Developing remote sensing inversion algorithms

 

Two directions have been pursued: focusing on improved estimation of individual variables, and exploring a new inversion scheme for estimating a set of variables simultaneously based on data assimilation approach.

The inversion accuracy also depends on the quality of input data, which has to be controlled by different pre-processing techniques. Integration of high-level products as a post-processing technique is another way to improve the accuracy and quality of the final satellite products.

The following text contains the papers in four categories: 1). Review papers; 2). Pre-processing and post-processing techniques; 3). Algorithms for estimating individual variables, including radiative transfer modeling; LAI; FAPAR, FVC, incident shortwave radiation; incident PAR; surface albedo; LST; broadband emissivity; longwave net radiation, all-wave net radiation; ET; soil moisture and other hydrological variables; GPP; biomass and crop yield; TOA fluxes; and image classification; and 4). A data assimilation based new inversion scheme.

1.    Review papers

·       Multiangular remote sensing (Liang et al. 2000a)

·       Angular correction in radiation budget (Liang et al. 2000b);

·       Canopy radiative transfer modeling(Qin and Liang 2000)

·       Snow/soil radiative transfer modeling (Nolin and Liang 2000)

·       Biophysical parameter retrieval (Liang 2007)

·       Earth’s energy budget (Liang 2017; Liang et al. 2010)

·       Surface radiation budget (Liang  et al. 2009)

·       Integration of high-level products (Liang et al. 2017).

2.    Pre-processing and post-processing techniques

1)  Sensor radiometric calibration

·       Calibrating Chinese environmental (HJ) (Jiang et al. 2013) and meteorological (FY) (Kim et al. 2014a) satellites, and also on Landsat-5 TM (Kim et al. 2014b) data;

·       Quantifying MODIS thermal errors (Liu et al. 2006b).

2)  Atmospheric correction

·       Correcting the effects of spatial variations in aerosol loadings for ETM+ (Fallah-Adl et al. 1997; Fallah-Adl et al. 1996; Liang et al. 1997a; Liang et al. 2001; Liang et al. 2002b), MODIS(Guang et al. 2012; Liang et al. 2006b; Zhong et al. 2007), ASTER(Guang et al. 2011);

·       Correcting water vapor effects for  AVIRIS and Hyperion hyperspectral data(Liang and Fang 2004; Zhao et al. 2008);

·       Atmospheric correction for multi-angular data (Liang and Strahler 1994a).

3)  Re-construction of high-level products

·       Re-constructing surface reflectance from the re-processed NDVI data (Xiao et al. 2017b); 

·       Reconstructing high-level products (Fang et al. 2007; Fang et al. 2008b; Lu et al. 2007).

4)  Integration of high-level satellite products

·       Optimal Interpolation (OI) and Empirical Orthogonal Functions (EOF) for integrating the LAI and evapotranspiration products (Feng et al. 2016a; Wang and Liang 2011, 2014);

·       Multi-Resolution Tree (MRT) method for integrating multiple products of land surface broadband albedo (He et al. 2014b), FAPAR (Tao et al. 2017) and land surface emissivity (Shi et al. 2016b);

·       Bayesian Model Averaging (BMA) method for integrating surface longwave downward radiation and surface latent heat flux products (Chen et al. 2015; Wu et al. 2012b; Yao et al. 2014a).

3.    Algorithms for estimating individual variables

1)     Radiative transfer modeling

A series of radiative transfer models of the Earth’s surface have been developed which coupled elements of the soil-vegetation-atmosphere system. These models have been proven particularly valuable for linking remote sensing observations with environmental variables, and have consequently led to the development of new inversion algorithms;

·       Canopy RT modeling(Albers et al. 1990; Liang and Strahler 1993a; Liang and Strahler 1993b; Liang and Strahler 1995; Liang et al. 1997b);

·       Atmospheric RT modeling (Liang and Lewis 1996; Liang and Strahler 1994b);

·       Soil RT modeling (Liang 1997; Liang and Mishchenko 1997; Liang and Townshend 1996a, b);

·       Snow RT modeling (Cheng and Liang 2011; Cheng et al. 2010b);

·       Simulating LAI and albedo scaling(Liang 2000) and thermal scaling(Su et al. 2003).

