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Surface downward longwave radiation (DLR) is a crucial component in Earth’s surface energy balance. Yu et al. (2013) developed a parameterization for retrieving clear-sky DLR at high spatial resolution by combined use of satellite thermal infrared (TIR) data and column integrated water vapor (IWV). We extended the Yu2013 parameterization to Moderate Resolution Imaging Spectroradiometer (MODIS) data based on atmospheric radiative simulation, and we modified the parameterization to decrease the systematic negative biases at large IWVs. The new parameterization improved DLR accuracy by 1.9 to 3.1 W/m2 for IWV ≥3 cm compared to the Yu2013 algorithm. We also compared the new parameterization with four algorithms, including two based on Top-of-Atmosphere (TOA) radiance and two using near-surface meteorological parameters and water vapor. The algorithms were first evaluated using simulated data and then applied to MODIS data and validated using surface measurements at 14 stations around the globe. The results suggest that the new parameterization outperforms the TOA-radiance based algorithms in the regions where ground temperature is substantially different (enough that the difference between them is as large as 20 K) from skin air temperature. The parameterization also works well at high elevations where atmospheric parameter-based algorithms often have large biases. Furthermore, comparing different sources of atmospheric input data, we found that using the parameters interpolated from atmospheric reanalysis data improved the DLR estimation by 7.8 W/m2 for the new parameterization and 19.1 W/m2 for other algorithms at high-altitude sites, as compared to MODIS atmospheric products.
Shanshan Yu; Xiaozhou Xin; Qinhuo Liu; Hailong Zhang; Li Li. An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data. Remote Sensing 2019, 11, 425 .
AMA StyleShanshan Yu, Xiaozhou Xin, Qinhuo Liu, Hailong Zhang, Li Li. An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data. Remote Sensing. 2019; 11 (4):425.
Chicago/Turabian StyleShanshan Yu; Xiaozhou Xin; Qinhuo Liu; Hailong Zhang; Li Li. 2019. "An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data." Remote Sensing 11, no. 4: 425.
Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m2, −5.9 W/m2, and 14.9 W/m2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m2 (16.8%), 127.6 W/m2 (55.1%), and 99.5 W/m2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for ‘clearest sky’ will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness.
Hailong Zhang; Chong Huang; Shanshan Yu; Li Li; Xiaozhou Xin; Qinhuo Liu. A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data. Remote Sensing 2018, 10, 411 .
AMA StyleHailong Zhang, Chong Huang, Shanshan Yu, Li Li, Xiaozhou Xin, Qinhuo Liu. A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data. Remote Sensing. 2018; 10 (3):411.
Chicago/Turabian StyleHailong Zhang; Chong Huang; Shanshan Yu; Li Li; Xiaozhou Xin; Qinhuo Liu. 2018. "A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data." Remote Sensing 10, no. 3: 411.
Heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of evapotranspiration (ET) (or latent heat flux, LE) estimated from remote sensing satellite data. However, most of the current research uses statistical methods in the mixed pixel to correct the ET or LE estimation error, and there is a lack of research from the perspective of the remote sensing model. The method of using frequency distributions or generalized probability density functions (PDFs), which is called the “statistical-dynamical” approach to describe the heterogeneity of land surface characteristics, is a good way to solve the problem. However, in attempting to produce an efficient PDF-based parameterization of remotely sensed ET or LE, first and foremost, it is necessary to systematically understand the variables that are most consistent with the heterogeneity (i.e., variability for a fixed target area or landscape, where the variation in the surface parameter value is primarily concerned with the PDF-based model) of surface turbulence flux. However, the use of PDF alone does not facilitate direct comparisons of the spatial variability of surface variables. To address this issue, the objective of this study is to find an indicator based on PDF to express variability of surface variables. We select the dimensionless or dimensional consistent coefficient of variation (CV), Gini coefficient and entropy to express variability. Based on the analysis of simulated data and field experimental data, we find that entropy is more stable and accurate than the CV and Gini coefficient for expressing the variability of surface variables. In addition, the results of the three methods show that the variability of the leaf area index (LAI) is greater than that of the land surface temperature (LST). Our results provide a suitable method for comparing the variability of different variables.
Xiaojun Li; Xiaozhou Xin; Zhiqing Peng; Hailong Zhang; Chuanxiang Yi; Bin Li. Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign. Remote Sensing 2018, 10, 91 .
AMA StyleXiaojun Li, Xiaozhou Xin, Zhiqing Peng, Hailong Zhang, Chuanxiang Yi, Bin Li. Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign. Remote Sensing. 2018; 10 (2):91.
Chicago/Turabian StyleXiaojun Li; Xiaozhou Xin; Zhiqing Peng; Hailong Zhang; Chuanxiang Yi; Bin Li. 2018. "Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign." Remote Sensing 10, no. 2: 91.
