Satellite-based Soil Moisture Data Research Articles

Evaluation of the fully coupled WRF and WRF-Hydro modelling system initiated with satellite-based soil moisture data

ABSTRACT This study comparatively evaluates the skill of the (un)coupled Weather Research and Forecasting (WRF) – WRF-Hydro system supported with satellite-based soil moisture initiation in long-term simulations of hydrometeorological variables under humid and semi-arid climate conditions. Prior to the coupling, the WRF-Hydro model is calibrated using streamflow data at different data scales (i.e. hourly and daily) to discuss the influences of the changing temporal resolution of the reference dataset on the optimum parameter values. According to the results, fully coupled models correct the location and magnitude of the occurrence of hourly extreme precipitation. Changing the initial soil moisture data source affects the terrestrial water cycle elements in the semi-arid region (i.e. Mediterranean (MED)) more than in the humid area (i.e. Eastern Black Sea (EBS)), especially in spring and summer. The initiation of the coupled model with satellite-based soil moisture improves the streamflow outputs after a reasonable spin-up period for the MED region.

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

AbstractTemporal gaps in satellite‐based soil moisture (SM) products are a persistent issue. This study presents an entirely observation‐based method to derive volumetric SM content for filling gaps in Soil Moisture Active Passive (SMAP) retrievals. Using a water balance equation, 12‐hr topsoil water amount variations are determined based on observed precipitation from the Global Precipitation Measurement Mission (inflow) and a hydrologic loss function (outflow) built on SMAP dry‐downs. A temporally seamless SM product, composed of SMAP dry‐downs and precipitation‐driven moisture approximations, was generated as a secondary outcome in determining optimal water balance parameters. This data set maintains the original SMAP SM dynamics with a median Pearson correlation (R) of 0.69 and an unbiased root‐mean‐square error (ubRMSE) of 0.05 m3/m3. Using these parameters and available SMAP observations, a 12‐hourly SM product was produced over the conterminous United States. Validated against in situ measurements, this 12‐hourly SM product exhibits good performance with a median R of 0.63 and captures most SM peaks induced by heavy rainfall. A time series examination revealed the produced 12‐hourly SM product closely corresponds to in situ SM variations and outperforms two other SMAP‐based 12‐hourly SM products gap‐filled using temporal linear interpolation and a three‐dimensional smoothing approach, especially during sparse SMAP data periods. The proposed scheme's validity is further verified by the comparable performance of the exclusive filled‐on SM estimates. Utilizing the 12‐hourly SM data set and its paired hydrologic losses could enhance the quantification connections among the hydrologic components and benefit the understanding of land‐surface hydrology.

Soil moisture-based global liquefaction model (SMGLM) using soil moisture active passive (SMAP) satellite data

The role of soil saturation condition on the liquefaction occurrence highlights the need for a tool to track the ground-truth soil moisture content involved in this seismic phenomenon. Soil Moisture Active Passive (SMAP) satellite estimates near-real time surface and root zone soil moisture measurements with global coverage. Typical proxies for soil saturation in liquefaction analysis include average water table depth patterns, mean annual precipitation measurements, and topographic conditions. As an alternative to these proxies, this paper incorporates satellite-based soil moisture data to enhance the understanding of the interrelation between saturation conditions and liquefaction events. Proposing a new approach for sampling non-liquefaction cases, a liquefaction/non-liquefaction database was developed in this paper based on reconnaissance reports of eleven target earthquakes. Well-known geospatial explanatory variables as well as new SMAP-based soil moisture parameters, affecting soil liquefaction, are used to develop a new soil moisture-based global liquefaction model (SMGLM), which is compared with an existing global liquefaction model. Considering the ongoing advancement of earth observing satellites, the results of this paper can build a basis for developing fully satellite-based models that could identify liquefied sites using high resolution near-real time soil moisture data.