2)     LAI algorithms

·       MODIS(Chai et al. 2012; Fang and Liang 2005; Fang et al. 2008b; Wang et al. 2008a; Xiao et al. 2014; Zhang et al. 2012; Zhou et al. 2017);

·       AVHRR(Xiao et al. 2016a);

·       VIIRS(Xiao et al. 2016b);

·       MISR and VEGETATION(Liu et al. 2014; Ma et al. 2017b; Wan et al. 2009);

·       Landsat TM/ETM+ (Fang and Liang 2003; Fang et al. 2003; Fang et al. 2005; Walthall et al. 2004);

·       EO1 ALI(Liang et al. 2003a);

·       simulation data(Gong et al. 1999);

·       Data assimilation based algorithm(Liu et al. 2014; Qin et al. 2008; Wang et al. 2010b; Xiao et al. 2011; Xiao et al. 2009; Xiao et al. 2012);

·       Validation and inter-comparison: evaluation of long-time series LAI products from AVHRR data (Xiao et al. 2017a), time series analysis (Jiang et al. 2010), validation of products(Fang et al. 2012), and product intercomparson(Fang et al. 2013a; Fang et al. 2004).

2). FAPAR

·       Landsat/ETM+(Fang et al. 2005);

·       MODIS(Tao et al. 2016; Xiao et al. 2016c);

·       MISR(Tao et al. 2016);

·       AVHRR(Xiao et al. 2016c);

·       VIIRS (Xiao et al. 2016b).

3). Fraction of vegetation cover (FVC)

·       MODIS (Jia et al. 2015a; Yang et al. 2016);

·       ETM+ (Jia et al. 2017b; Wang et al. 2017b; Yang et al. 2017);

·       GF(Jia et al. 2016b);

·       AVHRR (Jia et al. 2015b);

·       Evaluating tree cover products(Liu et al. 2006a).

4). Incident shortwave radiation

·       Parameterization algorithm (Qin et al. 2015);

·       Look-up table (LUT) methods for MODIS (Zhang et al. 2014a). GMS-5 (Lu et al. 2010), and MTSAT (Huang et al. 2011);

·       Neural network method (Qin et al. 2011a);

·       Calibrating satellite product with ground measurements(Zhang et al. 2016a), and comparing satellite products with reanalysis products(Zhang et al. 2016b);

·       Product validation(Gui et al. 2010; Schroeder et al. 2009).

5). Incident PAR

·       LUT method (Liang et al. 2006a) for MODIS (Liu et al. 2008; Wang et al. 2010a). GOES (Zheng et al. 2008), and AVHRR (Liang et al. 2007) data;

·       Estimating daily PAR from sunshine data(Qin et al. 2012) and from MODIS high-level products(Tang et al. 2017).

·       Temporal scaling for daily PAR (Wang et al. 2010a; Zheng and Liang 2011)

6). Broadband albedo

·        “direct estimation method” estimating albedo directly from satellite observations based on extensive radiative transfer simulations, different from the traditional approaches consisting of atmospheric correction, BRDF modeling, narrowband to broadband conversion: (Liang 2003; Liang et al. 1999; Liang et al. 2005a). It has been used for GLASS (Liu et al. 2013a; Liu et al. 2013b; Qu et al. 2014) and the VIIRS albedo production(Wang et al. 2013; Wang et al. 2017a), and for a variety of remotely sensed data, such as MODIS(Wang et al. 2015c), MISR (He et al. 2017a);AVIRIS (He et al. 2014a), Landsat (He et al. 2017b),ALI(Liang et al. 2003a),  and HJ (He et al. 2015a);

·       Narrowband to broadband albedo conversion (Liang 2001b; Liang et al. 2003b; Liang et al. 2005b);

·       Estimating albedo and BRDF using the optimization method(He et al. 2012)and ensemble Kalman filter(Qin et al. 2006), and high-performance computing(Kalluri et al. 2001; Zhang et al. 1998);

·       Estimating albedo over oceans (Feng et al. 2016b; Qu et al. 2016) and evaluating existing ocean albedo products(Cao et al. 2016);

·       Evaluating and validating the global albedo products: MODIS(Jin et al. 2003a, b; Liang et al. 2002a; Román et al. 2013; Stroeve et al. 2005; Wang et al. 2010d), MISR(Chen et al. 2008; Taberner et al. 2010; Wu et al. 2012a), VIIRS (Zhou et al. 2016 ), and GEWEX/ISCCP(Qin et al. 2011b).

7). LST

·       Optimization method to estimate LST from multispectral thermal data(Liang 2001c), and also validating the MODIS (Wang et al. 2008b) and ASTER LST products(Wang and Liang 2009b);

·       Estimating LST and spectral emissivity from hyperspectral data (Cheng et al. 2011a; Cheng et al. 2010a).

