR2 soil moisture for stream flow estimation. Finally, the model retrievals show a satisfactory agreement between observed and simulated streamflow.

A comparison of SMOS and AMSR2 soil moisture using representative sites of the OzNet monitoring network

This paper evaluates the performance of different soil moisture products from AMSR2 and SMOS against the most representative stations within the Yanco study area in the Murrumbidgee catchment, in southeast Australia. AMSR2 Level 3 (L3) soil moisture products retrieved from two versions of brightness temperatures using the Japanese Aerospace eXploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithm were included. For the LPRM algorithm, two different parameterization methods were applied. Furthermore, two versions of SMOS L3 soil moisture product were assessed. The results are contrasted against the use of “random” stations. Accounting for all versions, frequencies and overpasses, the latest versions of the JAXA (JX2) and LPRM (LP3) products were found to surpass the earlier versions (JX1, LP1 and LP2). Soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used but not X-band. However, X-band retrievals (r: 0.71, MAE: 0.07, RMSD: 0.08m3/m3) were found to perform better than C-band (r: 0.68–0.70, MAE: 0.07–0.09m3/m3, RMSD: 0.09–0.10m3/m3). Moreover, an intercomparison between different acquisition times (morning and evening) of AMSR2 X-band products found a better performance from evening overpasses (1:30pm; r: 0.69–0.77) as opposed to morning overpasses (1:30am; r: 0.47–0.66). In the case of SMOS, morning (6:00am; r: 0.77) retrievals were found to be superior over evening (6:00pm; r: 0.69) retrievals. Overall, both versions of JAXA products, the second and third versions of LPRM X-band products, and two versions of SMOS products were found to meet the mean average error (MAE) goal accuracy of the AMSR2 mission (MAE < 0.08 m3/m3) but none of the products achieved the SMOS goal of RMSD < 0.04 m3/m3. Furthermore, performance of the products differed depending on the statistic used to evaluate them. Consequently, considering the results in this study, JX2 products are recommended if both absolute and temporal accuracy of the soil moisture product is of importance, whereas LP3X products from evening observations and SMOS version 3.00 (SMOS2) products from morning overpasses are recommended if temporal accuracy is of greater importance.

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they can be routinely used at a global scale. In this paper, we evaluated the Soil Moisture Active/Passive (SMAP) and the Advanced Microwave Scanning Radiometer (AMSR2) radiometer soil moisture products against in situ measurements collected from American networks with four statistics, including the mean difference (MD), the root mean squared difference (RMSD), the unbiased root mean square error (ubRMSE) and the correlation coefficient (R). The evaluation results of SMAP and AMSR2 soil moisture products were compared. Moreover, the triple collocation (TC) error model was used to assess the error among AMSR2, SMAP and in situ data. The monthly average and daily AMSR2 and SMAP soil moisture data were analyzed. Different spatial series, temporal series and combined spatial-temporal analysis were carried out. The results reveal that SMAP soil moisture retrievals are generally better than AMSR2 soil moisture data. The remotely sensed retrievals show the best agreement with in situ measurements over the central Great Plains and cultivated crops throughout the year. In particular, SMAP soil moisture data shows a stable pattern for capturing the spatial distribution of surface soil moisture. Further studies are required for better understanding the SMAP soil moisture product.

Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM) and the Soil Moisture Operational Products System (SMOPS) observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb) using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM) maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS) using the Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS) SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD) of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb) at an incidence angle of 55° using the Community Microwave Emission Model (CMEM) Radiative transfer Model (RTM). This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA) brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data

The conditional merging (CM) spatial interpolation technique was applied to obtain the composite soil moisture products using the AMSR2 and in situ soil moisture for the 51 days of the summer through the late fall season of the year 2012 in Korean Peninsula. The ‘leave one out cross-validation’ analysis was conducted to assess the performance of the composite soil moisture products in estimating the soil moisture in ungagged locations. The control variable for comparison was the soil moisture products obtained by spatially interpolating the in situ soil moisture data measured at eight gage locations using the Ordinary Kriging (KR) technique. The results show that the composite soil moisture products are more accurate than the in situ only soil moisture products in estimating the soil moisture for the following cases: (1) when the spatial correlation of in situ soil moisture data is low. Such case includes when there is little rainfall and where the altitude is high (mountainous area) and (2) where the gage density is low or the area located further away from the in situ gages. For both cases, the KR method cannot use enough information due to the low spatial correlation of the in situ measurement for interpolation, while the CM method can take advantage of the satellite soil moisture measurement not affected by the spatial correlation of the in situ data.