True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis

Quantifying the accuracy of the satellite-based soil moisture (SM) data is important for a number of key applications, such as: combining satellite-based SM products for long-term SM analyses, assimilating SM data into land surface models, and providing quality flags to mask bad quality SM data.A range of statistical methods have been proposed to estimate error statistics for large-scale SM datasets including the: instrumental variable (IV) method, triple collocation analysis (TCA), and quadruple collocation analysis (QCDA). While requiring only two input products, the IV method also imposes an additional assumption that one input product possesses serially uncorrelated errors - thus limiting its scope compared to TC. Likewise, QCDA requires four independent SM data products that are difficult to obtain and may not always be available for analysis. Nonetheless, TCA-based methods still cannot provide truly global error maps for satellite SM products due to the limited number of independent SM products and difficulties with baseline TCA assumptions. Moreover, temporal sampling requirements for TCA are often impractical because of low SM retrieval skill in forested and arid areas – as well as in regions prone to radio frequency interference.Here, we seek to fill significant spatial gaps in TCA results using machine learning (ML) and therefore provide spatially complete error maps for the satellite-based SM data products derived from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) systems. Furthermore, we use SHapley Additive exPlanations (SHAP) values, a model-agnostic technique for interpreting ML models, to examine the impact of various environmental conditions on the quality of satellite-based SM retrievals.Globally, and across all three products, 72.0% of missing error information in a TCA-based analysis, due to either the lack of valid data or the inability of TCA to provide reliable results, can be reconstructed from the ensemble prediction mean of the ML models. Overall, we found that 22.7% (a.m.) and 34.2% (p.m.) of the Earth'sSM dynamics (between 60°S to 60°N) have not been investigated properly across all three satellite missions.

Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images

Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m3/m3 and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.

Trend analysis and variability of satellite-based soil moisture data for the Lower Bhavani basin, Tamil Nadu using Google Earth Engine

Soil moisture is a significant hydrological component that is dynamic in nature. The variation in soil moisture in the basin scale would affect the vegetation, ecology and environment. Soil moisture trend analysis aids in providing the variation of soil moisture over the basin. The present study aimed to analyse the soil moisture trend in Lower Bhavani basin, Tamil Nadu from 2003-2022. Satellite-based soil moisture Global Land Data Assimilation System (GLDAS) data was extracted from the Google Earth Engine (GEE) platform to analyse the variation and trend over the period of time. The highest and lowest soil moisture was observed during monsoon and summer months and its percentage variation was studied. Using Man-Kendall test and Sen’s slope, trend analysis was calculated for two decades (2003-2012 and 2013-2022). In 2003-2012, an increasing trend of soil moisture was observed during winter (October to February); from 2013-2022, an increasing trend was observed during both winter (October to February) and monsoon seasons (June to September). The remaining season did not follow any trend, and there was no decreasing trend in soil moisture. The trend analysis of the study will help to monitor and manage the environmental system across the Lower Bhavani basin.

Reconstructing long-term global satellite-based soil moisture data using deep learning method

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.

Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data

Flood monitoring in the Chaohe River Basin is crucial for the timely and accurate forecasting of flood flow. Hydrological models used for the simulation of hydrological processes are affected by soil moisture (SM) data and uncertain model parameters. Hence, in this study, measured satellite-based SM data obtained from different spatial scales were merged, and the model state and parameters were updated in real time via the data assimilation method named ensemble Kalman filter. Four different assimilation settings were used for the forecasting of different floods at three hydrological stations in the Chaohe River Basin: flood forecasting without data assimilation (NA case), assimilation of runoff data (AF case), assimilation of runoff and satellite-based soil moisture data (AFWR case), and assimilation of runoff and merged soil moisture data (AFWM case). Compared with NA, the relative error (RE) of small, medium, and large floods decreased from 0.53 to 0.23, 0.35 to 0.16, and 0.34 to 0.12 in the AF case, respectively, indicating that the runoff prediction was significantly improved by the assimilation of runoff data. In the AFWR and AFWM cases, the REs of the small, medium, and large floods also decreased, indicating that the soil moisture data play important roles in the assimilation of medium and small floods. To study the factors affecting the assimilation, the changes in the parameter mean and variance and the number of set samples were analyzed. Our results have important implications for the prediction of different levels of floods and related assimilation processes.