8). Broadband emissivity

·       The GLASS longwave broadband emissivity product algorithms based on conversion of shortwave spectral albedos for soils (Cheng and Liang 2013a; Cheng and Liang 2013b; Cheng et al. 2011b) and radiative transfer calculations for vegetation  (Cheng et al. 2016);

·       Angular effects (Cheng and Liang 2014), spectral range (Cheng et al. 2013b), validation (Cheng et al. 2014),and product evaluation(Cheng et al. 2013a); Empirical algorithm for determining the vegetation emissivity(Ren et al. 2013);

·       Emissivity product applied to Earth system model simulation (Jin and Liang 2006);

·       Ocean emissivity estimation algorithm(Cheng et al. 2017a).

9). Longwave net radiation

·       Downward and upwelling longwave radiation are estimated separately. Upwelling longwave radiation can be calculated from LST and broadband longwave emissivity, but the uncertainties of these two components may cause large errors (Wang and Liang 2009b).

·       The direct estimation methods have been developed for estimating upwelling, downward and net longwave radiation (Cheng and Liang 2016; Cheng et al. 2017b; Wang and Liang 2009d, 2010; Wang et al. 2009b);

·       Meteorological observations have been also used for calculating downward radiation(Wang and Liang 2009c);

·       Product validation(Gui et al. 2010).

10). All-wave net radiation

·       Instead of adding all components together, we have developed the algorithms for converting shortwave net radiation in conjunction with other information. The algorithms for estimating shortwave net radiation have been developed for various sensors, such as MODIS(Huang et al. 2012; Kim and Liang 2010); Landsat(Wang et al. 2014),MERSI(Wang et al. 2015a), AVIRIS(He et al. 2015b; Wang et al. 2015b) , and incident shortwave radiation using other methods(Zhang et al. 2016a; Zhang et al. 2016b);

·       Comparing different linear formulae (Jiang et al. 2015), machine learning techniques (Jiang et al. 2014), the MARS algorithm is used for producing the GLASS day-time all-wave net radiation (Jiang et al. 2016);

·       Empirical algorithms (Wang and Liang 2009a);

·       Validation and comparison with other products (Jia et al. 2016a; Jia et al. 2017a).

11). ET

·       Empirical ET algorithms (Wang et al. 2009a; Wang et al. 2010c; Wang et al. 2010e, f; Wang and Liang 2008; Yao et al. 2010a; Yao et al. 2010b);

·       Priestley–Taylor type algorithms(Yao et al. 2015; Yao et al. 2017c; Yao et al. 2014c; Yao et al. 2013);

·       Penman-Monteith type algorithm (Li et al. 2014; Sun et al. 2013; Yuan et al. 2012b);

·       Integrated algorithms (Feng et al. 2015; Feng et al. 2016a; Yao et al. 2017a; Yao et al. 2014a; Yao et al. 2017b; Yao et al. 2014b; Yao et al. 2016)

·       Energy budget based model from ASTER (Galleguillos et al. 2011)

·       Regression tree method(Xia et al. 2014a);

·       Product evaluations (Chen et al. 2014);

·       Data assimilation methods for estimating ET/heat fluxes (Bateni and Liang 2012; Qin et al. 2005; Qin et al. 2007; Xu et al. 2015; Xu et al. 2014; Xu et al. 2011a; Xu et al. 2011b).

12). Soil moisture and other hydrological variables

·       Data assimilation methods for predicting river variables (Meng et al. 2017; Xie et al. 2014) and soil moisture (Qin et al. 2009);

·       Estimating soil moisture from temperature and vegetation index(Sun et al. 2012).

13). GPP

·       Estimating GPP over China (Li et al. 2013), high-latitude regions(Yuan et al. 2014b), and globe(Cai et al. 2014; Xia et al. 2015; Xia et al. 2014b; Yuan et al. 2012a; Yuan et al. 2011).

·       Model evaluation (Cai et al. 2014)

13). Biomass & crop yield

·       Integrating Lidar and MODIS data to estimate forest biomass (Zhang et al. 2014b);

·       Crop yield estimation using regression and neural networks(Li et al. 2007);

·       Assimilating LAI, vegetation index and reflectance into DISSAT model to estimate crop yield (Fang et al. 2011; Fang et al. 2013b; Fang et al. 2008a);

·       Assimilating LAI into WOFOST model to estimate winter wheat yield (Huang et al. 2016; Huang et al. 2015).

