Estimation of High-Resolution Global Monthly Ocean Latent Heat Flux from MODIS SST Product and AMSR-E Data

Accurate estimation of satellite-derived ocean latent heat flux (LHF) at high spatial resolution remains a major challenge. Here, we estimate monthly ocean LHF at 4 km spatial resolution over 5 years using bulk algorithm COARE 3.0, driven by satellite data and meteorological variables from reanalysis. We validated the estimated ocean LHF by multiyear observations and by comparison with seven ocean LHF products. Validation results from monthly observations at 96 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA, and RAMA) indicated a bias of less than 7 W/m2 with R2 of more than 0.80 (p<0.01) and with a King–Gupta efficiency (KGE) of over 0.84. Our estimated ocean LHF also performs well in simulating annual variability and predicting between-site variability, as indicated by a bias of lower than 6 W/m2 and an R2 of more than 0.84 (p<0.01). Overall, the average KGE for estimated ocean LHF increased by 18%–23% compared to other LHF products, indicating robust LHF estimation performance. Importantly, our estimated annual ocean LHF has similar global spatial distribution compared to other LHF products, although there are general differences in LHF values due to the difference in the models and the spatial resolution.

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Quantifying the uncertainty in estimates of surface–atmosphere fluxes through joint evaluation of the SEBS and SCOPE models

Hydrology and Earth System Sciences ◽

10.5194/hess-17-1561-2013 ◽

2013 ◽

Vol 17 (4) ◽

pp. 1561-1573 ◽

Cited By ~ 26

Author(s):

J. Timmermans ◽

Z. Su ◽

C. van der Tol ◽

A. Verhoef ◽

W. Verhoef

Keyword(s):

Remote Sensing ◽

Heat Flux ◽

Energy Balance ◽

Latent Heat ◽

Latent Heat Flux ◽

Estimation Error ◽

Accurate Estimation ◽

Area Index ◽

Ground Heat Flux ◽

Scope Model

Abstract. Accurate estimation of global evapotranspiration is considered to be of great importance due to its key role in the terrestrial and atmospheric water budget. Global estimation of evapotranspiration on the basis of observational data can only be achieved by using remote sensing. Several algorithms have been developed that are capable of estimating the daily evapotranspiration from remote sensing data. Evaluation of remote sensing algorithms in general is problematic because of differences in spatial and temporal resolutions between remote sensing observations and field measurements. This problem can be solved in part by using soil-vegetation-atmosphere transfer (SVAT) models, because on the one hand these models provide evapotranspiration estimations also under cloudy conditions and on the other hand can scale between different temporal resolutions. In this paper, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model is used for the evaluation of the Surface Energy Balance System (SEBS) model. The calibrated SCOPE model was employed to simulate remote sensing observations and to act as a validation tool. The advantages of the SCOPE model in this validation are (a) the temporal continuity of the data, and (b) the possibility of comparing different components of the energy balance. The SCOPE model was run using data from a whole growth season of a maize crop. It is shown that the original SEBS algorithm produces large uncertainties in the turbulent flux estimations caused by parameterizations of the ground heat flux and sensible heat flux. In the original SEBS formulation the fractional vegetation cover is used to calculate the ground heat flux. As this variable saturates very fast for increasing leaf area index (LAI), the ground heat flux is underestimated. It is shown that a parameterization based on LAI reduces the estimation error over the season from RMSE = 25 W m−2 to RMSE = 18 W m−2. In the original SEBS formulation the roughness height for heat is only valid for short vegetation. An improved parameterization was implemented in the SEBS algorithm for tall vegetation. This improved the correlation between the latent heat flux predicted by the SEBS and the SCOPE algorithm from −0.05 to 0.69, and led to a decrease in difference from 123 to 94 W m−2 for the latent heat flux, with SEBS latent heat being consistently lower than the SCOPE reference. Lastly the diurnal stability of the evaporative fraction was investigated.

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Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe

Remote Sensing ◽

10.3390/rs12040687 ◽

2020 ◽

Vol 12 (4) ◽

pp. 687 ◽

Cited By ~ 2

Author(s):

Ke Shang ◽

Yunjun Yao ◽

Yufu Li ◽

Junming Yang ◽

Kun Jia ◽

...

