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

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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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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Satellite Detection of Water Stress Effects on Terrestrial Latent Heat Flux With MODIS Shortwave Infrared Reflectance Data

Journal of Geophysical Research Atmospheres ◽

10.1029/2018jd029011 ◽

2018 ◽

Vol 123 (20) ◽

pp. 11,410-11,430 ◽

Cited By ~ 2

Author(s):

Yunjun Yao ◽

Shunlin Liang ◽

Bao Cao ◽

Shaomin Liu ◽

Guirui Yu ◽

...

Keyword(s):

Heat Flux ◽

Water Stress ◽

Latent Heat ◽

Latent Heat Flux ◽

Infrared Reflectance ◽

Stress Effects ◽

Shortwave Infrared ◽

Reflectance Data

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Characterizing the multi-scale spatial structure of land-atmosphere interactions with information theory

Biogeosciences Discussions ◽

10.5194/bgd-8-3435-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 3435-3462 ◽

Cited By ~ 1

Author(s):

N. A. Brunsell ◽

M. C. Anderson

Keyword(s):

Information Theory ◽

Heat Flux ◽

Spatial Structure ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Near Surface ◽

Multi Scale

Abstract. A more thorough understanding of the multi-scale spatial structure of land surface heterogeneity will enhance understanding of the relationships and feedbacks between land surface conditions, mass and energy exchanges between the surface and the atmosphere, and regional meteorological and climatological conditions. The objectives of this study were to (1) quantify which spatial scales are dominant in determining the evapotranspiration flux between the surface and the atmosphere and (2) to quantify how different spatial scales of atmospheric and surface processes interact for different stages of the phenological cycle. We used the ALEXI/DisALEXI model for three days (DOY 181, 229 and 245) in 2002 over the Ft. Peck Ameriflux site to estimate the latent heat flux from Landsat, MODIS and GOES satellites. We then applied a multiresolution information theory methodology to quantify these interactions across different spatial scales and compared the dynamics across the different sensors and different periods. We note several important results: (1) spatial scaling characteristics vary with day, but are usually consistent for a given sensor, but (2) different sensors give different scalings, and (3) the different sensors exhibit different scaling relationships with driving variables such as fractional vegetation and near surface soil moisture. In addition, we note that while the dominant length scale of the vegetation index remains relatively constant across the dates, but the contribution of the vegetation index to the derived latent heat flux varies with time. We also note that length scales determined from MODIS are consistently larger than those determined from Landsat. These results aid in identifying the dominant cross-scale nature of local to regional biosphere-atmosphere interactions.

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Estimation of terrestrial latent heat flux over Qilian Mountains by the fusion of five satellite-derived products using Extremely Randomized Trees

10.5194/egusphere-egu2020-2817 ◽

2020 ◽

Author(s):

Ke Shang ◽

Yunjun Yao ◽

Junming Yang ◽

Xiaowei Chen ◽

Xiangyi Bei ◽

...

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Qilian Mountains ◽

Fusion Method ◽

Extremely Randomized Trees ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

<p>The latent heat flux (LE) governs the associated heat flux from the interactions between the land surface and its atmosphere and plays an important role in the surface water and energy balance. The Qilian Mountains is the largest marginal mountain system in the northeast of the Qinghai-Tibet Plateau. An accurate representation of spatio-temporal patterns of LE over Qilian Mountains is essential in many terrestrial biosphere, hydrosphere, and atmosphere applications. Various satellite-derived LE products have been widely used to estimate terrestrial LE, yet each&#160;individual LE&#160;product exhibits large discrepancies. To reduce uncertainties from individual product and improve terrestrial LE estimation over Qilian Mountains, we produced five satellite-derived LE products using traditional algorithms based on Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, LAI products and China Meteorological Forcing Dataset (CMFD), and implemented the fusion of these five LE products using Extremely Randomized Trees (Extra-Trees) combining information from ground-based measurements. A validation using ground-based measurements was applied at different plant function types and the validation results demonstrate that the fusion product using Extra-Trees outperformed all the LE products used in the fusion method. Comparing with three other machine learning fusion models such as Gradient Boosting Regression Tree (GBRT), Random Forest (RF) and Gaussian Process Regression (GPR), Extra-Trees exhibits the best performance in terms of both training and validation accuracy. This fusion LE product also outperformed when compared against two state-of-the-art global LE products such as Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS). The fusion LE product showed improvements in the linear correlation, bias and RMSE at different plant function types. Our results suggest that the fusion method using Extra-Trees could be successfully applied to other region and to enhance the estimation of other hydrometeorological variables.</p>

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

Advances in Meteorology ◽

10.1155/2020/8857618 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Xiaowei Chen ◽

Yunjun Yao ◽

Shaohua Zhao ◽

Yufu Li ◽

Kun Jia ◽

...

