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Data Assimilation of Satellite-Based Observations into Land Surface Models

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 40466

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Special Issue Editors

Dr. Clement Albergel

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ESA - European Space Agency, Climate Office, Oxfordshire, UK
Interests: land surface modelling; climate change; hydrology; data analysis
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Dr. Emanuel Dutra

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Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
Interests: meteorology; climate; land surface; hydrology

Dr. Sujay Kumar

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Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: land surface modeling; hydrology; data assimilation; remote sensing; optimization
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Dr. Christoph Rüdiger

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Department of Civil Engineering, Faculty of Engineering, 23 College Walk, Monash University, VIC 3800, Australia (Clayton campus)
Interests: soil moisture; remote sensing; hydrology; climate change
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Prof. Dr. Dongryeol Ryu

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Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Austrilia
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
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Dr. Nemesio Rodriguez-Fernandez

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CESBIO, CNES/CNRS/IRD/UPS, UMR 5126, 31401 Toulouse, CEDEX 9, France
Interests: microwave remote sensing; soil moisture; vegetation optical depth; biomass; machine learning
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Special Issue Information

Dear Colleagues,

The accurate characterization and simulation of hydrological and biophysical variables at the land surface pose a significant challenge, given the large spatial heterogeneity and human modifications of the land surface. In particular, observing and simulating the response and feedbacks of land surface conditions to extreme events is important in our ability to manage the adaptation to climate change. The role of Land Surface Model (LSM) has evolved over the years, from the primary goal of providing boundary conditions to atmospheric models to being used as a monitoring and forecasting tool for estimating land surface conditions. As a result, there is a big emphasis on constraining the LSM estimates with observational inputs and coupling them with other models of the Earth system (e.g. river-routing models). The modeling of terrestrial variables can be improved through the dynamical integration of observations. Remote sensing observations are particularly useful in this context, as they are now unrestrictedly available at a global scale, high resolution, and long time periods. Many satellite-derived products relevant to the hydrological (e.g., soil moisture, snow depth and cover, terrestrial water storage), vegetation (e.g., LAI, NDVI, FAPAR, biomass), and energy (e.g., LST, albedo) cycles are already available. Data assimilation allows to spatially and temporally integrate the observed information into LSMs in a consistent way.

We invite papers dealing with the integration of satellite earth observations into Land Surface Models. Potential topics include, but are not limited to, novel schemes, methodologies, and applications.

Dr. Clement Albergel
Dr. Emanuel Dutra
Dr. Sujay Kumar
Dr. Christoph Rüdiger
Dr. Dongryeol Ryu
Dr. Nemesio Rodriguez-Fernandez
Guest Editors

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Keywords

Spatial Remote Sensing
Land Surface Modeling
Data Assimilation
Earth Observations

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Published Papers (7 papers)

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Research

29 pages, 6898 KiB

Open AccessArticle

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

by Yonghwan Kwon, Barton A. Forman, Jawairia A. Ahmad, Sujay V. Kumar and Yeosang Yoon

Remote Sens. 2019, 11(19), 2265; https://doi.org/10.3390/rs11192265 - 28 Sep 2019

Cited by 33 | Viewed by 4245

Abstract

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔT_B) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔT_B, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔT_B is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔT_B is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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25 pages, 18864 KiB

Open AccessArticle

Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework

by Han Yang, Lihua Xiong, Qiumei Ma, Jun Xia, Jie Chen and Chong-Yu Xu

Remote Sens. 2019, 11(11), 1335; https://doi.org/10.3390/rs11111335 - 3 Jun 2019

Cited by 13 | Viewed by 3642

Abstract

The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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23 pages, 12151 KiB

Open AccessArticle

SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

by Nemesio Rodríguez-Fernández, Patricia de Rosnay, Clement Albergel, Philippe Richaume, Filipe Aires, Catherine Prigent and Yann Kerr

Remote Sens. 2019, 11(11), 1334; https://doi.org/10.3390/rs11111334 - 3 Jun 2019

Cited by 43 | Viewed by 7976

Abstract

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T_2m and RH_2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T_2m and RH_2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T_2m and RH_2m DA improves the forecast in April–September, while NNSM alone has a significant positive effect in July–September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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26 pages, 13474 KiB

Open AccessArticle

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

by Moustapha Tall, Clément Albergel, Bertrand Bonan, Yongjun Zheng, Françoise Guichard, Mamadou Simina Dramé, Amadou Thierno Gaye, Luc Olivier Sintondji, Fabien C. C. Hountondji, Pinghouinde Michel Nikiema and Jean-Christophe Calvet

Remote Sens. 2019, 11(6), 735; https://doi.org/10.3390/rs11060735 - 26 Mar 2019

Cited by 18 | Viewed by 5887

Abstract

This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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22 pages, 11019 KiB

Open AccessFeature PaperArticle

Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

by Clément Albergel, Emanuel Dutra, Bertrand Bonan, Yongjun Zheng, Simon Munier, Gianpaolo Balsamo, Patricia de Rosnay, Joaquin Muñoz-Sabater and Jean-Christophe Calvet

Remote Sens. 2019, 11(5), 520; https://doi.org/10.3390/rs11050520 - 4 Mar 2019

Cited by 46 | Viewed by 7686

Abstract

This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Météo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000–2018) and SSM (2008–2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25° × 0.25° (January 2008 to October 2018) and 0.10° × 0.10° (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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29 pages, 7143 KiB

Open AccessArticle

An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US

by Jostein Blyverket, Paul D. Hamer, Laurent Bertino, Clément Albergel, David Fairbairn and William A. Lahoz

Remote Sens. 2019, 11(5), 478; https://doi.org/10.3390/rs11050478 - 26 Feb 2019

Cited by 25 | Viewed by 5537

Abstract

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a

95 %

significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the

95 %

significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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28 pages, 3385 KiB

Open AccessArticle

Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks

by Aynom T. Teweldebrhan, John F. Burkhart, Thomas V. Schuler and Chong-Yu Xu

Remote Sens. 2019, 11(1), 28; https://doi.org/10.3390/rs11010028 - 25 Dec 2018

Cited by 7 | Viewed by 4146

Abstract

Remote sensing fractional snow cover area (fSCA) has been increasingly used to get an improved estimate of the spatiotemporal distribution of snow water equivalent (SWE) through reanalysis using different data assimilation (DA) schemes. Although the effective assimilation period of fSCA is well recognized in previous studies, little attention has been given to explicitly account for the relative significance of measurements in constraining model parameters and states. Timing of the more informative period varies both spatially and temporally in response to various climatic and physiographic factors. Here we use an automatic detection approach to locate the critical points in the time axis where the mean snow cover changes and where the melt-out period starts. The assimilation period was partitioned into three timing windows based on these critical points. A fuzzy coefficient was introduced in two ensemble-based DA schemes to take into account for the variability in informational value of fSCA observations with time. One of the DA schemes used in this study was the particle batch smoother (Pbs). The main challenge in Pbs and other Bayesian-based DA schemes is, that most of the weights are carried by few ensemble members. Thus, we also considered an alternative DA scheme based on the limits of acceptability concept (LoA) and certain hydrologic signatures and it has yielded an encouraging result. An improved estimate of SWE was also obtained in most of the analysis years as a result of introducing the fuzzy coefficients in both DA schemes. The most significant improvement was obtained in the correlation coefficient between the predicted and observed SWE values (site-averaged); with an increase by 8% and 16% after introducing the fuzzy coefficient in Pbs and LoA, respectively. Full article

(This article belongs to the Special Issue Data Assimilation of Satellite-Based Observations into Land Surface Models)

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