Estimating surface energy fluxes using a dual‐source data assimilation approach adjoined to the heat diffusion equation

Bateni, Sayed M.; Liang, Shunlin

doi:10.1029/2012jd017618

Cited by 40 publications

(80 citation statements)

References 72 publications

(121 reference statements)

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“…have been developed during the last two decades [35][36][37][38]. Data assimilation methods have been used to generate temporal continuous soil moisture, soil temperature, surface fluxes, NDVI and LAI, by assimilating remote sensing data into land surface models [39][40][41][42][43][44][45][46]. For surface flux gap filling, a data assimilation scheme has been developed to reconstruct surface flux data from eddy covariance measurements [47].…”

Section: Introductionmentioning

confidence: 99%

Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

Liu

et al. 2015

Remote Sensing

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Currently, thermal remote sensing-based evapotranspiration (ET) models can only calculate instantaneous ET at the time of satellite overpass. Five temporal upscaling methods, namely, constant evaporative fraction (ConEF), corrected ConEF (CorEF), diurnal evaporative fraction (DiEF), constant solar radiation ratio (SolRad), and constant reference evaporative fraction (ConETrF), were selected to upscale the instantaneous ET to daily values. Moreover, five temporal reconstruction approaches, namely, data assimilation (ET_EnKF and ET_SCE_UA), surface resistance (ET_SR), reference evapotranspiration (ET_ETrF), and harmonic analysis of time series (ET_HANTS), were used to produce continuous daily ET with discrete clear-sky daily ET values. For clear-sky daily ET generation, SolRad and ConETrF produced the best estimates. In contrast, ConEF usually underestimated the daily ET. The optimum method, however, was found by combining SolRad and ConETrF, which produced the lowest root-mean-square error (RMSE) values. OPEN ACCESSRemote Sens. 2015, 7 3401For continuous daily ET production, ET_ETrF and ET_SCE_UA performed the best, whereas the ET_SR and ET_HANTS methods had large errors. The annual ET distributions over the Beijing area were calculated with these methods. The spatial ET distributions from ET_ETrF and ET_SCE_UA had the same trend as ETWatch products, and had a smaller RMSE when compared with ET observations derived from the water balance method.

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Section: Introductionmentioning

confidence: 99%

Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

Liu

et al. 2015

Remote Sensing

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“…Bateni & Liang, 2012;Lu et al, 2016), intercomparing different data assimilation techniques(Xu et al, 2019) and assessing the benefit of model parameter calibration(Koster et al, 2018).…”

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confidence: 99%

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

Dong

Crow

Reichle

et al. 2019

Geophysical Research Letters

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The Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, a quantitative estimate of the relative skill of SMAP Level‐4 and model‐only surface soil moisture (vs. true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high‐quality, ground‐based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data‐rich areas like the United States and Europe. In contrast, much larger improvement is observed in data‐sparse regions, including much of Africa and central Australia, where model‐only simulations are disproportionately impacted by low‐quality model forcing. Therefore, ground validation conducted in data‐rich areas does not adequately sample the added value of SMAP data assimilation for data‐sparse regions and substantially underestimates the added skill provided by the SMAP level 4 system.

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“…• empirical methods that involve the use of statistically-derived relationships between ET and vegetation indices such as the normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) [20][21][22][23][24][25], • residual methods of surface energy balance (single-and dual-source models) [8,26] which include the Surface Energy Balance Algorithm over Land (SEBAL) [27,28], Surface Energy Balance System (SEBS) [8,29,30] and Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) [6,31,32], • physically-based methods that involve the application of the combination of Penman-Monteith [7,33,34] and Priestley-Taylor types of equations [35][36][37][38][39], and • Data assimilation methods adjoined to the heat diffusion equation [40] and through the radiometric surface temperature sequences [41].…”

mentioning

confidence: 99%

Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa

et al. 2014

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Globally, water is an important resource required for the survival of human beings. Water is a scarce resource in the semi-arid environments, including South Africa. In South Africa, several studies have quantified evapotranspiration (ET) in different ecosystems at a local scale. Accurate spatially explicit information on ET is rare in the country mainly due to lack of appropriate tools. In recent years, a remote sensing ET product from the MODerate Resolution Imaging Spectrometer (MOD16) has been developed. However, its accuracy is not known in South African ecosystems. The objective of this study was to validate the MOD16 ET product using data from two eddy covariance flux towers, namely; Skukuza and Malopeni installed in a savanna and woodland ecosystem within the Kruger National Park, South Africa. Eight day cumulative ET data from the flux towers was calculated to coincide with the eight day MOD16 products over a period of 10 years from 2000 to 2010. The Skukuza flux tower results showed inconsistent comparisons with MOD16 ET. The Malopeni site achieved a poorer comparison with MOD16 ET compared to the Skukuza, and due to a shorter measurement period, data validation was performed for 2009 only. The inconsistent comparison of MOD16 and flux tower-based ET can be attributed to, among other things, the parameterization of the Penman-Monteith model, flux tower measurement errors, and flux tower footprint vs. OPEN ACCESSRemote Sens. 2014, 6 7407 MODIS pixel. MOD16 is important for global inference of ET, but for use in South Africa's integrated water management, a locally parameterized and improved product should be developed.

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Estimating surface energy fluxes using a dual‐source data assimilation approach adjoined to the heat diffusion equation

Cited by 40 publications

References 72 publications

Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa

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