Retrievals of all-weather daytime air temperature from MODIS products

Zhu, Wenxiang; Lü, Aijun; Jia, Shaofeng; Yan, Jiabao; Mahmood, Rashid

doi:10.1016/j.rse.2016.11.011

Cited by 93 publications

(44 citation statements)

References 37 publications

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“…The coefficient of determination (R 2 ), MAE, RMSE, RRMSE and B are 0.94, 2.28 K, 3.02 K, 0.01 and 1.72 K, respectively. T a of the same 21 days in the SGP region was also estimated by Sun et al [50] using an empirical method proposed by Zaksek and Schroedter-Homscheidt [58] with a R 2 of 0.91, and RMSE of 3.1 K. Therefore, the accuracy of the algorithm developed by Zhu et al [49] applied in the SGP is acceptable.…”

Section: Estimation Of Near Surface Air Temperaturementioning

confidence: 97%

“…Specifically, MOD06_L2 product was used to retrieve land surface temperature, while MOD07_L2 product was used to retrieve atmosphere temperature profiles and surface air pressure. Details about these two products were given by Zhu et al [49].…”

Section: Study Area and Field Measurementsmentioning

confidence: 99%

“…Besides remotely sensed and VI, near surface air temperature ( ) is another variable needed to determine the TVX lines. A simple and operational algorithm was developed by Zhu et al [49] to retrieve the instantaneous for the Southern Great Plains (SGP) of the United States of America. They find that the systematic errors caused by near surface air temperature ( ) retrieved from MOD07_L2 product and retrieved from MOD06_L2 product are in completely different directions.…”

Section: Estimation Of Near Surface Air Temperaturementioning

confidence: 99%

See 2 more Smart Citations

A New Contextual Parameterization of Evaporative Fraction to Reduce the Reliance of the Ts − VI Triangle Method on the Dry Edge

Zhu

Jia

et al. 2017

Remote Sensing

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Abstract:In this study, a new parameterization scheme of evaporative fraction (EF) was developed from the contextual information of remotely sensed radiative surface temperature (T s ) and vegetation index (VI). In the traditional T s − V I triangle methods, the Priestley-Taylor parameter ∅ of each pixel was interpolated for each VI interval; in our proposed new parameterization scheme (NPS), it was performed for each isopiestic line of soil surface moisture. Specifically, ∅ of mixed pixels was determined as the weighted-average value of bare soil ∅ and full-cover vegetation ∅. The maximum T s of bare soil (T smax ) is the sole parameter needed as the constraint of the dry edge. This has not only bypassed the task involved in the determination of the maximum T s of fully vegetated surface (T cmax ), but also made it possible to reduce the reliance of the T s − V I triangle methods on the determination of the dry edge. Ground-based measurements taken during 21 days in 2004 were used to validate the EF retrievals. Results show that the accuracy achieved by the NPS is comparable to that achieved by the traditional T s − V I triangle methods. Therefore, the simplicity of the proposed new parameterization scheme does not compromise its accuracy in monitoring EF.

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Section: Estimation Of Near Surface Air Temperaturementioning

confidence: 97%

Section: Study Area and Field Measurementsmentioning

confidence: 99%

Section: Estimation Of Near Surface Air Temperaturementioning

confidence: 99%

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A New Contextual Parameterization of Evaporative Fraction to Reduce the Reliance of the Ts − VI Triangle Method on the Dry Edge

Zhu

Jia

et al. 2017

Remote Sensing

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“…As MODIS LST is only available under clear sky in the land products (MOD11 and MYD11), many previous studies [25,27,35,36,59] estimated Ta by integrating MODIS LST and station observations under clear sky. To estimate Ta under both clear and cloudy sky conditions, Zhu et al [16] used the clear sky LST from the MODIS cloud product (MOD06) and Ta from the MODIS atmosphere profile products (MOD07) to build the relationship between LST and Ta at 5-km scale, and then applied this relationship under cloudy sky. The LST provided by the MOD06 cloud product is a combination of data from various sources including MOD11 product, National Centers for Environmental Prediction (NCEP) gridded reanalysis and Data Assimilation Office (DAO) data [60].…”

Section: Impact Of Weather Condition On the Ta Estimation Accuracymentioning

confidence: 99%

“…For example, the atmospheric temperature and moisture profiles products (MOD07_L2/MYD07_L2) from the moderate-resolution imaging spectroradiometer (MODIS) are produced at 20 vertical levels by using synthetic regression retrieval algorithm under clear sky, in which the regression coefficients are derived using a fast radiative transfer model with atmospheric characteristics taken from a global profile dataset [14]. Bisht et al [15] and Zhu et al [16] obtained the Ta by using the temperature lapse rate method from the MODIS temperature profile products. The main problem of the direct inversion method is that the spatial resolution and inversion accuracy are relatively low and the lapse rate is assumed to be vertically constant which may be inconsistent with the actual situation [17].…”

Section: Introductionmentioning

confidence: 99%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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Development and evaluation of the MTVDI for soil moisture monitoring

Zhu

Sun

2017

JGR Atmospheres

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Several parameterization schemes have been developed to retrieve the soil moisture information involved in the remotely sensed surface temperature‐vegetation index (Ts − VI) space. However, most of them are performed with the constraint of the dry edge of the Ts − VI space to define the maximum water stressed conditions. In view of the subjectivity and uncertainty involved in the determination of the dry edge, a new index termed as the Modified Temperature‐Vegetation Dryness Index (MTVDI) was developed in this paper to reduce the reliance of the parameterization scheme on the dry edge. In the parameterization scheme of MTVDI, isopleth lines of soil moisture involved in the feature space were retrieved by the temperature‐vegetation index method, and only the maximum surface temperature of bare soil (Tsmax) was indispensable in the definition of maximum water stressed conditions. For evaluation purpose, the MTVDI was demonstrated in the Southern Great Plains region of the U.S. and was compared with two other traditional soil moisture indexes developed under the constraint of dry edge. The comparison confirmed the effectivity of the MTVDI in monitoring the spatial pattern and seasonal variation of soil moisture. Our analyses also suggest that Tsmax, the only parameter needed in the definition of maximum water stressed conditions, can be retrieved directly from the parameterization scheme itself. Therefore, the retrieval of MTVDI can be performed independent of the dry edge, which is a significant improvement to the traditional parameterization schemes of soil moisture from the Ts − VI feature space.

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Retrievals of all-weather daytime air temperature from MODIS products

Cited by 93 publications

References 37 publications

A New Contextual Parameterization of Evaporative Fraction to Reduce the Reliance of the Ts − VI Triangle Method on the Dry Edge

A New Contextual Parameterization of Evaporative Fraction to Reduce the Reliance of the Ts − VI Triangle Method on the Dry Edge

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

Development and evaluation of the MTVDI for soil moisture monitoring

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