Keyword(s):

Heat Flux ◽

Spatial Distribution ◽

High Resolution ◽

Latent Heat ◽

Latent Heat Flux ◽

Spatial Resolution ◽

Satellite Data ◽

High Spatial Resolution ◽

Accurate Estimation ◽

The Difference

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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Characterizing the multi–scale spatial structure of remotely sensed evapotranspiration with information theory

Biogeosciences ◽

10.5194/bg-8-2269-2011 ◽

2011 ◽

Vol 8 (8) ◽

pp. 2269-2280 ◽

Cited By ~ 19

Author(s):

N. A. Brunsell ◽

M. C. Anderson

Keyword(s):

Information Theory ◽

Heat Flux ◽

Spatial Structure ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Near Surface ◽

Multi Scale

Abstract. A more thorough understanding of the multi-scale spatial structure of land surface heterogeneity will enhance understanding of the relationships and feedbacks between land surface conditions, mass and energy exchanges between the surface and the atmosphere, and regional meteorological and climatological conditions. The objectives of this study were to (1) quantify which spatial scales are dominant in determining the evapotranspiration flux between the surface and the atmosphere and (2) to quantify how different spatial scales of atmospheric and surface processes interact for different stages of the phenological cycle. We used the ALEXI/DisALEXI model for three days (DOY 181, 229 and 245) in 2002 over the Ft. Peck Ameriflux site to estimate the latent heat flux from Landsat, MODIS and GOES satellites. We then applied a multiresolution information theory methodology to quantify these interactions across different spatial scales and compared the dynamics across the different sensors and different periods. We note several important results: (1) spatial scaling characteristics vary with day, but are usually consistent for a given sensor, but (2) different sensors give different scalings, and (3) the different sensors exhibit different scaling relationships with driving variables such as fractional vegetation and near surface soil moisture. In addition, we note that while the dominant length scale of the vegetation index remains relatively constant across the dates, the contribution of the vegetation index to the derived latent heat flux varies with time. We also note that length scales determined from MODIS are consistently larger than those determined from Landsat, even at scales that should be detectable by MODIS. This may imply an inability of the MODIS sensor to accurately determine the fine scale spatial structure of the land surface. These results aid in identifying the dominant cross-scale nature of local to regional biosphere-atmosphere interactions.

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Analyzing influence of spatial resolution on the estimated evapotranspiration by using remote sensing data over an oasis area in Northwestern China

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-1321-2009 ◽

2009 ◽

Vol 6 (1) ◽

pp. 1321-1345

Author(s):

H. Tian ◽

J. Wen ◽

Z. B. Su ◽

Y. M. Ma ◽

L. Wang ◽

...

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Spatial Resolution ◽

Land Surface ◽

Large Scale ◽

Northwestern China ◽

Retrieval Algorithm ◽

Modis Data ◽

Relative Errors

Abstract. In this paper, the influence of spatial resolution on the precision of estimates was analyzed through evapotranspiration (ET hereafter) modeling over a typical oasis in northwestern China by using the Landsat-TM and MODIS data. A relatively high consistency was observed between the TM-based latent heat flux and daily ET estimates and in-situ measurements, with relative errors of 9.7% and 8.8%, respectively. Despite lower precision of the relative errors of 22.4% and 17.0%, respectively, the MODIS-based latent heat flux and ET estimates can effectively depict the basic trend of the spatial distribution of the land surface processes. When the visible and near-infrared information of 250 m resolution was syncretized into MODIS LST retrieval algorithm, the precision of latent heat flux prediction was improved evidently. Additionally, the diurnal variation of the reference ET fraction shows that the temporal upscaling method of ET is suitable for the study area. In spite of suffering the influence of the heterogeneity of land surface, the moderate resolution MODIS data, combined with the parameterization model of land surface energy flux applied in this investigation, are suitable for the ET mapping at large scale while high-resolution data can serve as an important supplement.

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