14). Top-of-atmosphere (TOA) radiative fluxes

High-resolution MODIS data have been used to estimate TOA albedo(Wang and Liang 2016) and reflected flux (Wang et al. 2017a).

15). Image classification

·       Classification algorithms for AVHRR (Liang 2001a), ETM+ (Jia et al. 2014a; Jia et al. 2014b; Jia et al. 2014d), MODIS(Jia et al. 2014c), and Lidar data(Dong et al. 2017);

·       Mapping plant functional types (Sun and Liang 2008; Sun et al. 2008), afforestation(Yuan et al. 2014a),wheat(Pan et al. 2012) and rice(Van Niel et al. 2003),  and snow/ice(Hui et al. 2016);

·       Effects of point response function(Huang et al. 2002; Townshend et al. 2000).

4.    A data assimilation based new inversion scheme

After over two decade of efforts, many land products are now being produced systematically from a variety of satellite data, and these products have been widely used. However, estimating a set of atmospheric and surface variables from one sensor data is often an ill-posed inversion problem, because the number of unknowns is often larger than the available bands. Thus, one has to make assumptions while trying to obtain realistic solutions, and as a result, most products still need significant improvements of quality and accuracy. Although the average accuracy may be acceptable, the error of each product can be very large under certain conditions. Furthermore, different products of land variables from different inversion algorithms are physically inconsistent for most cases. Many products in the current form are not suitable for climate study because the products are not continuous both spatially and temporally due to factors such as clouds. There is an urgent need to develop more advanced new inversion methods and produce more accurate products.

We have recently proposed a new scheme based on the data assimilation approach to estimate an improved suite of products simultaneously from one or multiple satellite data.  Multiple case studies have been conducted:

A.    Input: MODIS surface reflectance; Output: LAI, FAPAR and albedo (Xiao et al. 2015);

B.    Input: surface reflectance of multiple sensors (MODIS+VEGETATION+MISR); Output: LAI, FAPAR and albedo (Ma et al. 2017b);

C.    Input: TOA clear-sky MODIS reflectance; Output: LAI, FAPAR, albedo, PAR/APAR (Shi et al. 2016a);

D.    Input: TOA all-sky MODIS reflectance; Output: LAI, FAPAR, albedo, PAR/APAR(Shi et al. 2016a);

E.    Input: MODIS surface reflectance + TOA thermal radiance; Output: LAI, FAPAR, albedo, PAR/APAR, LST, emissivity, longwave net radiation (Ma et al. 2017a).

5.    References

Albers, B.J., Strahler, A.H., Li, X., Liang, S., & Clarke, K.C. (1990). Radiometric measurements of gap probability in conifer tree canopies. Remote Sensing of Environment, 34, 179-192

Bateni, S., & Liang, S. (2012). Estimating surface energy fluxes using a dual-source data assimilation approach adjoined to the heat diffusion equation. Journal of Geophysical Research, 117, D17118

Cai, W., Yuan, W., Liang, S., Liu, S., Dong, W., Chen, Y., Liu, D., & Zhang, H. (2014). Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models. Remote Sensing, 6, 8945

Cao, Y., Liang, S., He, T., & Chen, X. (2016). Evaluation of Four Reanalysis Surface Albedo Data Sets in Arctic Using a Satellite Product. Ieee Geoscience and Remote Sensing Letters, 13, 384-388

Chai, L., Qu, Y., Zhang, L., Liang, S., & Wang, J. (2012). Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs. International Journal of Remote Sensing, 33, 5712-5731

Chen, Y., Liang, S., Wang, J., Kim, H., & Martonchik, J.V. (2008). Validation of the MISR land surface broadband albedo. International Journal of Remote Sensing, 29, 6971-6983

Chen, Y., Xia, J., Liang, S., Feng, J., Fisher, J.B., Li, X., Li, X., Liu, S., Ma, Z., Miyata, A., Mu, Q., Sun, L., Tang, J., Wang, K., Wen, J., Xue, Y., Yu, G., Zha, T., Zhang, L., Zhang, Q., Zhao, T., Zhao, L., & Yuan, W. (2014). Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sensing of Environment, 140, 279-293

