Evaluations of MODIS and microwave based satellite evapotranspiration products under varied cloud conditions over East Asia forests

Wang, Yipu; Hu, Jiheng; Wang, Xuewen; Kabeja, Crispin; Min, Qilong; Wang, Yu

doi:10.1016/j.rse.2021.112606

Cited by 19 publications

(11 citation statements)

References 98 publications

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“…The MLSE results in this study were successfully used in the retrieval of vegetation water content [70], the estimation of water vapor evapotranspiration rate in mid-latitude forests [15,16,71], and the volatile organic gas emissions [17]. The influence of clouds on the evapotranspiration process has also been studied using MLSE-based products [72]. The above studies have proved that the all-weather MLSE obtained by this research has great application prospects in the field of vegetation-atmosphere interaction research.…”

Section: Potential Applicationsmentioning

confidence: 63%

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Zhang

et al. 2021

Remote Sensing

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Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

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Section: Potential Applicationsmentioning

confidence: 63%

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Zhang

et al. 2021

Remote Sensing

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“…Similarly, even for ET NOAH and ET CLSM with the same GLDAS forcing data, the two ET perform a large discrepancy under clouds (Figures 1f and 1g) and respond differently to the drivers over cloudy areas (Figures 4f and 4g). These inconsistent performances suggest that the errors in forcing inputs could be counteracted by those in processed‐based model physics under the changing cloud conditions, which could be related to the representation of energy partitioning and canopy conductance schemes affected by clouds (Lowman & Godoy, 2020; Y. Wang et al., 2021b). At this point, the errors remain and are not yet answered in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Owing to the rapid cloud formation and transformation, cloud‐induced noises in ET estimation are typically noticeable at a short‐term scale (e.g., subdaily, daily and 8 day) (Y. Wang et al., 2022), while those would be filtered by time averaging at longer temporal scales. Moreover, cloud‐caused errors in ET at a short‐term scale can be propagated and accumulated at a longer temporal scale (Y. Wang et al., 2021b), leading to the bias understanding of ET trend analysis. Therefore, evaluating ET errors due to clouds is important and fundamental.…”

Section: Introductionmentioning

confidence: 99%

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Increasing Cloud Coverage Deteriorates Evapotranspiration Estimating Accuracy From Satellite, Reanalysis and Land Surface Models Over East Asia

Wang

Song

et al. 2023

Geophysical Research Letters

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Terrestrial evapotranspiration (ET) returns more than 2/3 land precipitation to the atmosphere through vegetation transpiration and evaporation (Oki & Kanae, 2006). This process links carbon, water and energy cycles across the biosphere, atmosphere and hydrosphere (Bonan, 2008). Thus, accurate estimation of regional ET is fundamental to improving water resource management, monitoring ecosystem droughts (

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“…The monthly PET from MODIS-16 PET was derived using Penman-Monteith-based algorithm supported by the use of MODIS-LAI [50]. A study in East Asia [64] highlighted the role of no-to-small cloud fraction areas in the overestimation of MODIS-16 ET, due to the overestimation of LAI in these areas. This overestimation is more severe in tropical regions than in higher latitude areas.…”

Section: Discussionmentioning

confidence: 99%

An Application of Improved MODIS-Based Potential Evapotranspiration Estimates in a Humid Tropic Brantas Watershed—Implications for Agricultural Water Management

Astuti

Wiwoho

Purwanto

et al. 2022

IJGI

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The reliance on native MODIS-16 PET potential evapotranspiration (PET) in scarce-data-driven areas is growing in support among ecohydrological studies, yet information about its performance is limited or unknown as validation studies are mostly concentrated in developed countries. This study aimed to assess its performance at the monthly level using four ground measurements in a tropical watershed system with complex topography, applying a machine learning artificial neural network (ANN) to improve the estimates, and using the ANN-adjusted MODIS-16 PET to characterize the spatio-temporal patterns of PET in the Brantas watershed, as well as to understand the monthly patterns of water deficiency in areas under eight different vegetation covers. The results showed that the native MODIS-16 PET experienced overestimation with an RMSE of 37–66 mm/month and NRSME of up to 33%. The performance decreased in drier periods. The ANN-based adjustment using only one variable showed improved estimates with a reduction of RSME to only 14 mm and lower than 10% NRMSE. Sari-temporal patterns of PET in the Brantas watershed showed that the PET characteristics were not uniform. The southern part of the Brantas watershed has areas with relatively lower PET that are, thus, more prone to water deficiency. Complex topography and climate gradients within the watershed apparently became the multi-controllers of PET variations. The difference in vegetation cover also influenced the magnitudes of water deficiency.

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Evaluations of MODIS and microwave based satellite evapotranspiration products under varied cloud conditions over East Asia forests

Cited by 19 publications

References 98 publications

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Increasing Cloud Coverage Deteriorates Evapotranspiration Estimating Accuracy From Satellite, Reanalysis and Land Surface Models Over East Asia

An Application of Improved MODIS-Based Potential Evapotranspiration Estimates in a Humid Tropic Brantas Watershed—Implications for Agricultural Water Management

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