scholarly journals Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements

2019 ◽  
Vol 11 (24) ◽  
pp. 2891 ◽  
Author(s):  
Li Bai ◽  
Xin Lv ◽  
Xiaojun Li
Keyword(s):  
Soil Moisture ◽  
Comprehensive Evaluation ◽  
Pearson Correlation ◽  
Global Scale ◽  
Retrieval Algorithm ◽  
Weather Forecasts ◽  
Dual Channel ◽  
Multi Temporal ◽  
Mean Square Difference

A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active Passive) mission data in April 2015, the associated SM retrieval algorithms have developed rapidly, and their improvement work is still in progress. However, some newly developed SM retrievals have not been fully assessed and inter-compared. One such product is the new multi-temporal dual-channel retrieval algorithm (MT-DCA) SM retrievals, which was recently retrieved using the so-called MT-DCA algorithm. To solve this, we aim to assess the MT-DCA SM retrievals along with the SMAP-enhanced level three SM products (SPL3SMP_E, version 2). More specifically, in this paper we evaluated and inter-compared the two SMAP SM retrievals with the ECMWF (European Centre for Medium-Range Weather Forecasts) modeled SM and ISMN (International Soil Moisture Network) in situ observations by applying four statistical scores: Pearson correlation coefficient (R), root mean square difference (RMSD), bias, and unbiased RMSD (ubRMSD). It was found that both SMAP SM retrievals can better capture the seasonal variations of ECMWF-modeled SM and ground-based measurements according to correlations, and MT-DCA SM was drier than SPL3SMP_E SM by ~0.018 m3/m3 on average on a global scale. With respect to the ISMN ground-based measurements, the performance of SPL3SMP_E SM compared better than the MT-DCA SM. The median ubRMSD of SPL3SMP_E SM and MT-DCA SM with ground measurements computed over all selected ISMN sites were 0.058 m3/m3 and 0.070 m3/m3, respectively.

scholarly journals Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture

2021 ◽  
Vol 25 (9) ◽  
pp. 5029-5045
Author(s):  
Bonan Li ◽  
Stephen P. Good
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Model Uncertainty ◽  
Pearson Correlation ◽  
Microwave Remote Sensing ◽  
Random Uncertainty ◽  
Dual Channel ◽  
Informational Model

Abstract. The National Aeronautics and Space Administration (NASA) Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal (TBh) and vertical (TBv) polarized microwave brightness temperatures through a modeled mechanistic relationship between vegetation opacity, surface scattering albedo, and soil effective temperature (Teff). Although this model has been validated against in situ soil moisture, there is a lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, we assess how the information content of in situ soil moisture observations from the US Climate Reference Network contrasts with (1) the information contained within raw SMAP observations (i.e., “informational random uncertainty”) derived from TBh, TBv, and Teff themselves and with (2) the information contained in SMAP's dual-channel algorithm (DCA) soil moisture estimates (i.e., “informational model uncertainty”) derived from the model's inherent structure and parameterizations. The results show that, on average, 80 % of the information in the in situ soil moisture is unexplained by SMAP DCA soil moisture estimates. Loss of information in the DCA modeling process contributes 35 % of the unexplained information, while the remainder is induced by a lack of additional explanatory power within TBh, TBv, and Teff. Overall, retrieval quality of SMAP DCA soil moisture, denoted as the Pearson correlation coefficient between SMAP DCA soil moisture and in situ soil moisture, is negatively correlated with the informational uncertainties, with slight differences across different land covers. The informational model uncertainty (Pearson correlation of −0.59) was found to be more influential than the informational random uncertainty (Pearson correlation of −0.34), suggesting that the poor performance of SMAP DCA at some locations is driven by model parameterization and/or structure and not underlying satellite measurements of TBh and TBv. A decomposition of mutual information between TBh, TBv, and DCA soil moisture shows that on average 58 % of information provided by TBh and TBv to DCA estimates is redundant. The amount of information redundantly and synergistically provided by TBh and TBv was found to be closely related (Pearson correlations of 0.79 and −0.82, respectively) to the retrieval quality of SMAP DCA. TBh and TBv tend to contribute large redundant information to DCA estimates under surfaces or conditions where DCA makes better retrievals. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end-user products.