Chen, Y., Yuan, W.P., Xia, J.Z., Fisher, J.B., Dong, W.J., Zhang, X.T., Liang, S., Ye, A.Z., Cai, W.W., & Feng, J.M. (2015). Using Bayesian model averaging to estimate terrestrial evapotranspiration in China. Journal of Hydrology, 528, 537-549

Cheng, J., Cheng, X., Liang, S., Niclòs, R., Nie, A., & Liu, Q. (2017a). A Lookup Table-Based Method for Estimating Sea Surface Hemispherical Broadband Emissivity Values (813.5 μm). Remote Sensing, 9, 245doi:210.3390/rs9030245

Cheng, J., & Liang, S. (2011). Radiative Transfer Modeling. In V.P. Singh, P. Singh, & U.K. Haritashya (Eds.), Encyclopedia of Snow, Ice and Glaciers (pp. 903-913): Springer

Cheng, J., & Liang, S. (2013a). Estimating global land surface broadband thermal-infrared emissivity from the Advanced Very High Resolution Radiometer optical data. International Journal of Digital Earth, DOI: 10.1080/17538947.17532013.17783129

Cheng, J., & Liang , S. (2013b). A Novel Algorithm for Estimating Broadband Emissivity of Global Bare Soil using MODIS Albedo Product. IEEE Transactions on Geoscience and Remote Sensing, 51, 2619-2631

Cheng, J., & Liang, S. (2014). Effects of thermal-infrared emissivity directionality on surface broadband emissivity and longwave net radiation estimation. Ieee Geoscience and Remote Sensing Letters, 4, 459-463

Cheng, J., & Liang, S. (2016). Global Estimates for High-Spatial-Resolution Clear-Sky Land Surface Upwelling Longwave Radiation From MODIS Data. IEEE Transactions on Geoscience and Remote Sensing, 54, 4115-4129

Cheng, J., Liang, S., Dong, L., Ren, B., & Shi, L. (2014). Validation of the moderate-resolution imaging spectroradiometer land surface emissivity products over the Taklimakan Desert. Journal of Applied Remote Sensing, 8, 083675-083675

Cheng, J., Liang, S., Liu, Q., & Li, X. (2011a). Temperature and Emissivity Separation From Ground-Based MIR Hyperspectral Data. Geoscience and Remote Sensing, IEEE Transactions on, 49, 1473-1484

Cheng, J., Liang, S., Tzeng, Y.C., Wang, C., & Rebn, H. (2011b). Mapping global broadband emissivity from MODIS albedo product by using dynamic learning neural network. IEEE Transactions on Geoscience and Remote Sensing, submitted

Cheng, J., Liang, S., Verhoef, W., Shi, L., & Liu, Q. (2016). Estimating the Hemispherical Broadband Longwave Emissivity of Global Vegetated Surfaces Using a Radiative Transfer Model. IEEE Transactions on Geoscience and Remote Sensing, 54, 905-917

Cheng, J., Liang, S., Wang, J., & Li, X. (2010a). A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 48, 1588-1597

Cheng, J., Liang, S., Wang, W., & Guo, Y. (2017b). An efficient hybrid method for estimating clear-sky surface downward longwave radiation from MODIS data. Journal of Geophysical Research: Atmospheres, 122, 2616-2630

Cheng, J., Liang, S., Weng, F., Wang, J., & Li, X. (2010b). Comparison of Radiative Transfer Models for Simulating Snow Surface Thermal Infrared Emissivity. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 3, 323-336

Cheng, J., Liang, S., Yao, Y., Ren, B., Shi, L., & Liu, H. (2013a). A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data. Remote Sensing, 6, 111-134

Cheng, J., Liang, S., Yao, Y., & Zhang, X. (2013b). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. Ieee Geoscience and Remote Sensing Letters, 10, 401-405

Dong, W., Lan, J., Liang, S., Yao, W., & Zhan, Z. (2017). Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification. International Journal of Applied Earth Observation and Geoinformation, 60, 99-110

Fallah-Adl, H., JaJa, J., & Liang, S. (1997). Fast algorithms for estimating aerosol optical depth of Thematic Mapper (TM) imagery. Journal of Supercomputing, 10, 315-330

Fallah-Adl, H., JaJa, J., Liang, S., Kaufman, Y., & Townshend, J.R.G. (1996). Efficient parallel algorithms for atmospheric correction of remotely sensed data. IEEE Computational Science and Engineering, 1, 66-77

