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Abstract: This paper develops a statistical regression method to estimate the instantaneous Downwelling Surface Longwave Radiation (DSLR) for cloud-free skies using only the satellite-based radiances measured at the Top Of the Atmosphere (TOA), and subsequently combines the DSLR with the MODIS land surface temperature/emissivity products (MOD11_L2) to estimate the instantaneous Net Surface Longwave Radiation (NSLR). The proposed method relates the DSLR directly to the TOA radiances in the MODIS Thermal InfraRed (TIR) channels provided that the terrain altitude and the satellite Viewing Zenith Angle (VZA) are known. The simulation analysis shows that the instantaneous DSLR could be estimated by the proposed method with the Root Mean Square Error (RMSE) of 12.4 W/m2 for VZA=0 and terrain altitude z =0 km. Similar results are obtained for the other VZAs and altitudes. Considering the MODIS instrumental errors of 0.25 K for the TOA brightness temperatures in channels 28, 33 and 34, and of 0.05 K for channels 29 and 31, and of 0.35 K for channel 36, the overall retrieval accuracy in terms of the RMSE is decreased to 13.1 W/m2 for the instantaneous DSLR. Moreover, a comparison of MODIS derived DSLR and NSLR are done with the field measurements made at six sites of the Surface Radiation Budget Network (SURFRAD) in the United States for days with cloud-free conditions at the moment of MODIS overpass in 2006. The results show that the bias, RMSE and the square of the correlation coefficient (R 2) between the MODIS derived DSLR with the proposed method and the field measured DSLR are 20.3 W/m2, 30.1 W/m2 and 0.91 respectively, and bias=11.7 W/m2, RMSE=26.1 W/m2 and R 2 =0.94 for NSLR. In addition, the scheme proposed by Bisht et al. [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52–67], which requires the MODIS atmospheric profile product (MOD07) and also the MODIS land surface temperature/emissivity products (MOD11_L2) as inputs, is used to estimate the instantaneous DSLR and NSLR for comparison with the field measurements as well as the MODIS derived DSLR and NSLR using our proposed method. The results of the comparisons show that, at least for our cases, our proposed method for estimating DSLR from the MODIS radiances at the TOA and the resultant NSLR gives results comparable to those estimated with Bisht et al.''s scheme [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52–67]. [Copyright &y& Elsevier]

Abstract: The Net Surface Shortwave Radiation (NSSR) is of primary interest in climate research because it controls the total energy exchange between the atmosphere and the land/ocean surface. The conventional methods for estimating NSSR rely on broadband satellite data such as Earth Radiation Budget Experiment (ERBE) wide-field-of-view planetary albedo. The spatial resolution of the current ERBE satellite data having nadir footprints larger than 30 km is too coarse. The primary objective of this study is to estimate NSSR using multispectral narrowband data such as Moderate Resolution Imaging Spectroradiometer (MODIS) data. A direct method was developed for narrowband-to-broadband albedo conversion, which links the narrowband apparent reflectance at the Top Of Atmosphere (TOA) to shortwave broadband albedo for clear and cloudy skies without performing any surface angular modeling. The conversion coefficients were derived as functions of the secant Viewing Zenith Angle (VZA) for a given Solar Zenith Angle (SZA) and a given interval of Relative Azimuth angle (RAA). The result of comparing the values of estimated MODIS TOA shortwave broadband albedos with those of the Clouds and the Earth''s Radiant Energy System (CERES) data indicated that this direct method could predict TOA shortwave broadband albedo accurately with Root Mean Square (RMS) error between CERES observations and the estimated instantaneous MODIS TOA albedos less than 0.02. Based on more accurate radiative transfer model MODTRAN 4, the parameterization coefficients of Masuda et al. for the estimation of the NSSR from TOA broadband albedo were recalculated. The result showed that the coefficients should be categorized by land surfaces, ocean surface and snow/ice surface, respectively. Finally, the NSSR estimated from MODIS data was compared with the measurements of meteorological data for an extended period of time covering all seasons in a year 2003. The RMS error is less than 20 W/m2 for clear skies and 35 W/m2 for cloudy skies. [Copyright &y& Elsevier]

The commonly applied surface temperature–vegetation index (T s–VI) triangle method is used to estimate regional evapotranspiration (ET) in arid and semi-arid regions. A practical algorithm based on the T s–VI triangle method is developed to determine quantitatively the dry and wet edges of this triangle space. First, the T s–VI triangle method is reviewed. Assumptions involved in this method are highlighted, and advantages, disadvantages and applicability are discussed. Then, an experimental use of the T s–VI triangle method is developed and applied to several MODIS/TERRA datasets acquired during the Heihe Field Experiment from May 20th to August 21st, 2008. The sensible heat fluxes retrieved using MODIS data from a grassland located in the middle reach of Heihe river basin, Northwest China, are in good agreement with those measured from a Large Aperture Scintillometer (LAS). The Root Mean Square Error of this comparison is 25.07W/m2. It is shown that determination of dry and wet edges using the proposed algorithm is accurate enough at least in most cases of our study for the estimates of regional surface ET. [Copyright &y& Elsevier]

Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (<7.5°) and less than 0.8 K at a large viewing direction (<65°). The in situ validation shows that the minimum MAE obtained by the optimal band combination is approximately 1 K (root mean square error (RMSE) = 1.12 K; coefficient of determination (R2) = 0.902). The retrieval accuracy is improved by increasing the number of TIR bands in the atmospheric window, and adding accurate atmospheric water vapor information produces better results. In general, four TIR bands in the atmospheric window bands are sufficient to retrieve the LST with high accuracy. Likewise, three TIR bands plus atmospheric water vapor information are sufficient for the retrieval requirements. All analyses indicate that our method is feasible and reliably accurate and can also be used to help design the instrument band to retrieve the LST with high precision. [Display omitted] • A novel LST retrieval method is proposed based on model-data-knowledge-driven and DL. • Abduction is introduced from artificial intelligence to retrieve LST. • The DL-NN algorithm make good use of the relationship among geophysical parameters. • DL-NN can be used to solve and optimize complex LST retrieval calculations. [ABSTRACT FROM AUTHOR]