Comparison of Two Satellite‐Based Evapotranspiration Models of the Nagqu River Basin of the Tibetan Plateau

Zou, Minmin; Zhong, Lei; Ma, Yaoming; Hu, Yuanyuan; Huang, Zhengyi; Xu, Kepiao; Lu, Feng

doi:10.1002/2017jd027965

Cited by 23 publications

(12 citation statements)

References 81 publications

(91 reference statements)

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“…Based on the original 10‐day MVC synthetic product, a new time series of a cloud‐free data set was constructed by applying the HANTS algorithm. In the HANTS algorithm, cloud removal is combined with a smoothing process using a Fourier analysis, and those pixels identified as cloudy are replaced by a filtered value (Zou et al, ). Details on the HANTS algorithm can be found in Verhoef ().…”

Section: Methodsmentioning

confidence: 99%

Climate Change Trends and Impacts on Vegetation Greening Over the Tibetan Plateau

et al. 2019

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The Tibetan Plateau (TP) is an ecologically fragile region that is sensitive to climate change. In the context of global climate change, the climate change trends of the TP and the vegetation dynamic response need to be investigated. Based on in situ meteorological data, Satellite Pour l'Observation de la Terre vegetation data, and Moderate Resolution Imaging Spectroradiometer land cover data, a comprehensive analysis was conducted to determine the trends of climate parameters in the TP region at different time scales (long term: 1960–2014; midterm: 1980–2014; short term 1999–2014). A consistent warming trend was observed for different temporal scales, while a warming slowdown was identified during 1999 and 2014. The warming rate was also shown to be much higher in the high‐altitude regions (>4,000 m), especially at midterm and short‐term time scales. A new cloud‐free time series of vegetation index data sets was reconstructed, and the vegetation density showed a general increasing trend along with a warming trend in the TP. The regions showing significant increases accounted for 7.63% of the total Tibetan territory. The major significant greening trend of the TP was mainly caused by climate factors. The reforestation projects may have played a minor role in the vegetation greening in specific regions of the TP. In addition, various vegetation types showed markedly different responses to climate changes. The grassland in semiarid regions, which accounted for 41.9% of the territory of the TP, was identified to be very sensitive to variations in both temperature and precipitation.

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Section: Methodsmentioning

confidence: 99%

Climate Change Trends and Impacts on Vegetation Greening Over the Tibetan Plateau

et al. 2019

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“…The forcing data were produced by merging a variety of data sources, including data from the China Meteorological Administration station, TRMM satellite, GEWEX Surface Radiation Budget (GEWEX‐SRB), and the Global Land Data Assimilation System (Ma et al, ). Additionally, some in situ measurements and independent data sets have been used to evaluate the forcing data (Chen et al, ; Zou, Zhong, Ma, Hu, Huang, et al, ). In this study, the CMFD radiation product was also selected an existing product for intercomparison.…”

Section: Datamentioning

confidence: 99%

Estimation of Downwelling Shortwave and Longwave Radiation in the Tibetan Plateau Under All‐Sky Conditions

Zhong

Zou

et al. 2019

JGR Atmospheres

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Downwelling shortwave radiation (DSWR) and downwelling longwave radiation (DLWR) are two important components of the Earth's surface radiation balance. In this study, the Heliosat method and the parameterization of Crawford and Duchon (1999, https://doi.org/10.1175/1520‐0450(1999)038<0474:AIPFEE>2.0.CO;2, hereafter CD99) were calibrated to make them suitable for estimation of DSWR and DLWR over the Tibetan Plateau (TP). Based on meteorological data, forcing data, and observations from polar‐orbiting satellites, the cloud albedo was calculated, and the clear‐sky index estimation scheme of the Heliosat method was improved. These improvements were then applied to derive 10‐day DSWR under all‐sky conditions over the TP by combining the clear‐sky shortwave radiation scheme with a clear‐sky index. The coefficient of the CD99 parameterization scheme clear‐sky DLWR was also calibrated, and 10‐day all‐sky DLWR was then determined and validated using ground‐based measurements. The spatiotemporal distributions of DSWR and DLWR were analyzed in detail. The results showed that the modified methods are efficient and applicable for downward radiation retrieval under all‐sky conditions over the TP with a reasonable accuracy. The mean percentage errors for DSWR and DLWR were −4.75% and 0.11%, respectively. The variation in the monthly DSWR (DLWR) showed a convex shape, with a maximum appearing in May (July). The spatial distributions of DLWR showed a southeast‐high and northwest‐low pattern. As the subsolar point moves northward, DSWR increases gradually and is clearly influenced by the Asian summer monsoon.