Fang, H., Jiang, C., Li, W., Wei, S., Baret, F., Chen, J.M., Garcia-Haro, J., Liang, S., Liu, R., Myneni, R.B., Pinty, B., Xiao, Z., & Zhu, Z. (2013a). Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties. Journal of Geophysical Research - Biogeosciences, 118, DOI:10.1002/jgrg.20051

Fang, H., Kim, H., Liang, S., Schaaf, C., Strahler, A., Townshend, G.R.G., & Dickinson, R. (2007). Developing a spatially continuous 1 km surface albedo data set over North America from Terra MODIS products. Journal of Geophysical Research, 112, D20206, doi:20210.21029/22006JD008377

Fang, H., & Liang, S. (2003). Retrieve LAI from Landsat 7 ETM+ Data with a Neural Network Method: Simulation and Validation Study. IEEE Transactions on Geoscience and Remote Sensing, 41, 2052-2062

Fang, H., & Liang, S. (2005). A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment, 94, 405-424

Fang, H., Liang, S., Chen, M., Walthall, C., & Daughtry, C. (2004). Intercomparison of MISR land surface reflectance and albedo products with ETM+ and MODIS products. International Journal of Remote Sensing, 25, 409-422

Fang, H., Liang, S., & Hoogenboom, G. (2011). Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International  Journal of Remote Sensing, 32, 1039-1065

Fang, H., Liang , S., & Hoogenboom, G. (2013b). Assimilation of remote sensing data and crop simulation models for agricultural study: Recent Advances and Future Directions. In S. Liang , X. Li, & X. Xie (Eds.), Land Surface Observation, Modeling and Data Assimilation (pp. 405-439): World Scientific

Fang, H., Liang, S., Hoogenboom, G., Teasdale, J., & Cavigelli, M. (2008a). Crop yield estimation through assimilation of remotely sensed data into DSSAT-CERES. International Journal of Remote Sensing, 29, 3011-3032

Fang, H., Liang, S., & Kuusk, A. (2003). Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment, 85, 257-270

Fang, H., Liang, S., McClaran, M.P., van Leeuwen, W.J.D., Drake, S., Marsh, S.E., Thomson, A.M., Izaurralde, R.C., & Rosenberg, N.J. (2005). Biophysical characterization and management effects on semiarid rangeland observed from landsat ETM plus data. IEEE Transactions on Geoscience and Remote Sensing, 43, 125-134

Fang, H., Liang, S., Townshend, J., & Dickinson, R. (2008b). Spatially and temporally continuous LAI data sets based on an new filtering method: Examples from North America. Remote Sensing of Environment, 112, 75–93

Fang, H., Wei, S.S., & Liang, S. (2012). Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Environment, 119, 43-54

Feng, F., Chen, J.Q., Li, X.L., Yao, Y.J., Liang, S., Liu, M., Zhang, N.N., Guo, Y., Yu, J., & Sun, M.M. (2015). Validity of Five Satellite-Based Latent Heat Flux Algorithms for Semi-arid Ecosystems. Remote Sensing, 7, 16733-16755

Feng, F., Li, X.L., Yao, Y.J., Liang, S., Chen, J.Q., Zhao, X., Jia, K., Pinter, K., & McCaughey, J.H. (2016a). An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations. Plos One, 11, 16

Feng, Y., Liu, Q., Qu, Y., & Liang, S. (2016b). Estimation of the Ocean Water Albedo From Remote Sensing and Meteorological Reanalysis Data. IEEE Transactions on Geoscience and Remote Sensing, 54, 850-868

Galleguillos, M., Jacob, F., Prévot, L., Lagacherie, P., & Liang, S. (2011). Mapping daily evapotranspiration over a Mediterranean vineyard watershed. Ieee Geoscience and Remote Sensing Letters, 8, 168-172

Gong, P., Wang, S.X., & Liang, S. (1999). Inverting a canopy reflectance model using a neural network. International Journal of Remote Sensing, 20, 111-122

Guang, J., Xue, Y., Li, Y., Liang, S., Mei, L., & Xu, H. (2012). Retrieval of aerosol optical depth over bright land surfaces by coupling bidirectional reflectance distribution function model and aerosol retrieval model. Remote Sensing Letters, 3, 577-584

Guang, J., Xue, Y., Wang, Y., Li, Y., Mei, L., Xu, H., Liang, S., Wang, J., & Bai, L. (2011). Simultaneous determination of aerosol optical thickness and surface reflectance using ASTER visible to near-infrared data over land. International Journal of Remote Sensing, 32, 6961-6974