Using high-resolution satellite data to perform routine (i.e., daily to weekly) monitoring of surface evapotranspiration, evapotranspiration (ET) (or LE, i.e., latent heat flux) has not been feasible because of the low frequency of satellite coverage over regions of interest (i.e., approximately every two weeks). Cloud cover further reduces the number of useable observations, and the utility of these data for routine ET or LE monitoring is limited. Moderate-resolution satellite imagery is available multiple times per day; however, the spatial resolution of these data is too coarse to enable the estimation of ET from individual agricultural fields or variations in ET or LE. The objective of this study is to combine high-resolution satellite data collected in the visible and near-infrared (VNIR) bands with data from the MODIS thermal-infrared (TIR) bands to estimate subpixel surface LE. Two temperature-sharpening methods, the disaggregation procedure for radiometric surface temperature (DisTrad) and the geographically-weighted regression (GWR)-based downscaling algorithm, were used to obtain accurate subpixel land surface temperature (LST) within the Zhangye oasis in China, where the surface is heterogeneous. The downscaled LSTs were validated using observations collected during the HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration) project. In addition, a remote sensing-based energy balance model was used to compare subpixel MODIS LST-based turbulent heat fluxes estimates with those obtained using the two LST downscaling approaches. The footprint validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all, leading to significant errors (i.e., the root mean square error is 73.15 W·m−2) in LE, whereas the errors in the LE estimates obtained using DisTrad and GWR were 45.84 W·m−2 and 47.38 W·m−2, respectively. Furthermore, additional analysis showed that the ability of DisTrad and GWR to capture subpixel LST variations depends on the value of Shannon’s diversity index (SHDI) and the surface type within the flux contribution source area.
Xiaojun Li; Xiaozhou Xin; Jingjun Jiao; Zhiqing Peng; Hailong Zhang; Shanshan Shao; Qinhuo Liu. Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data. Remote Sensing 2017, 9, 836 .
AMA StyleXiaojun Li, Xiaozhou Xin, Jingjun Jiao, Zhiqing Peng, Hailong Zhang, Shanshan Shao, Qinhuo Liu. Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data. Remote Sensing. 2017; 9 (8):836.
Chicago/Turabian StyleXiaojun Li; Xiaozhou Xin; Jingjun Jiao; Zhiqing Peng; Hailong Zhang; Shanshan Shao; Qinhuo Liu. 2017. "Estimating Subpixel Surface Heat Fluxes through Applying Temperature-Sharpening Methods to MODIS Data." Remote Sensing 9, no. 8: 836.
It remains a challenging issue to accurately estimate the fraction of absorbed photosynthetically-active radiation (FPAR) using remote sensing data, as the direct and diffuse radiation reaching the vegetation canopy have different effects for FPAR. In this research, a FPAR inversion model was developed that may distinguish direct and diffuse radiation (the DnD model) based on the energy budget balance principle. Taking different solar zenith angles and diffuse PAR proportions as inputs, the instantaneous FPAR could be calculated. As the leaf area index (LAI) and surface albedo do not vary in a short periods, the FPAR not only on a clear day, but also on a cloudy day may be calculated. This new method was used to produce the FPAR products in the Heihe River Basin with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI and surface albedo products as the input data source. The instantaneous FPAR was validated by using field-measured data (RMSE is 0.03, R2 is 0.85). The daily average FPAR was compared with the MODIS FPAR product. The inversion results and the MODIS FPAR product are highly correlated, but the MODIS FPAR product is slightly high in forest areas, which is in agreement with other studies for MODIS FPAR products.
Li Li; Yongming Du; Yong Tang; Xiaozhou Xin; Hailong Zhang; Jianguang Wen; Qinhuo Liu. A New Algorithm of the FPAR Product in the Heihe River Basin Considering the Contributions of Direct and Diffuse Solar Radiation Separately. Remote Sensing 2015, 7, 6414 -6432.
AMA StyleLi Li, Yongming Du, Yong Tang, Xiaozhou Xin, Hailong Zhang, Jianguang Wen, Qinhuo Liu. A New Algorithm of the FPAR Product in the Heihe River Basin Considering the Contributions of Direct and Diffuse Solar Radiation Separately. Remote Sensing. 2015; 7 (5):6414-6432.
Chicago/Turabian StyleLi Li; Yongming Du; Yong Tang; Xiaozhou Xin; Hailong Zhang; Jianguang Wen; Qinhuo Liu. 2015. "A New Algorithm of the FPAR Product in the Heihe River Basin Considering the Contributions of Direct and Diffuse Solar Radiation Separately." Remote Sensing 7, no. 5: 6414-6432.