scholarly journals Variational Estimation of Land–Atmosphere Heat Fluxes and Land Surface Parameters Using MODIS Remote Sensing Data

2013 ◽  
Vol 14 (2) ◽  
pp. 608-621 ◽  
Author(s):  
Chunlei Meng ◽  
Chaolin Zhang ◽  
Ronglin Tang
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Energy Balance Equation ◽  
Heat Fluxes ◽  
Area Index ◽  
Common Land Model ◽  
Modis Lai

Abstract A variational data assimilation algorithm for assimilating the land surface temperature (LST) into the Common Land Model (CLM) was implemented using the land surface energy balance equation as the adjoint physical constraint. In this data assimilation algorithm, the evaporative fractions of the soil and canopy were adjusted according to the remotely sensed surface temperature observations. This paper developed a very simple analytical algorithm to characterize the errors’ weighting matrices in the cost function. The leaf area index (LAI) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) was also assimilated into CLM using the direct insertion method. The analysis results from the CLM with the LST assimilation algorithm compare well with MODIS and field observations for the Yucheng site, especially in daytime. On the basis of the histogram of the error of the LST, it can be concluded that after assimilation, the LST was greatly improved in comparison with the MODIS observations, especially in daytime. These results indicate that this surface temperature assimilation method is efficient and effective, even when only one time observational LST data point is available for each day, especially in daytime. The regional spatial patterns of evapotranspiration and soil surface moisture were also compared before assimilation on the basis of LAI data calculated using the empirical formula, before assimilation on the basis of MODIS LAI data, and after assimilation on the basis of MODIS LAI data.

scholarly journals Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model

2011 ◽  
Vol 12 (2) ◽  
pp. 227-244 ◽  
Author(s):  
Tongren Xu ◽  
Shaomin Liu ◽  
Shunlin Liang ◽  
Jun Qin
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Heat Fluxes ◽  
Model Parameters ◽  
Modis Land Surface Temperature ◽  
Common Land Model ◽  
The Common

Abstract Four data assimilation scheme combinations derived from two strategies and two optimization algorithms [the ensemble Kalman filter (EnKF) and the shuffled complex evolution method developed at The University of Arizona (SCE-UA)] are developed based on the Common Land Model (CLM) to improve predictions of water and heat fluxes. The first strategy is constructed through adjusting the soil temperature, while the second strategy adjusts the soil moisture. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are compared with ground-measured surface temperature, and assimilated into the CLM. The relationship equation between the MODIS LST products and CLM surface temperature is taken as the observation operator and the root-mean-square error (RMSE) is applied as the observation error. The assimilation results are validated by measurements from six observation sites located in Germany, the United States, and China. Results indicate that the developed data assimilation schemes can improve estimates of water and heat fluxes. Overall, strategy 2 is superior to strategy 1 when using the same optimization algorithm. The EnKF algorithm performs slightly better than the SCE-UA algorithm when using the same strategy. Strategy 2 combined with the EnKF algorithm performs best for water and heat fluxes, and the reductions in the RMSE are found to be 24.0 and 15.2 W m−2 for sensible and latent heat fluxes, respectively. The joint assimilation of the MODIS LST and soil moisture observations can produce better results for strategy 2 with the SCE-UA. Since preprocessing model parameters are used in this study, the uncertainties in the model parameters may have resulted in suboptimal assimilation results. Therefore, model calibrations should be conducted in the future.

scholarly journals Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman–Monteith Model to Meteorological and Land Surface Input Variables

2021 ◽  
Vol 13 (5) ◽  
pp. 882
Author(s):  
Nobuhle P. Majozi ◽  
Chris M. Mannaerts ◽  
Abel Ramoelo ◽  
Renaud Mathieu ◽  
Wouter Verhoef
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temporal Frequency ◽  
Vapour Pressure Deficit ◽  
Remote Sensing Data ◽  
Area Index ◽  
Input Variables

This study analysed the uncertainty and sensitivity of core and intermediate input variables of a remote-sensing-data-based Penman–Monteith (PM-Mu) evapotranspiration (ET) model. We derived absolute and relative uncertainties of core measured meteorological and remote-sensing-based atmospheric and land surface input variables and parameters of the PM-Mu model. Uncertainties of important intermediate data components (i.e., net radiation and aerodynamic and surface resistances) were also assessed. To estimate the instrument measurement uncertainties of the in situ meteorological input variables, we used the reported accuracies of the manufacturers. Observational accuracies of the remote sensing input variables (land surface temperature (LST), land surface emissivity (εs), leaf area index (LAI), land surface albedo (α)) were derived from peer-reviewed satellite sensor validation reports to compute their uncertainties. The input uncertainties were propagated to the final model’s evapotranspiration estimation uncertainty. Our analysis indicated relatively high uncertainties associated with relative humidity (RH), and hence all the intermediate variables associated with RH, like vapour pressure deficit (VPD) and the surface and aerodynamic resistances. This is in contrast to other studies, which reported LAI uncertainty as the most influential. The semi-arid conditions and seasonality of the regional South African climate and high temporal frequency of the variations in VPD, air and land surface temperatures could explain the uncertainties observed in this study. The results also showed the ET algorithm to be most sensitive to the air-land surface temperature difference. An accurate assessment of those in situ and remotely sensed variables is required to achieve reliable evapotranspiration model estimates in these generally dry regions and climates. A significant advantage of the remote-sensing-based ET method remains its full area coverage in contrast to classic-point (station)-based ET estimates.