Estimating rainfall depth from satellite-based soil moisture data: A new algorithm by integrating SM2RAIN and the analytical net water flux models

Sparse distribution of rain gauge networks challenges the estimation of rainfall variability over space and time. The SM2RAIN algorithm was developed to estimate rainfall from the knowledge of soil moisture (SM) by inverting the soil‐water balance equation. The algorithm was simplified by neglecting the contribution of evapotranspiration and surface runoff rate during the rainfall event. A recent study developed an analytical model to estimate the net water flux (NWF) from SM data via inversion of analytical Warrick’s equation. In this study, the SM2RAIN-NWF algorithm was developed by integrating the SM2RAIN algorithm and the NWF model to improve the accuracy of rainfall estimation. The applicability of the SM2RAIN-NWF algorithm was evaluated based on observed rainfall data in the Lake Urmia basin, Iran. Satellite SM data was obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The algorithm calibrated based on the data from July 3, 2012, to December 31, 2017, was then used to estimate rainfall for two years extending from January 2018 to December 2019. Estimated rainfall through SM2RAIN-NWF algorithm improved compared to SM2RAIN by 14% and 37.4% increase in the average values of correlation coefficient (R) and Nash–Sutcliffe (NS), and 11.5% decrease in the Percentage Root Mean Square Error (PRMSE) over the calibration period. Validating the estimated rainfall showed a considerable improvement in the performance of the SM2RAIN-NWF algorithm compared to the SM2RAIN algorithm by 8.6% and 30.4% increase in the average values of R and NS, and 13.4% decrease in the PRMSE. It was also found that the SM2RAIN-NWF algorithm contributes to the improvement of error indices in rainfall estimation and simulates the rainfall variation trend in a better fashion than the SM2RAIN algorithm.

Extraction of Irrigation Signals by Using SMAP Soil Moisture Data

To allow extraction of irrigation signals from satellite-derived data on soil moisture, this study describes the development of an irrigation signal extraction method that takes into account multiple environmental factors in irrigation. Firstly, the fuzzy membership functions of irrigation relating to multiple environmental factors are constructed. Then, a model is built based on the fuzzy membership functions by using operation rules of fuzzy sets, which is used to infer the relevant degree of irrigation to nonirrigation. Finally, the irrigation signals in satellite-based soil moisture data are recognized according to the relevant degree. Taking Henan Province in the North China Plain as the study area, the proposed method is used to extract irrigation signals from the SMAP Level 3 Passive Soil Moisture Product. Extracted irrigation signals from two SMAP grids are validated using daily in situ soil moisture and precipitation data, with the results showing correct identification of most of the irrigation signals. By grading the membership degree of the extracted irrigation signals, irrigation frequency maps for the 2016–2017 winter crop growth season and the 2017 summer crop growth season are obtained for Henan Province. Compared to the irrigation frequency maps with data on the annual precipitation and the annual potential evapotranspiration, the irrigation frequency maps show a spatial pattern opposite that of the annual precipitation and a spatial pattern similar to that of the annual potential evapotranspiration. It is common sense that areas with low precipitation and high evapotranspiration need more irrigation frequency and irrigation water. Thus, the spatial patterns of irrigation frequency maps are reasonable in a sense. However, it should be noted that the observed irrigation data used in the qualitative assessments are rendered less convincing by the SMAP product’s coarse resolution. Quantitative validation of extracted irrigation signals remains a significant challenge, and small-scale irrigation cannot be captured by coarse-resolution satellite-based soil moisture products. Thus, a high-resolution soil moisture product should be used to extract irrigation signals in future.

A novel framework to determine the usefulness of satellite-based soil moisture data in streamflow prediction using dynamic Budyko model

Soil moisture plays an important role in partitioning rainfall into runoff and evapotranspiration. Due to advancements in remotely sensed soil moisture data acquisition techniques, many soil moisture data assimilation (SMDA) studies have been conducted to improve streamflow prediction. It is thus expected that the outcome of a SMDA exercise will be determined by the quality of soil moisture data in hand and the hydrology of the catchment. Our study begins with the hypothesis that it is possible to determine the usefulness of a satellite-based soil moisture data product for areas where paramaterizing a complex model is difficult due to availability of limited information. To this end, we use satellite-based GLDAS root-zone soil moisture data with dynamic Budyko (DB), rainfall-runoff model, to improve streamflow prediction for 60 US basins. Our results suggest that there is a reasonably good one-to-one or universal relationship between instantaneous dryness-index (φ), the key state variable of the DB model, and root-zone soil moisture (θ), which implies that the model can directly use soil moisture information for predicting streamflow. To check the robustness of the universal φ-θ relationship, we also developed basin specific φ-θ relationships. Model performance, expressed in terms of Nash-Sutcliffe efficiency (NSE), improved in 34 basins when we considered the universal φ-θ relationship and in 51 basins when we considered the basin specific relationships. Multiple linear regression (MLR) analysis reveals that change in NSE due to the use of soil moisture information is predicted quite well by certain basin characteristics. In particular, it is found that available water capacity and forest area positively influence NSE, whereas average sand, silt and clay contents, latitude, and longitude affect it negatively. A slightly stronger MLR relationship was observed while considering soil moisture deficit (the difference between saturated soil moisture and actual soil moisture) in place of soil moisture. From the stepwise MLR analysis, it is observed that the vegetation and runoff generation mechanism control the parameters of the φ-θ relationship. Overall, our study provides a framework to determine the suitability of remotely-based soil moisture data for hydrological modelling.

Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?

Abstract The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales.

Satellite Imagery-Based SERVES Soil Moisture for the Analysis of Soil Moisture Initialization Input Scale Effects on Physics-Based Distributed Watershed Hydrologic Modelling

Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture and parameter value identification are critical for a physics-based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide accurate estimates of soil moisture, but such techniques are limited due to the number of physical sensors required to cover a large area effectively. Available satellite-based digital soil moisture data ranges from 9 km to 20 km in resolution which obscures the soil moisture details of a hill slope scale. This resolution limitation of available satellite-based distributed soil moisture data has impacted critical analysis of soil moisture resolution variance on physics-based distributed simulation results. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column and that would inform little about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed effective root zone soil moisture at 30 m resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with the GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 m resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 m resolution SERVES soil moisture data was resampled to 4500 m and 9000 m resolutions and was separately employed in the calibrated hydrological model to determine the soil moisture resolution effect on the model simulated outputs and the model parameter values. It was found that the simulated discharge is underestimated, infiltration rate/volume is overestimated and higher soil moisture state distribution is filtered out as the initial soil moisture resolution was coarsened. To compensate for this disparity in the simulated results, the soil saturated hydraulic conductivity value decreased with respect to the decreased resolutions.

Evaluation of soil moisture from CCAM-CABLE simulation, satellite-based models estimates and satellite observations: a case study of Skukuza and Malopeni flux towers

Abstract. Reliable estimates of daily, monthly and seasonal soil moisture are useful in a variety of disciplines. The availability of continuous in situ soil moisture observations in southern Africa barely exists; hence, process-based simulation model outputs are a valuable source of climate information, needed for guiding farming practices and policy interventions at various spatio-temporal scales. The aim of this study is to evaluate soil moisture outputs from simulated and satellite-based soil moisture products, and to compare modelled soil moisture across different landscapes. The simulation model consists of a global circulation model known as the conformal-cubic atmospheric model (CCAM), coupled with the CSIRO Atmosphere Biosphere Land Exchange model (CABLE). The satellite-based soil moisture data products include satellite observations from the European Space Agency (ESA) and satellite-observation-based model estimates from the Global Land Evaporation Amsterdam Model (GLEAM). The evaluation is done for both the surface (0–10 cm) and root zone (10–100 cm) using in situ soil moisture measurements collected from two study sites. The results indicate that both the simulation- and satellite-derived models produce outputs that are higher in magnitude range compared to in situ soil moisture observations at the two study sites, especially at the surface. The correlation coefficient ranges from 0.7 to 0.8 (at the root zone) and 0.7 to 0.9 (at the surface), suggesting that models mostly are in an acceptable phase agreement at the surface than at the root zone, and this was further confirmed by the root mean squared error and the standard deviation values. The models mostly show a bias towards overestimation of the observed soil moisture at both the surface and root zone, with the CCAM-CABLE showing the least bias. An analysis evaluating phase agreement using the cross-wavelet analysis has shown that, despite the models' outputs being in phase with the in situ observations, there are time lags in some instances. An analysis of soil moisture mutual information (MI) between CCAM-CABLE and the GLEAM models has successfully revealed that both the simulation and model estimates have a high MI at the root zone as opposed to the surface. The MI mostly ranges between 0.5 and 1.5 at both the surface and root zone. The MI is predominantly high for low-lying relative to high-lying areas.

