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 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 The AQUI Soil Moisture Network for Satellite Microwave Remote Sensing Validation in South-Western France

2018 ◽  
Vol 10 (11) ◽  
pp. 1839 ◽  
Author(s):  
A. Al-Yaari ◽  
S. Dayau ◽  
C. Chipeaux ◽  
C. Aluome ◽  
A. Kruszewski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Agricultural Research ◽  
Pearson Correlation ◽  
European Space Agency ◽  
Microwave Remote Sensing ◽  
Climate Conditions ◽  
Space Agency ◽  
Satellite Microwave

Global soil moisture (SM) products are currently available thanks to microwave remote sensing techniques. Validation of these satellite-based SM products over different vegetation and climate conditions is a crucial step. INRA (National Institute of Agricultural Research) has set up the AQUI SM and soil temperature in situ network (composed of three main sites Bouron, Bilos, and Hermitage), over a flat area of dense pine forests, in South-Western France (the Bordeaux–Aquitaine region) to validate the Soil Moisture and Ocean salinity (SMOS) satellite SM products. SMOS was launched in 2009 by the European Space Agency (ESA). The aims of this study are to present the AQUI network and to evaluate the SMOS SM product (in the new SMOS-IC version) along with other microwave SM products such as the active ASCAT (Advanced Scatterometer) and the ESA combined (passive and active) CCI (Climate Change Initiative) SM retrievals. A first comparison, using Pearson correlation, Bias, RMSE (Root Mean Square Error), and Un biased RMSE (ubRMSE) scores, between the 0–5 cm AQUI network and ASCAT, CCI, and SMOS SM products was conducted. In general all the three products were able to reproduce the annual cycle of the AQUI in situ observations. CCI and ASCAT had best and similar correlations (R~0.72) over the Bouron and Bilos sites. All had comparable correlations over the Hermitage sites with overall average values of 0.74, 0.68, and 0.69 for CCI, SMOS-IC, and ASCAT, respectively. Considering anomalies, correlation values decreased for all products with best ability to capture day to day variations obtained by ASCAT. CCI (followed by SMOS-IC) had the best ubRMSE values (mostly < 0.04 m3/m3) over most of the stations. Although the region is highly impacted by radio frequency interferences, SMOS-IC followed correctly the in situ SM dynamics. All the three remotely-sensed SM products (except SMOS-IC over some stations) overestimated the AQUI in situ SM observations. These results demonstrate that the AQUI network is likely to be well-suited for satellite microwave remote sensing evaluations/validations.

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2777
Author(s):  
Tao Cheng ◽  
Siyang Hong ◽  
Bensheng Huang ◽  
Jing Qiu ◽  
Bikui Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Pearson Correlation ◽  
Microwave Remote Sensing ◽  
Northeastern China ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Passive Microwave Remote Sensing ◽  
Soil Moisture Data

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

2018 ◽  
Vol 22 (6) ◽  
pp. 3515-3532 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

scholarly journals Comparing soil moisture retrievals from SMOS and ASCAT over France

2011 ◽  
Vol 8 (5) ◽  
pp. 8565-8607 ◽  
Author(s):  
M. Parrens ◽  
E. Zakharova ◽  
S. Lafont ◽  
J.-C. Calvet ◽  
Y. Kerr ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Correlation Coefficients ◽  
Area Index ◽  
Brightness Temperatures ◽  
Geographic Patterns ◽  
Ocean Salinity ◽  
In Situ Observations ◽  
Better Than

Abstract. The first products derived over France in 2010 from the L-band brightness temperatures (Tb) measured by the SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, were compared with the surface soil moisture (SSM) estimates produced by the C-band Advanced Scatterometter, ASCAT, launched in 2006 on board METOP-A. SMOS and ASCAT SSM products were compared with the simulations of the ISBA-A-gs model and with in situ measurements from the SMOSMANIA network, including 21 stations located in southern France. ASCAT tended to correlate better than SMOS with ISBA-A-gs. The significant anomaly correlation coefficients between in situ observations and the SMOS (ASCAT) product ranged from 0.23 to 0.48 (0.35 to 0.96). However, in wet conditions, similar results between the two satellite products were found. An attempt was made to derive SSM from regressed empirical logarithmic equations using a combination of SMOS Tb at different incidence angles and different polarizations, and the Leaf Area Index (LAI) modeled by ISBA-A-gs. The analysis of the intercept coefficient of the regression showed an impact of topography. A similar analysis applied to ASCAT and SMOS SSM values showed a more limited impact of topography on the intercept coefficient of the SMOS SSM product, while fewer residual geographic patterns were found for the ASCAT SSM.

