Remotely-sensed Soil Moisture Retrievals Research Articles

Irrigation characterization improved by the direct use of SMAP soil moisture anomalies within a data assimilation system

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation.

Improving Spatial Patterns Prior to Land Surface Data Assimilation via Model Calibration Using SMAP Surface Soil Moisture Data

AbstractPrior to their application in a land surface data assimilation (DA) system, remotely sensed (RS) soil moisture (SM) products are typically rescaled to remove systematic differences with respect to comparable SM estimates obtained from a land surface model (LSM). This preprocessing of RS SM data—commonly referred to as bias correction—implicitly discards spatial information present in the RS SM retrievals. Here, based on dense SM network measurements obtained within the Huai River Basin of China, we demonstrate that L‐band Soil Moisture Active Passive (SMAP) L3 surface SM retrievals provide a better representation of spatial variability in time‐averaged SM fields than a LSM—suggesting that, as typically applied, bias correction is neglecting important spatial information present in RS SM products. To maximally use RS information, we propose an alternative bias correction approach for SM DA that provides the benefits of traditional rescaling while also improving modeled SM spatial patterns. Specifically, the regional mean of SMAP SM is first corrected using modeled SM of the Variable Infiltration Capacity (VIC) model. Then, two key VIC soil parameters (EXPT and BULKD) are calibrated to match the relative subregional spatial variability captured by the SMAP SM product. Results show that our model calibration method successfully improves VIC SM spatial patterns and pixel‐wise time series. Furthermore, these SM improvements translate into enhanced VIC streamflow estimates. Overall, results suggest that the proposed bias correction framework can improve current land surface DA systems by maximally utilizing spatial information contained in RS SM products.

The Efficiency of Data Assimilation.

Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real-world application of Bayes' theorem is approximate, and therefore we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of tradeoffs between improving (usually expensive) remote sensing observing systems vs. improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the Ensemble Kalman Filter used to assimilate remote sensing soil moisture retrievals from AMSR-E into the Noah land surface model.

Information theoretic evaluation of satellite soil moisture retrievals

Microwave radiometry has a long legacy of providing estimates of remotely sensed near surface soil moisture measurements over continental and global scales. A consistent assessment of the errors and uncertainties associated with these retrievals is important for their effective utilization in modeling, data assimilation and end-use application environments. This article presents an evaluation of soil moisture retrieval products from AMSR-E, ASCAT, SMOS, AMSR2 and SMAP instruments using information theory-based metrics. These metrics rely on time series analysis of soil moisture retrievals for estimating the measurement error, level of randomness (entropy) and regularity (complexity) of the data. The results of the study indicate that the measurement errors in the remote sensing retrievals are significantly larger than that of the ground soil moisture measurements. The SMAP retrievals, on the other hand, were found to have reduced errors (comparable to those of in-situ datasets), particularly over areas with moderate vegetation. The SMAP retrievals also demonstrate high information content relative to other retrieval products, with higher levels of complexity and reduced entropy. Finally, a joint evaluation of the entropy and complexity of remotely sensed soil moisture products indicates that the information content of the AMSR-E, ASCAT, SMOS and AMSR2 retrievals is low, whereas SMAP retrievals show better performance. The use of information theoretic assessments is effective in quantifying the required levels of improvements needed in the remote sensing soil moisture retrievals to enhance their utility and information content.

Exploration of empirical relationship between surface soil temperature and surface soil moisture over two catchments of contrasting climates and land covers

Soil moisture plays a very important role in hydrological processes. It has been found in many studies that the surface soil moisture (SSM) is highly related to the diurnal change of the surface soil temperature (∆SST) at the same soil depth. However, some studies contradict this common belief with findings of a much stronger correlation between the SSM and the SST. In order to investigate this further, we have carried out for the first time a comparative assessment of the in-situ measured SST and ΔSST for SSM estimations, over two catchments with contrasting climate types and land uses (i.e. one in the UK and the other in Australia). In both catchments, the time point for the highest relationship between the SST and the SSM is explored. As a result, it is found the SST is more suitable to monitor the variability of the SSM than the ΔSST in both catchments. Moreover the proposed seasonal-based classification method further improves the SSM simulation results in both catchments, with a superior performance observed in the UK catchment (NSE = 0.900 and RMSE = 0.030). In the Australian catchment, a relatively weaker correlation is observed and some potential reasons are explained. The potential applications of the findings for remote sensing soil moisture retrievals are also discussed.

Active-Layer Soil Moisture Content Regional Variations in Alaska and Russia by Ground-Based and Satellite-Based Methods, 2002 through 2014

Soil moisture is a vital physical parameter of the active-layer in permafrost environments, and associated biological and geophysical processes operative at the microscopic to hemispheric spatial scales and at hourly to multi-decadal time scales. While in-situ measurements can give the highest quality of information on a site-specific basis, the vast permafrost terrains of North America and Eurasia require space-based techniques for assessments of cause and effect and long-term changes and impacts from the changes of permafrost and the active-layer. Satellite-based 6.925 and 10.65 GHz sensor algorithmic retrievals of soil moisture by Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard NASA-Aqua and follow-on AMSR2 onboard JAXA-Global Change Observation Mission—Water-1 are ongoing since July 2002. Accurate land-surface temperature and vegetation parameters are critical to the success of passive microwave algorithmic retrieval schemes. Strategically located soil moisture measurements are needed for spatial and temporal co-location evaluation and validation of the space-based algorithmic estimates. We compare on a daily basis ground-based (subsurface-probe) 50- and 70-MHz radio-frequency soil moisture measurements with NASA- and JAXA-algorithmic retrieval passive microwave retrievals. We find improvements in performance of the JAXA-algorithm (AMSR-E reprocessed and AMSR2 ongoing) relative to the earlier NASA-algorithm version. In the boreal forest regions, accurate land-surface temperatures and vegetation parameters are still needed for algorithmic retrieval success. Over the period of AMSR-E retrievals, we find evidence of at the high northern latitudes of growing terrestrial radio-frequency interference in the 10.65 GHz channel soil moisture content. This is an important error source for satellite-based active and passive microwave remote sensing soil moisture retrievals in Arctic regions that must be addressed.

Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here, we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23° N-50° N and 128° W-65° W. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals

Abstract. A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.