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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 35525

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Special Issue Editors

Dr. Patricia De Rosnay

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Guest Editor

European Center For Medium-Range Weather Forecasts, UK
Interests: land surface data assimilation; coupled assimilation; Earth system modelling; land surface observations; forward modelling
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Dr. Clement Albergel

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Guest Editor

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

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Guest Editor

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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Special Issue Information

Dear Colleagues,

Land surface observations are increasingly used to constrain climate models, weather prediction systems and hydrological forecast and flood alert systems. Current satellites provide relevant information on hydrology (e.g., soil moisture, snow depth and cover, terrestrial water storage, inland water extent and temperature), vegetation (e.g., LAI, NDVI, FAPAR, biomass) and energy (e.g., LST, albedo).

This Special Issue aims at documenting most recent progress in using land surface remote sensing observations (soil moisture, vegetation, snow extent and water equivalent, lakes and land surface temperature, land surface albedo, flooded areas) for Earth system modelling applications, including weather forecasts, climate modelling and hydrological forecasts. We welcome studies related to land surface data assimilation, land surface re-analysis, as well as land surface forward modelling (VIS/IR/MW), inverse modelling and machine learning. The Special Issue also encourages studies that investigate land surface parameter retrieval, coupled assimilation in land-hydrology-atmosphere systems as well as intercomparison studies.

Dr. Patricia De Rosnay
Dr. Sujay Kumar
Dr. Clement Albergel
Guest Editors

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Keywords

Land surface observations
Land surface data assimilation Land surface monitoring
Coupled land–atmosphere data assimilation
Retrieval of surface parameters
Land surface forward modelling

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Published Papers (9 papers)

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24 pages, 11357 KiB

Open AccessArticle

Improving Snow Analyses for Hydrological Forecasting at ECCC Using Satellite-Derived Data

by Camille Garnaud, Vincent Vionnet, Étienne Gaborit, Vincent Fortin, Bernard Bilodeau, Marco Carrera and Dorothy Durnford

Remote Sens. 2021, 13(24), 5022; https://doi.org/10.3390/rs13245022 - 10 Dec 2021

Cited by 3 | Viewed by 3032

Abstract

As part of the National Hydrological Services Transformation Initiative, Environment and Climate Change Canada (ECCC) designed and implemented the National Surface and River Prediction System (NSRPS) in order to provide surface and river flow analysis and forecast products across Canada. Within NSRPS, the Canadian Land Data Assimilation System (CaLDAS) produces snow analyses that are used to initialise the land surface model, which in turn is used to force the river routing component. Originally, CaLDAS was designed to improve atmospheric forecasts with less focus on hydrological processes. When snow data assimilation occurs, the related increments remove/add water from/to the system, which can sometimes be problematic for streamflow forecasting, in particular during the snowmelt period. In this study, a new snow analysis method introduces multiple innovations that respond to the need for higher quality snow analyses for hydrological purposes, including the use of IMS snow cover extent data instead of in situ snow depth observations. The results show that the new snow assimilation methodology brings an overall improvement to snow analyses and substantially enhances water conservation, which is reflected in the generally improved streamflow simulations. This work represents a first step towards a new snow data assimilation process in CaLDAS, with the final objective of producing a reliable snow analysis to initialise and improve NWP as well as environmental predictions, including flood and drought forecasts. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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17 pages, 6201 KiB

Open AccessArticle

SMOS Brightness Temperature Monitoring Quality Control Review and Enhancements

by Peter Weston and Patricia de Rosnay

Remote Sens. 2021, 13(20), 4081; https://doi.org/10.3390/rs13204081 - 13 Oct 2021

Viewed by 2236

Abstract

Brightness temperature (Tb) observations from the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) instrument are passively monitored in the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Several quality control procedures are performed to screen out poor quality data and/or data that cannot accurately be simulated from the numerical weather prediction (NWP) model output. In this paper, these quality control procedures are reviewed, and enhancements are proposed, tested, and evaluated. The enhancements presented include improved sea ice screening, coastal and ambiguous land-ocean screening, improved radio frequency interference (RFI) screening, and increased usage of observation at the edge of the satellite swath. Each of the screening changes results in improved agreement between the observations and model equivalent values. This is an important step in advance of future experiments to test the direct assimilation of SMOS Tbs into the ECMWF land data assimilation system. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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18 pages, 35894 KiB