scholarly journals Comparing soil moisture retrievals from SMOS and ASCAT over France

2012 ◽  
Vol 16 (2) ◽  
pp. 423-440 ◽  
Author(s):  
M. Parrens ◽  
E. Zakharova ◽  
S. Lafont ◽  
J.-C. Calvet ◽  
Y. Kerr ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Correlation Coefficients ◽  
Area Index ◽  
Brightness Temperatures ◽  
Geographic Patterns ◽  
Ocean Salinity ◽  
In Situ Observations ◽  
Better Than

Abstract. The first products derived over France in 2010 from the L-band brightness temperatures (Tb) measured by the SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, were compared with the surface soil moisture (SSM) estimates produced by the C-band Advanced Scatterometer, ASCAT, launched in 2006 on board METOP-A. SMOS and ASCAT SSM products were compared with the simulations of the ISBA-A-gs model and with in situ measurements from the SMOSMANIA network, including 21 stations located in southern France. ASCAT tended to correlate better than SMOS with ISBA-A-gs. The significant anomaly correlation coefficients between in situ observations and the SMOS (ASCAT) product ranged from 0.23 to 0.48 (0.35 to 0.96). However, in wet conditions, similar results between the two satellite products were found. An attempt was made to derive SSM from regressed empirical logarithmic equations using a combination of SMOS Tb at different incidence angles and different polarizations, and the Leaf Area Index (LAI) modeled by ISBA-A-gs. The analysis of the intercept coefficient of the regression showed an impact of topography. A similar analysis applied to ASCAT and SMOS SSM values showed a more limited impact of topography on the intercept coefficient of the SMOS SSM product, while fewer residual geographic patterns were found for the ASCAT SSM.

scholarly journals Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002

2006 ◽  
Vol 7 (1) ◽  
pp. 23-38 ◽  
Author(s):  
H. Gao ◽  
E. F. Wood ◽  
T. J. Jackson ◽  
M. Drusch ◽  
R. Bindlish
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Hydrological Modeling ◽  
Dynamic Range ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Southern United States ◽  
Retrieval Algorithm ◽  
Brightness Temperatures

Abstract Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

scholarly journals Dynamic time warping analysis of the evolution of SMOS surface and in-situ soil moisture time series

2020 ◽  
Author(s):  
Christoph Herbert ◽  
Miriam Pablos ◽  
Mercedes Vall-llossera ◽  
Adriano Camps
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Soil Moisture ◽  
Temporal Variability ◽  
Dynamic Time Warping ◽  
Time Lag ◽  
Time Warping ◽  
In Situ Observations ◽  
Dynamic Time

&lt;p&gt;A comprehensive understanding of temporal variability of root-zone and surface soil moisture (SM) and the relationship with the underlying soil characteristics is of great importance in hydrological and agricultural applications. For the last ten years, global and frequent satellite SM observations have been available to investigate SM dynamics. However, validating remote sensing retrievals against in-situ observations based on the comparison of collocated SM time series is complicated. While satellite retrievals are approximated from inversion models over an area, in-situ measurements are determined at point-scale. This usually produces different SM dynamic ranges and biases in the corresponding time series. Moreover, the influence of soil properties and meteorological conditions can cause SM time series obtained from indirect remote sensing techniques and direct in-situ observations to be non-linearly related. Dynamic Time Warping (DTW) is a dynamic programming technique, capable of coping with temporal distortions by aiming for finding the optimal match between time series.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, DTW was used to provide a time lag evolution as a continuous dissimilarity measure comprising the main temporal variability features of two time series. The DTW technique was applied to SM time series from the Soil Moisture and Ocean Salinity mission (SMOS) L4 product developed at Barcelona Expert Center (BEC) with in-situ measurements at top- and subsoil-representative depth levels, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW parameters were customized to the particular input time series to obtain a robust and meaningful time lag. Seasonal differences in SM dynamics were analyzed in a clustering approach by investigating the link between SM time series and SM-regime-related parameters including precipitation and categorical features such as soil type and land use. Since the technique resolves the non-linear behaviour of time series, it has the potential to generally assess major differences in SM acquisition techniques. It could also be useful to investigate spatial SM variability in heterogeneous regions and to make informed choices in future sensor deployment in SM networks.&lt;/p&gt;

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