scholarly journals Validation of SMOS-IC Soil Moisture over Brazilian Semiarid Using in situ Measurements

10.29007/19gn ◽  
2018 ◽  
Author(s):  
Diego Cézar Dos Santos Araújo ◽  
Suzana Maria Gico Lima Montenegro ◽  
Ana Cláudia Villar E Luna Gusmão ◽  
Diogo Francisco Borba Rodrigues
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
In Situ Measurements ◽  
Remote Sensors ◽  
Error Metrics ◽  
Brazilian Semiarid ◽  
Ocean Salinity ◽  
Mean Square Difference

Soil Moisture and Ocean Salinity (SMOS) data validation has been widely performed worldwide since the product became available. However, there are few studies for Brazil. This study focused on the validation of a new version of SMOS product developed by the Institut National de la Recherche Agronomique (INRA) and Center d'Etudes Spatiales de la BIOsphère (CESBIO). One of the advantages of SMOS- INRA-CESBIO (SMOS-IC) is that the product is as independent as possible from auxiliary data. The validation was performed at 7 stations located in a semi-arid mesoregion of Pernambuco State, Northeast Brazil, for the year 2016. Bias, root mean square difference (RMSD), unbiased RMSD and the Pearson correlation coefficient (R) were computed between the SMOS-IC data and in situ measurements, considering only the ascending orbit (6 am local time). The results were consistent with those found in several studies, including error metrics. The correlation coefficient (r) ranged from 0.53 to 0.86 (mean 0.68) and the sensor was able to respond adequately to rainfall events. The results of this study are useful to demonstrate the need to continue validating soil moisture data obtained by remote sensors throughout the state, especially in more rainy locations.

scholarly journals Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1225
Author(s):  
Lanka Karthikeyan ◽  
Ming Pan ◽  
Dasika Nagesh Kumar ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
Global Scale ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Structural Uncertainty ◽  
Three Solutions ◽  
Brightness Temperatures ◽  
Retrieval Algorithms

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

scholarly journals Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.

2018 ◽  
Vol 10 (9) ◽  
pp. 1351 ◽  
Author(s):  
Hongzhang Xu ◽  
Qiangqiang Yuan ◽  
Tongwen Li ◽  
Huanfeng Shen ◽  
Liangpei Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Cycle ◽  
Satellite System ◽  
Global Scale ◽  
Generalized Regression Neural Network ◽  
Level 3 ◽  
Global Navigation Satellite ◽  
Microwave Sensors ◽  
Surface Fusion

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm3 cm−3. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM.

scholarly journals Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

2019 ◽  
Vol 11 (9) ◽  
pp. 1113 ◽  
Author(s):  
Franklin Paredes-Trejo ◽  
Humberto Barbosa ◽  
Carlos A. C. dos Santos
Keyword(s):  
Soil Moisture ◽  
Pearson Correlation ◽  
European Space Agency ◽  
Rainfall Events ◽  
Error Components ◽  
Space Agency ◽  
Daily Scale ◽  
Bioclimatic Conditions ◽  
Heavy Rainfall Events

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.

scholarly journals Information – based uncertainty decomposition in dual channel microwave remote sensing of soil moisture

2020 ◽  
Author(s):  
Bonan Li ◽  
Stephen P. Good
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Mutual Information ◽  
Microwave Remote Sensing ◽  
Reference Network ◽  
Brightness Temperatures ◽  
Dual Channel ◽  
The Us ◽  
In Situ Observations