Open AccessArticle

Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods

by Zhangyu Sun, Bao Zhang and Yibin Yao

Remote Sens. 2021, 13(5), 1016; https://doi.org/10.3390/rs13051016 - 8 Mar 2021

Cited by 34 | Viewed by 3497

Abstract

As a crucial parameter in estimating precipitable water vapor from tropospheric delay, the weighted mean temperature (T_m) plays an important role in Global Navigation Satellite System (GNSS)-based water vapor monitoring techniques. However, the rigorous calculation of T_m requires vertical profiles of temperature and water vapor pressure that are difficult to acquire in practice. As a result, empirical models are widely used but have limited accuracy. In this study, we use three machine learning methods, i.e., random forest (RF), backpropagation neural network (BPNN), and generalized regression neural network (GRNN), to improve the estimation of empirical T_m in China. The basic idea is to use the high-quality radiosonde observations estimated T_m to calibrate and optimize the empirical T_m through machine learning methods. Validating results show that the three machine learning methods improve the T_m accuracy by 37.2%, 32.6%, and 34.9% compared with the global pressure and temperature model 3 (GPT3). In addition to the overall accuracy improvement, the proposed methods also mitigate the accuracy variations in space and time, guaranteeing evenly high accuracy. This study provides a new idea to estimate T_m, which could potentially contribute to the GNSS meteorology. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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21 pages, 3706 KiB

Open AccessArticle

The Met Office Operational Soil Moisture Analysis System

by Breogán Gómez, Cristina L. Charlton-Pérez, Huw Lewis and Brett Candy

Remote Sens. 2020, 12(22), 3691; https://doi.org/10.3390/rs12223691 - 11 Nov 2020

Cited by 19 | Viewed by 3117

Abstract

In this study, the current Met Office operational land surface data assimilation system used to produce soil moisture analyses is presented. The main aim of including Land Surface Data Assimilation (LSDA) in both the global and regional systems is to improve forecasts of surface air temperature and humidity. Results from trials assimilating pseudo-observations of 1.5 m air temperature and specific humidity and satellite-derived soil wetness (ASCAT) observations are analysed. The pre-processing of all the observations is described, including the definition and construction of the pseudo-observations. The benefits of using both observations together to produce improved forecasts of surface air temperature and humidity are outlined both in the winter and summer seasons. The benefits of using active LSDA are quantified by the root mean squared error, which is computed using both surface observations and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses as truth. For the global model trials, results are presented separately for the Northern (NH) and Southern (SH) hemispheres. When compared against ground-truth, LSDA in winter NH appears neutral, but in the SH it is the assimilation of ASCAT that contributes to approximately a 2% improvement in temperatures at lead times beyond 48 h. In NH summer, the ASCAT soil wetness observations degrade the forecasts against observations by about 1%, but including the screen level pseudo-observations provides a compensating benefit. In contrast, in the SH, the positive effect comes from including the ASCAT soil wetness observations, and when both observations types are assimilated there is a compensating effect. Finally, we demonstrate substantial improvements to hydrological prediction when using land surface data assimilation in the regional model. Using the Nash-Sutcliffe Efficiency (NSE) metric as an aggregated measure of river flow simulation skill relative to observations, we find that NSE was improved at 106 of 143 UK river gauge locations considered after LSDA was introduced. The number of gauge comparisons where NSE exceeded 0.5 is also increased from 17 to 28 with LSDA. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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25 pages, 6160 KiB

Open AccessArticle

Evaluation of a Microwave Emissivity Module for Snow Covered Area with CMEM in the ECMWF Integrated Forecasting System

by Yoichi Hirahara, Patricia de Rosnay and Gabriele Arduini

Remote Sens. 2020, 12(18), 2946; https://doi.org/10.3390/rs12182946 - 11 Sep 2020