Progress in integrating remote sensing data and hydrologic modeling

2014 ◽  
Vol 38 (4) ◽  
pp. 464-498 ◽  
Author(s):  
Xiaoyong Xu ◽  
Jonathan Li ◽  
Bryan A. Tolson
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Hydrologic Modeling ◽  
Snow Water Equivalent ◽  
Research Effort ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data ◽  
The Past

Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There has been an intensive global research effort to integrate remote sensing and land/hydrologic modeling over the past few decades. In particular, in recent years significant progress has been made in land/hydrologic remote sensing data assimilation. Hence there is a demand for an up-to-date review on these efforts. This paper presents an overview of research efforts to combine hydrologic/land models and remote sensing products (mainly including precipitation, surface soil moisture, snow cover, snow water equivalent, leaf area index, and evapotranspiration) over the past decade. This paper also discusses the major challenges remaining in this field, and recommends the directions for further research efforts.

scholarly journals One-dimensional soil temperature assimilation experiment based on unscented particle filter and Common Land Model

2018 ◽  
Vol 38 ◽  
pp. 03009
Author(s):  
Xiao Lei Fu ◽  
Bao Ming Jin ◽  
Xiao Lei Jiang ◽  
Cheng Chen
Keyword(s):  
Probability Distribution ◽  
Surface Temperature ◽  
Soil Temperature ◽  
Particle Filter ◽  
Land Surface ◽  
Prediction Accuracy ◽  
Soil Surface ◽  
Observation Error ◽  
Unscented Particle Filter ◽  
Common Land Model

Data assimilation is an efficient way to improve the simulation/prediction accuracy in many fields of geosciences especially in meteorological and hydrological applications. This study takes unscented particle filter (UPF) as an example to test its performance at different two probability distribution, Gaussian and Uniform distributions with two different assimilation frequencies experiments (1) assimilating hourly in situ soil surface temperature, (2) assimilating the original Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) once per day. The numerical experiment results show that the filter performs better when increasing the assimilation frequency. In addition, UPF is efficient for improving the soil variables (e.g., soil temperature) simulation/prediction accuracy, though it is not sensitive to the probability distribution for observation error in soil temperature assimilation.

scholarly journals Some practical notes on the land surface modeling in the Tibetan Plateau

2009 ◽  
Vol 6 (1) ◽  
pp. 1291-1320 ◽  
Author(s):  
K. Yang ◽  
Y.-Y. Chen ◽  
J. Qin
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
Soil Surface ◽  
Bare Soil ◽  
Heat Fluxes ◽  
The Tibetan Plateau ◽  
Central Plateau ◽  
Alpine Meadows ◽  
Common Land Model

Abstract. The Tibetan Plateau is a key region of land-atmosphere interactions, as it provides an elevated heat source to the middle-troposphere. The Plateau surfaces are typically characterized by alpine meadows and grasslands in the central and eastern part while by alpine deserts in the western part. This study evaluates performance of three state-of-the-art land surface models (LSMs) for the Plateau typical land surfaces. The LSMs of interest are SiB2 (the Simple Biosphere), CoLM (Common Land Model), and Noah. They are run with default parameters at typical alpine meadow sites in the central Plateau and typical alpine desert sites in the western Plateau. The recognized key processes and modeling issues are as follows. First, soil stratification is a typical phenomenon beneath the alpine meadows, with dense roots and soil organic matters within the topsoil, and it controls the profile of soil moisture in the central and eastern Plateau; all models significantly under-estimate the soil moisture within the topsoil. Second, a soil surface resistance controls the surface evaporation from the alpine deserts but it has not been reasonably modeled in LSMs; a new scheme is proposed to determine this resistance from soil water content. Third, an excess resistance controls sensible heat fluxes from dry bare-soil or sparsely vegetated surfaces, and all LSMs significantly under-predict the ground-air temperature difference in the daytime. A parameterization scheme for this resistance has been shown effective to remove this bias.

scholarly journals Shallow groundwater effect on land surface temperature and surface energy balance under bare soil conditions: modeling and description

2012 ◽  
Vol 16 (7) ◽  
pp. 1817-1831 ◽  
Author(s):  
F. Alkhaier ◽  
G. N. Flerchinger ◽  
Z. Su
Keyword(s):  
Energy Balance ◽  
Surface Temperature ◽  
Land Surface ◽  
Shallow Groundwater ◽  
Bare Soil ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Soil Conditions ◽  
Potential Evaporation ◽  
Available Energy

Abstract. Understanding when and how groundwater affects surface temperature and energy fluxes is significant for utilizing remote sensing in groundwater studies and for integrating aquifers within land surface models. To investigate the shallow groundwater effect under bare soil conditions, we numerically exposed two soil profiles to identical metrological forcing. One of the profiles had shallow groundwater. The different responses that the two profiles manifested were inspected regarding soil moisture, temperature and energy balance at the land surface. The findings showed that the two profiles differed in three aspects: the absorbed and emitted amounts of energy, the portioning out of the available energy and the heat fluency in the soil. We concluded that due to their lower albedo, shallow groundwater areas reflect less shortwave radiation and consequently get a higher magnitude of net radiation. When potential evaporation demand is sufficiently high, a large portion of the energy received by these areas is consumed for evaporation. This increases the latent heat flux and reduces the energy that could have heated the soil. Consequently, lower magnitudes of both sensible and ground heat fluxes are caused to occur. The higher soil thermal conductivity in shallow groundwater areas facilitates heat transfer between the top soil and the subsurface, i.e. soil subsurface is more thermally connected to the atmosphere. For the reliability of remote sensors in detecting shallow groundwater effect, it was concluded that this effect can be sufficiently clear to be detected if at least one of the following conditions occurs: high potential evaporation and high contrast between day and night temperatures. Under these conditions, most day and night hours are suitable for shallow groundwater depth detection.

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