Gui, S., Liang, S., Wang, K., & Li, L. (2010). Assessment of Three Satellite-Estimated Land Surface Downward Shortwave Radiation Datasets Ieee Geoscience and Remote Sensing Letters, 7, 776-780

He, T., Liang, S., & Wang, D. (2017a). Direct Estimation of Land Surface Albedo From Simultaneous MISR Data. IEEE Transactions on Geoscience and Remote Sensing, 55, 2605-2617

He, T., Liang, S., Wang, D., Cao, Y., Gao, F., & Yu, Y. (2017b). Evaluating a land surface albedo product from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment, revised

He, T., Liang, S., Wang, D., Chen, X., Song, D.-X., & Jiang, B. (2015a). Land Surface Albedo Estimation from Chinese HJ Satellite Data Based on the Direct Estimation Approach. Remote Sensing, 7, 5495-5510

He, T., Liang, S., Wang, D., Shi, Q., & Goulden, M.L. (2015b). Estimation of high-resolution land surface net shortwave radiation from AVIRIS data: Algorithm development and preliminary results. Remote Sensing of Environment, 167, 20-30

He, T., Liang, S., wang, D., Shi, Q., & Tao, X. (2014a). Estimation of high-resolution land surface shortwave albedo from AVIRIS data. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 7, 4919-4928

He, T., Liang, S., Wang, D., Wu, H., Yu, Y., & Wang, J. (2012). Estimation of Surface Albedo and Reflectance from Moderate Resolution Imaging Spectroradiometer Observations Remote Sensing of Environment, 119, 286-300

He, T., Liang, S., Wang, D.D., Shuai, Y.M., & Yu, Y.Y. (2014b). Fusion of Satellite Land Surface Albedo Products Across Scales Using a Multiresolution Tree Method in the North Central United States. IEEE Transactions on Geoscience and Remote Sensing, 52, 3428-3439

Huang, C., Townshend, J.R.G., Liang, S., Kalluri, S.N.V., & DeFries, R.S. (2002). Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution. Remote Sensing of Environment, 80, 203-212

Huang, G., Liu, S., & Liang, S. (2012). Estimation of net surface shortwave radiation from MODIS data. International Journal of Remote Sensing, 33, 804-825

Huang, G., Ma, M., Liang, S., Liu, S., & Li, X. (2011). A LUT-based approach to estimate surface solar irradiance by combining MODIS and MTSAT data. Journal of Geophysical Research, 116, D22201

Huang, J.X., Sedano, F., Huang, Y.B., Ma, H.Y., Li, X.L., Liang, S., Tian, L.Y., Zhang, X.D., Fan, J.L., & Wu, W.B. (2016). Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188-202

Huang, J.X., Tian, L.Y., Liang, S., Ma, H.Y., Becker-Reshef, I., Huang, Y.B., Su, W., Zhang, X.D., Zhu, D.H., & Wu, W.B. (2015). Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106-121

Hui, F., Li, X., Zhao, T., Shokr, M., Heil, P., Zhao, J., Liu, Y., Liang, S., & Cheng, X. (2016). Semi-Automatic Mapping of Tidal Cracks in the Fast Ice Region near Zhongshan Station in East Antarctica Using Landsat-8 OLI Imagery. Remote Sensing, 8, 242

Jia, A., Jiang, B., Liang, S., Zhang, X., & Ma, H. (2016a). Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product. Remote Sensing, 8, 90

Jia, A., Liang, S., Jiang, B., Zhang, X., & Wang, G. (2017a). Uncertainty analysis of the surface net radiation products from in-situ,  satellite and reanalysis data. Journal of Geophysical Research, submitted

Jia, K., Li, Y., Liang, S., Wei, X., Mu, X., & Yao, Y. (2017b). Fractional vegetation cover estimation based on soil and vegetation lines in a corn-dominated area. Geocarto International, 32, 531-540

Jia, K., Liang, S., Gu, X., Baret, F., Wei, X., Wang, X., Yao, Y., Yang, L., & Li, Y. (2016b). Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data. Remote Sensing of Environment, 177, 184-191

Jia, K., Liang, S., Liu, S.H., Li, Y.W., Xiao, Z.Q., Yao, Y.J., Jiang, B., Zhao, X., Wang, X.X., Xu, S., & Cui, J. (2015a). Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53, 4787-4796

Jia, K., Liang, S., Wei, X., Li, Q., Du, X., Jiang, B., Yao, Y., Zhao, X., & Li, Y. (2015b). Fractional forest cover changes in Northeast China from 1982 to 2011 and its relationship with climatic variations. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 775-783