Cited by 15 | Viewed by 3953

Abstract

The Community Microwave Emission Modelling platform (CMEM) has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator radiative transfer model for low frequency passive microwave brightness temperatures (TB). It is used at ECMWF for L-band TB monitoring over snow free areas. In this paper, upgrades to CMEM are presented in order to explore forward modelling in snow-covered areas for coupled land-atmosphere numerical weather prediction systems. The upgrades enable to use CMEM on an extended range of frequencies and the Helsinki University of Technology multi-layer snow emission model is implemented. Offline CMEM experiments are evaluated against AMSR2 (Advanced Microwave Scanning Radiometer 2) observations showing that simulated TB is improved when using a multi-layer snow scheme, compared to a single-layer scheme. The improvements mainly result from a better representation of snow characteristics in the multi-layer snowpack model. CMEM is also evaluated in the Integrated Forecasting System and coupled to RTTOV (Radiative Transfer for TOVS). The numerical results show improved simulated TB at low frequency V polarization over snow-covered area compared to a configuration using emissivity atlas. Degradations at frequencies higher than 20 GHz indicate that further improvements are required in the emissivity and snowpack properties modelling. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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23 pages, 6685 KiB

Open AccessEditor’s ChoiceArticle

From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States

by Anthony Mucia, Bertrand Bonan, Yongjun Zheng, Clément Albergel and Jean-Christophe Calvet

Remote Sens. 2020, 12(12), 2020; https://doi.org/10.3390/rs12122020 - 24 Jun 2020

Cited by 9 | Viewed by 3511

Abstract

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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27 pages, 2438 KiB

Open AccessArticle

Impact of Surface Albedo Assimilation on Snow Estimation

by Sujay Kumar, David Mocko, Carrie Vuyovich and Christa Peters-Lidard

Remote Sens. 2020, 12(4), 645; https://doi.org/10.3390/rs12040645 - 15 Feb 2020

Cited by 21 | Viewed by 4968

Abstract

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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21 pages, 7621 KiB

Open AccessArticle

Cold Bias of ERA5 Summertime Daily Maximum Land Surface Temperature over Iberian Peninsula

by Frederico Johannsen, Sofia Ermida, João P. A. Martins, Isabel F. Trigo, Miguel Nogueira and Emanuel Dutra

Remote Sens. 2019, 11(21), 2570; https://doi.org/10.3390/rs11212570 - 1 Nov 2019

Cited by 63 | Viewed by 6806

Abstract

Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim and ERA5 reanalyses over Iberian Peninsula using the Satellite Application Facility on Land Surface Analysis (LSA-SAF) product and to (ii) understand the main drivers of the LST errors in the reanalysis. Simulations with the ECMWF land-surface model in offline mode (uncoupled) were carried out over the Iberian Peninsula and compared with the reanalysis data. Several sensitivity simulations were performed in a confined domain centered in Southern Portugal to investigate potential sources of the LST errors. The Copernicus Global Land Service (CGLS) fraction of green vegetation cover (FCover) and the European Space Agency’s Climate Change Initiative (ESA-CCI) Land Cover dataset were explored. We found a general underestimation of daytime LST and slightly overestimation at night-time. The results indicate that there is still room for improvement in the simulation of LST in ECMWF products. Still, ERA5 presents an overall higher quality product in relation to ERA-Interim. Our analysis suggested a relation between the large daytime cold bias and vegetation cover differences between (ERA5 and CGLS FCocver) with a correlation of −0.45. The replacement of the low and high vegetation cover by those of ESA-CCI provided an overall reduction of the large Tmax biases during summer. The increased vertical resolution of the soil at the surface, has a positive impact, but much smaller when compared with the vegetation changes. The sensitivity of the vegetation density parameter, that currently depends on the vegetation type, provided further proof for a needed revision of the vegetation in the model, as there is a reasonable correlation between this parameter and the Tmax mean errors when using the ESA-CCI vegetation cover (while the same correlation cannot be reproduced with the original model vegetation). Our results support the hypothesis that vegetation cover is one of the main drivers of the LST summertime cold bias in ERA5 over Iberian Peninsula. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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16 pages, 4545 KiB

Open AccessTechnical Note

Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

by Alessio Lattanzio, Michael Grant, Marie Doutriaux-Boucher, Rob Roebeling and Jörg Schulz

Remote Sens. 2021, 13(10), 1992; https://doi.org/10.3390/rs13101992 - 19 May 2021

Viewed by 2656

Abstract

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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