A Prior Estimation of the Spatial Distribution Parameter of Soil Moisture Storage Capacity Using Satellite-Based Root-Zone Soil Moisture Data

Integration of satellite-based data with hydrological modelling was generally conducted via data assimilation or model calibration, and both approaches can enhance streamflow predictions. In this study, we assessed the feasibility of another approach that uses satellite-based soil moisture data to directly estimate the parameter β to represent the degree of the spatial distribution of soil moisture storage capacity in the semi-distributed Hymod model. The impact of using historical root-zone soil moisture data from the Soil Moisture Active Passive (SMAP) mission on the prior estimation of the parameter β was explored. Two different ways to incorporate the root-zone soil moisture data to estimate the parameter β are proposed, i.e., one is to derive a priori distribution of β , and the other is to derive a fixed value for β . The simulations of the Hymod models employing the two ways to estimate β are compared with the results produced by the original model, i.e., the one without employing satellite-based data to estimate the parameter β , at three study catchments (the Upper Hanjiang River catchment, the Xiangjiang River catchment, and the Ganjiang River catchment). The results illustrate that the two ways to incorporate the SMAP root-zone soil moisture data in order to predetermine the parameter β of the semi-distributed Hymod model both perform well in simulating streamflow during the calibration period, and a slight improvement was found during the validation period. Notably, deriving a fixed β value from satellite soil moisture data can provide better performance for ungauged catchments despite reducing the model freedom degrees due to fixing the β value. It is concluded that the robustness of the Hymod model in predicting the streamflow can be improved when the spatial information of satellite-based soil moisture data is utilized to estimate the parameter β .

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

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

Estimating error cross‐correlations in soil moisture data sets using extended collocation analysis

AbstractGlobal soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite‐based soil moisture data into water balance models or merging multisource soil moisture retrievals into a unified data set. However, this requires an appropriate parameterization of the error structures of the underlying data sets. While triple collocation (TC) analysis has been widely recognized as a powerful tool for estimating random error variances of coarse‐resolution soil moisture data sets, the estimation of error cross covariances remains an unresolved challenge. Here we propose a method—referred to as extended collocation (EC) analysis—for estimating error cross‐correlations by generalizing the TC method to an arbitrary number of data sets and relaxing the therein made assumption of zero error cross‐correlation for certain data set combinations. A synthetic experiment shows that EC analysis is able to reliably recover true error cross‐correlation levels. Applied to real soil moisture retrievals from Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) C‐band and X‐band observations together with advanced scatterometer (ASCAT) retrievals, modeled data from Global Land Data Assimilation System (GLDAS)‐Noah and in situ measurements drawn from the International Soil Moisture Network, EC yields reasonable and strong nonzero error cross‐correlations between the two AMSR‐E products. Against expectation, nonzero error cross‐correlations are also found between ASCAT and AMSR‐E. We conclude that the proposed EC method represents an important step toward a fully parameterized error covariance matrix for coarse‐resolution soil moisture data sets, which is vital for any rigorous data assimilation framework or data merging scheme.

Satellite-based terrestrial production efficiency modeling.

Production efficiency models (PEMs) are based on the theory of light use efficiency (LUE) which states that a relatively constant relationship exists between photosynthetic carbon uptake and radiation receipt at the canopy level. Challenges remain however in the application of the PEM methodology to global net primary productivity (NPP) monitoring. The objectives of this review are as follows: 1) to describe the general functioning of six PEMs (CASA; GLO-PEM; TURC; C-Fix; MOD17; and BEAMS) identified in the literature; 2) to review each model to determine potential improvements to the general PEM methodology; 3) to review the related literature on satellite-based gross primary productivity (GPP) and NPP modeling for additional possibilities for improvement; and 4) based on this review, propose items for coordinated research.This review noted a number of possibilities for improvement to the general PEM architecture - ranging from LUE to meteorological and satellite-based inputs. Current PEMs tend to treat the globe similarly in terms of physiological and meteorological factors, often ignoring unique regional aspects. Each of the existing PEMs has developed unique methods to estimate NPP and the combination of the most successful of these could lead to improvements. It may be beneficial to develop regional PEMs that can be combined under a global framework. The results of this review suggest the creation of a hybrid PEM could bring about a significant enhancement to the PEM methodology and thus terrestrial carbon flux modeling.Key items topping the PEM research agenda identified in this review include the following: LUE should not be assumed constant, but should vary by plant functional type (PFT) or photosynthetic pathway; evidence is mounting that PEMs should consider incorporating diffuse radiation; continue to pursue relationships between satellite-derived variables and LUE, GPP and autotrophic respiration (Ra); there is an urgent need for satellite-based biomass measurements to improve Ra estimation; and satellite-based soil moisture data could improve determination of soil water stress.