Validation of two gridded soil moisture products over India with in-situ observations

Surface level soil moisture from two gridded datasets over India are evaluated in this study. The first one is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation system based on Extended Kalman Filter method (EKF), which make use of satellite observation of Advanced Scatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Second dataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) using passive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations of soil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soil moisture products. The difference between these datasets over India is minimum in the non-monsoon months and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMO soil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellite soil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 and UKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in all seasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probability distribution of IMD soil moisture observation peaks at 0.25 m3/m3, NRSC at 0.15 m3/m3, AMSR-2 at 0.25 m3/m3 and UKMO at 0.35 m3/m3 during June–September period. Validation results show UKMO analysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets. The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soil moisture during monsoon season over India in NRSC data suggests the necessity of incorporating the actual vegetation for a better soil moisture retrieval using passive microwave sensors. Both products have good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf tree and urban land cover types.

中国の山西省におけるAMSR-E土壌水分の時空間変動に関する研究

中国の山西省におけるAMSR-E土壌水分の時空間変動に関する研究

An approach to validate soil moisture derived from passive microwave sensors using SAR as an interface

Validation of soil moisture derived from space-borne passive microwave sensors remains a challenging task owing to the large variability in soil moisture within the coarse-resolution cell offered by passive sensors. In this article, nine spatial/temporal synthetic aperture radar (SAR) data sets from Radar Satellite-1 (Radarsat-1) and Environmental Satellite-1 (Envisat-1) Advanced Synthetic Aperture Radar (ASAR) were used as an interface to upscale the field-level soil moisture to soil moisture at a 25 km × 25 km grid, the scale at which the Advanced Microwave Scanning Radiometer for the EOS (Earth Observing System) (AMSR-E) level-3 soil moisture product is available, leading to a total of 68 such grids where there is a pair of SAR-derived and corresponding AMSR-E-derived values. Out of the these 68 grid cells, each of size 25 km × 25 km, pair data from nine dates, data corresponding to 51 grid cell pairs (from six dates) were used for the development of the approach and data corresponding to 17 grid cell pairs (from three dates) were used for validating the approach. It is observed that while the spatial variation in SAR- and AMSR-E-derived soil moisture is broadly similar, there exists a bias in the difference between AMSR-E- and SAR-derived soil moisture. The root mean square (RMS) difference between AMSR-E soil moisture and SAR-derived soil moisture at 25 km over the model development sample of 51 grid cells, each of 25 km × 25 km, was observed to be 4.2 (%g cm–3). The study shows that the bias is significantly affected by the AMSR-E vegetation/roughness parameter, g, which is an indicator of the prevailing vegetation/surface roughness conditions. This dependence of the bias on g was exploited to arrive at modified soil moisture estimates at 25 km. The RMS difference between the modified AMSR-E soil moisture at 25 km and the SAR-derived soil moisture was observed to be 2.13 (%g cm–3) over a validation sample of 17 grid cells each of size 25 km × 25 km, comprising extreme, dry and wet, and soil moisture conditions. This demonstrative study provides the feasibility of calibrating and improving the soil moisture product from coarse-resolution space-borne passive sensors with the help of soil moisture derived using SAR. The approach is demonstrated for validating passive radiometer-derived soil moisture over bare/crop-covered conditions through an experiment involving 68 25 km × 25 km grid cells spread over nine temporal/spatial Radarsat and Envisat-1 satellite SAR data sets and synchronous ground soil moisture measurements over farmers’ fields, covering a large range of soil/crop conditions prevailing during the nine months of SAR data acquisition. Hence the results reported in this manuscript are encouraging.