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Regression Tree ◽

Gaussian Process Regression ◽

Gradient Boosting ◽

Accurate Estimation ◽

Fusion Method ◽

Extremely Randomized Trees

An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows large discrepancies and uncertainties. Our study used Extremely Randomized Trees (ETR) to fuse five satellite-derived terrestrial LE products to reduce uncertainties from the individual products and improve terrestrial LE estimations over Europe. The validation results demonstrated that the estimation using the ETR fusion method increased the R2 of five individual LE products (ranging from 0.53 to 0.61) to 0.97 and decreased the RMSE (ranging from 26.37 to 33.17 W/m2) to 5.85 W/m2. Compared with three other machine learning fusion models, Gradient Boosting Regression Tree (GBRT), Random Forest (RF), and Gaussian Process Regression (GPR), ETR exhibited the best performance in terms of both training and validation accuracy. We also applied the ETR fusion method to implement the mapping of average annual terrestrial LE over Europe at a resolution of 0.05 ◦ in the period from 2002 to 2005. When compared with global LE products such as the Global Land Surface Satellite (GLASS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the fusion LE using ETR exhibited a relatively small gap, which confirmed that it is reasonable and reliable for the estimation of the terrestrial LE over Europe.

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A new method to measure bowen ratios using high resolution vertical dry and wet bulb temperature profiles

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-7161-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 7161-7196 ◽

Cited By ~ 1

Author(s):

T. Euser ◽

W. Luxemburg ◽

C. Everson ◽

M. Mengistu ◽

A. Clulow ◽

...

Keyword(s):

Heat Flux ◽

Surface Layer ◽

High Resolution ◽

Eddy Covariance ◽

Latent Heat ◽

Latent Heat Flux ◽

Bowen Ratio ◽

Temperature Profiles ◽

Fibre Optic ◽

Fibre Optic Cable

Abstract. The Bowen ratio surface energy balance method is a relatively simple method to determine the latent heat flux and the actual land surface evaporation. Despite its simplicity, the Bowen ratio method is generally considered to be unreliable due to the use of two-level sensors that are installed by default in operational Bowen ratio systems. In this paper we present the concept of a new measurement methodology to estimate the Bowen ratio from high resolution vertical dry and wet bulb temperature profiles. A short field experiment with Distributed Temperature Sensing (DTS) in a fibre optic cable having 13 levels was undertaken. A dry and a wetted section of a fibre optic cable were suspended on a 6 m high tower installed over a sugar beet trial near Pietermaritzburg (South Africa). Using the DTS cable as a psychrometer, a near continuous observation of vapour pressure and temperature at 0.20 m intervals was established. These data allows the computation of the Bowen ratio with a high precision. By linking the Bowen ratio to net radiation and soil heat flux, the daytime latent heat flux was estimated. The latent heat flux derived from DTS-based Bowen ratio (BR-DTS) showed consistent agreement (correlation coefficients between 0.97 and 0.98) with results derived from eddy covariance, surface layer scintillometer and surface renewal techniques. The latent heat from BR-DTS overestimated the latent heat derived with the eddy covariance by 4% and the latent heat derived with the surface layer scintillometer by 8%. Through this research, a new window is opened to engage on simplified, inexpensive and easy to interpret in situ measurement techniques for measuring evaporation.

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ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation

Remote Sensing ◽

10.3390/rs13183703 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3703

Author(s):

Lilin Zhang ◽

Yunjun Yao ◽

Xiangyi Bei ◽

Yufu Li ◽

Ke Shang ◽

...

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Spatial Resolution ◽

Spatial Scales ◽

Irrigation Management ◽

Fusion Model ◽

Spatiotemporal Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Reflectance Data

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.