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“…The transformed NDVI or transformed squared NDVI might also overestimate or underestimate the fractional vegetation cover [41]. However, a recent study by Zou et al [42] found that the land surface heat fluxes were less sensitive to NDVI than the temperature-related variables and wind speed (~0.4% changes in land surface temperature would cause~11% changes in ET, while~10% changes in NDVI would only causẽ 0.2% changes in ET). Nevertheless, it is really a good suggestion for us to implement spectral mixture analysis methods in our future work to improve the accuracy of surface heat flux estimation.…”

Section: Discussionmentioning

confidence: 99%

“…The ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) is designed to accurately measure the land surface temperature and evaporative stress index, which may substantially improve our understanding of energy and water transfer in the atmospheric boundary layer. Since the land surface temperature is one of the most important parameters in surface heat flux estimation and the evapotranspiration was estimated to be the most sensitive to land surface temperature [42], the potentially higher accuracy of land surface temperature measurement may further reduce the errors in energy flux retrievals. The Sentinel-3 is aimed at measurements of land surface temperature, sea surface temperature, sea surface topography, and sea surface color with high accuracy, which may be very useful for the comprehensive study of land-atmosphere and sea-atmosphere interactions.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Land Surface Heat Fluxes Based on Landsat 7 ETM+ Data and Field Measurements over the Northern Tibetan Plateau

Gao

Zhong

et al. 2019

Remote Sensing

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Land surface heat fluxes consist of the net radiation flux, soil heat flux, sensible heat flux, and latent heat flux. The estimation of these fluxes is essential to the study of energy transfer in land–atmosphere systems. In this paper, Landsat 7 ETM+ SLC-on data were applied to estimate the land surface heat fluxes on the northern Tibetan Plateau using the SEBS (surface energy balance system) model, in combination with the calculation of field measurements at CAMP/Tibet (Coordinated Enhanced Observing Period (CEOP) Asia–Australia Monsoon Project on the Tibetan Plateau) automatic weather stations based on the combinatory method (CM) for comparison. The root mean square errors between the satellite estimations and the CM calculations for the net radiation flux, soil heat flux, sensible heat flux, and latent heat flux were 49.2 W/m2, 46.3 W/m2, 68.2 W/m2, and 54.9 W/m2, respectively. The results reveal that land surface heat fluxes all present significant seasonal variability. Apart from the sensible heat flux, the satellite-estimated net radiation flux, soil heat flux, and latent heat flux exhibited a trend of summer > spring > autumn > winter. In summer, spring, autumn, and winter, respectively, the median values of the net radiation flux (631.8 W/m2, 583.0 W/m2, 404.4 W/m2, 314.3 W/m2), soil heat flux (40.9 W/m2, 37.9 W/m2, 26.1 W/m2, 20.5 W/m2), sensible heat flux (252.7 W/m2, 219.5 W/m2, 221.4 W/m2, 204.8 W/m2), and latent heat flux (320.1 W/m2, 298.3 W/m2, 142.3 W/m2, 75.5 W/m2) exhibited distinct seasonal diversity. From November to April, the in situ sensible heat flux is higher than the latent heat flux; the opposite is true between June and September, leaving May and October as transitional months. For water bodies, alpine meadows and other main underlying surface types, sensible and latent heat flux generally present contrasting and complementary spatial distributions. Due to the 15–60 m resolution of the Landsat 7 ETM+ data, the distribution of land surface heat fluxes can be used as an indicator of complex underlying surface types over the northern Tibetan Plateau.

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Comparison of Two Satellite‐Based Evapotranspiration Models of the Nagqu River Basin of the Tibetan Plateau

Cited by 23 publications

References 81 publications

Climate Change Trends and Impacts on Vegetation Greening Over the Tibetan Plateau

Climate Change Trends and Impacts on Vegetation Greening Over the Tibetan Plateau

Estimation of Downwelling Shortwave and Longwave Radiation in the Tibetan Plateau Under All‐Sky Conditions

Estimation of Land Surface Heat Fluxes Based on Landsat 7 ETM+ Data and Field Measurements over the Northern Tibetan Plateau

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