Abstract. NASA's Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal, TBh, and vertical, TBv, polarized microwave brightness temperatures through a modeled relationship between vegetation opacity and surface scattering albedo (i.e. tau-omega model). Although this model has been validated against in situ soil moisture measurements across sparse validations sites, there is lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, soil moisture observations from the US Climate Reference Network are used within a mutual information framework to decompose the overall retrieval uncertainty from SMAPs Modified Dual Channel Algorithm (MDCA) into random uncertainty derived from raw data itself and model uncertainty derived from the model’s inherent structure. The results shown that, on average, 12 % of the uncertainty in SMAP soil moisture estimates is caused by the loss of information in the MDCA model itself while the remainder is induced by inadequacy of TBh and TBv observations. We find the fraction of algorithm induced uncertainty is negatively correlated (pearson r of −0.48) with correlations between in-situ observations and MDCA estimates. A decomposition of mutual information between TBh, TBv and MDCA soil moisture shows that on average 55 % of the mutual information is redundantly shared by TBh and TBv, while the information provided uniquely from both TBh and TBv is 15 %. The fraction of information redundantly provided by TBh and TBv was found to be tightly correlated (pearson r = −0.7) to how well the MDCA output correlated to in situ observations. Thus, MDCA overall quality improves as TBh and TBv provide more redundant information for the MDCA. This suggests the informational redundancy between these remotely sensed observations can be used as independent metric to assess the overall quality of algorithms using these data streams. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end user products.

scholarly journals Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

2021 ◽  
Author(s):  
Xiaolu Ling ◽  
Ying Huang ◽  
Weidong Guo ◽  
Yixin Wang ◽  
Chaorong Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Mainland China ◽  
Severe Drought ◽  
Weather Forecasts ◽  
European Centre ◽  
Drought Conditions ◽  
Medium Range

Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product [European Space Agency's Climate Change Initiative (ESA CCI)] and four reanalysis datasets [European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERAI), National Centers for Environmental Prediction (NCEP), the Twentieth Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)], are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over mainland China. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China, with a relative RMSE value larger than 0.1. However, none of the products can well reproduce the trends of interannual anomalies. The largest relative bias of 44.6 % is found for the ERAI SM product under severe drought conditions, and the lowest relative biases of 4.7 % and 9.5 % are found for the ESA CCI SM product under severe drought conditions and the NCEP SM product under normal conditions, respectively. As decomposing mean square errors in all the products suggests that the bias terms are the dominant contribution, the ESA CCI SM product is a good option for long-term hydrometeorological applications in mainland China. ERA5 is also a promising product, which is attributed to the incorporation of more observations. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

scholarly journals Validation of SMAP L2 passive-only soil moisture products using <i>in situ</i> measurements collected in Twente, The Netherlands

2019 ◽  
Author(s):  
Rogier van der Velde ◽  
Andreas Colliander ◽  
Michiel Pezij ◽  
Harm-Jan F. Benninga ◽  
Rajat Bindlish ◽  
...  
Keyword(s):  
Soil Moisture ◽  
The Netherlands ◽  
Mean Squared Error ◽  
Single Channel ◽  
Soil Depth ◽  
Soil Freezing ◽  
Horizontal Polarization ◽  
Dual Channel ◽  
Spatially Distributed

Abstract. The Twente region in the east of the Netherlands has a network with twenty soil monitoring stations that has been utilized for validation of the Soil Moisture Active/Passive (SMAP) passive-only soil moisture products. Over the period from April 2015 until December 2018, seven stations covered by the SMAP reference pixels have fairly complete data records. Spatially distributed soil moisture simulations with the Dutch national hydrological model have been utilized for the development of upscaling functions to translate the spatial mean of point measurements to the domain of the SMAP reference pixels. The native and upscaled spatial soil moisture means have been adopted as in situ references to assess the performance of the SMAP i) Single Channel Algorithm at Horizontal Polarization (SCA-H), ii) Single Channel Algorithm at Vertical Polarization (SCA-V), and iii) Dual Channel Algorithm (DCA) soil moisture estimates. In the case of the Twente network it was found that the SCA-V soil moisture retrieved SMAP observations collected in the afternoon had the best agreement with the in situ references leading to an unbiased Root Mean Squared Error (uRMSE) of 0.059 m3 m−3. This is larger than the mission target accuracy of 0.04 m3 m−3, which can be attributed to large over- and underestimation errors (> 0.08 m3 m−3) in particular at the end of dry spells and during freezing, respectively. The strong vertical dielectric gradients associated with rapid soil freezing and wetting causes the disparity in soil depth characterized by SMAP and in situ that leads to the large mismatches. Once filtered for frozen conditions and antecedent rainfall the uRMSE improves to 0.043 m3 m−3.

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