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Retrieval of High Spatial Resolution Aerosol Optical Depth from Chinese gAofen Data for Australian bushfire

10.5194/egusphere-egu2020-3986 ◽

2020 ◽

Author(s):

Yong Xue

Keyword(s):

High Resolution ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Spatial Resolution ◽

Satellite Data ◽

High Spatial Resolution ◽

Experimental Results ◽

Observation System ◽

Observation Data ◽

Surface Reflectance

<p>Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. However, the spatial resolution of AOD, based on Moderate-resolution Imaging Spectroradiometer (MODIS), is low, and hard to meet the needs of regional air quality fine monitoring. In 2018, China launched Gaofen-6 satellite, which set up a network with Gaofen-1 enabling two-day revisited observations in China's land area, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. Along with advancement of the Earth Observation System and the launch of high-resolution satellites, it is of profound significance to give full play to the active role of high-scoring satellites, in monitoring atmospheric environmental elements such as atmospheric aerosols and particulate matter concentrations, and achieve high-resolution retrieval of AOD through Gaofen satellites.</p><p>In this paper the data of Gaofen-6 and Gaofen-1 was used to retrieve the AOD. based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm. This algorithm can retrieve the surface reflectance and AOD synchronously through constructing a closed equation based on double star observations. It can be applied to various types of surface reflectance which extends the coverage of the retrieval of AOD inversion effectively. Experimental data includes the satellite data of New South Wales and eastern Queensland on November 21, 2019, which have been suffered from unprecedented large-scale forest fires for over 2 months. The retrieval of AOD during the time with the satellite data is benefit for the prevention and monitoring of forest fire. The experimental results are compared with the AERONET ground observation data for preliminary validation. The correlation coefficient is about 0.7. The experimental results show that the method have higher accuracy, and further validation work is continuing.</p>

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Moisture and latent heat flux variabilities in the tropical Pacific derived from satellite data

Journal of Geophysical Research Atmospheres ◽

10.1029/jc093ic06p06749 ◽

1988 ◽

Vol 93 (C6) ◽

pp. 6749 ◽

Cited By ~ 94

Author(s):

W. Timothy Liu

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Satellite Data ◽

Tropical Pacific ◽

The Tropical Pacific

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Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data

Sensors ◽

10.3390/s20102811 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2811

Author(s):

Xiangyi Bei ◽

Yunjun Yao ◽

Lilin Zhang ◽

Yi Lin ◽

Shaomin Liu ◽

...

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Spatial Resolution ◽

Water Resource Management ◽

Vegetation Indices ◽

Great Promise ◽

Wide Field ◽

Spatiotemporal Resolution ◽

High Spatiotemporal Resolution

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

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Estimation of crop latent heat flux from high resolution thermal imagery

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV ◽

10.1117/12.2519216 ◽

2019 ◽

Author(s):

Yan Zhu ◽

Keith Cherkauer

Keyword(s):

Heat Flux ◽

High Resolution ◽

Latent Heat ◽

Latent Heat Flux ◽

Thermal Imagery

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High-resolution land surface fluxes from satellite and reanalysis data (HOLAPS v1.0): evaluation and uncertainty assessment

Geoscientific Model Development ◽

10.5194/gmd-9-2499-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2499-2532 ◽

Cited By ~ 11

Author(s):

Alexander Loew ◽

Jian Peng ◽

Michael Borsche

Keyword(s):

Heat Flux ◽

High Resolution ◽

Surface Energy ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Reanalysis Data ◽

Surface Radiation ◽

Water Fluxes ◽

Essential Components

Abstract. Surface water and energy fluxes are essential components of the Earth system. Surface latent heat fluxes provide major energy input to the atmosphere. Despite the importance of these fluxes, state-of-the-art data sets of surface energy and water fluxes largely differ. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high-resolved flux estimates at the quasi-global scale (50° S, 50° N) (High resOlution Land Atmosphere Parameters from Space – HOLAPS v1.0). The framework makes use of existing long-term satellite and reanalysis data records and ensures internally consistent estimates of the surface radiation and water fluxes. The manuscript introduces the technical details of the developed framework and provides results of a comprehensive sensitivity and evaluation study. Overall the root mean square difference (RMSD) was found to be 51.2 (30.7) W m−2 for hourly (daily) latent heat flux, and 84 (38) W m−2 for sensible heat flux when compared against 48 FLUXNET stations worldwide. The largest uncertainties of latent heat flux and net radiation were found to result from uncertainties in the solar radiation flux obtained from satellite data products.

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