Jia, K., Liang, S., Wei, X., Yao, Y., Su, Y., Jiang, B., & Wang, X. (2014a). Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data. Remote Sensing, 6, 11518-11532

Jia, K., Liang, S., Wei, X., Zhang, L., Yao, Y., & Gao, S. (2014b). Automatic land-cover update approach integrating iterative training sample selection and a Markov Random Field model. Remote Sensing Letters, 5, 148-156

Jia, K., Liang, S., Zhang, L., Wei, X., Yao, Y., & Xie, X. (2014c). Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. International Journal of Applied Earth Observation and Geoinformation, 33, 32-38

Jia, K., Liang, S., Zhang, N., Wei, X., Gu, X., Zhao, X., Yao, Y., & Xie, X. (2014d). Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 49-55

Jiang, B., Liang, S., Ma, H., Zhang, X., Xiao, Z., Zhao, X., Jia, K., Yao, Y., & Jia, A. (2016). GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation. Remote Sensing, 8, 222

Jiang, B., Liang, S., Townshend, J., & Dodson, Z. (2013). Assessment of the Radiometric Performance of Chinese HJ-1 Satellite CCD Instruments. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 6, 840-850

Jiang, B., Liang, S., Wang, J., & Xiao, Z. (2010). Modeling MODIS LAI time series using three statistical methods. Remote Sensing of Environment, 114, 1432-1444

Jiang, B., Zhang, Y., Liang, S., Wohlfahrt, G., Arain, A., Cescatti, A., Georgiadis, T., Jia, K., Kiely, G., Lund, M., Montagnani, L., Magliulo, V., Ortiz, P.S., Oechel, W., Vaccari, F.P., Yao, Y., & Zhang, X. (2015). Empirical estimation of daytime net radiation from shortwave radiation and ancillary information. Agricultural and Forest Meteorology, 211–212, 23-36

Jiang, B., Zhang, Y., Liang, S., Zhang, X., & Xiao, Z. (2014). Surface Daytime Net Radiation Estimation Using Artificial Neural Networks. Remote Sensing, 6, 11031-11050

Jin, M., & Liang, S. (2006). Improve land surface emissivity parameter for land surface models using global remote sensing observations. Journal of Climate, 19, 2867-2881

Jin, Y., Schaaf, C.B., Gao, F., Li, X., Strahler, A.H., Lucht, W., & Liang, S. (2003a). Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 1. Algorithm performance. Journal of Geophysical Research, 108, 4158, doi:4110.1029/2002JD002803

Jin, Y., Schaaf, C.B., Gao, F., Li, X., Strahler, A.H., Lucht, W., & Liang, S. (2003b). Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation. Journal of Geophysical Research, 108, 4159, doi:4110.1029/2002JD002804

Kalluri, S.N.V., Zhang, Z., JaJa, J., Liang, S., & Townshend, J.R.G. (2001). Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing. International Journal of Remote Sensing, 22, 2171-2191

Kim, H.Y., & Liang, S. (2010). Development of a hybrid method for estimating land surface shortwave net radiation from MODIS data. Remote Sensing of Environment, 114, 2393-2402

Kim, W., Cao, C., & Liang, S. (2014a). Assessment of Radiometric Degradation of FY-3A MERSI Reflective Solar Bands using TOA Reflectance of Pseudo-Invariant Calibration Sites. Ieee Geoscience and Remote Sensing Letters, 11, 793-797

Kim, W., Wang, D., He, T., Cao, C., & Liang, S. (2014b). Assessment of Long-term Sensor Radiometric Degradation Using Time Series Analysis. IEEE Transactions on Geoscience and Remote Sensing, Doi:10.1109/TGRS.2013.2268161

Li, A., Liang, S., Wang, A., & Qin, J. (2007). Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering and Remote Sensing, 73, 1149-1157

Li, X., Liang, S., Yu, G., Yuan, W., Cheng, X., Xia, J., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Chen, J., Shao, C., Li, S., Zhang, X., Zhang, Z., Chen, S., Ohta, T., Varlagin, A., Miyata, A., Takagi, K., Saiqusa, N., & Kato, T. (2013). Estimation of gross primary production over the terrestrial ecosystems in China. Ecological Modelling, 261–262, 80-92

Li, X., Liang, S., Yuan, W., Yu, G., Cheng, X., Chen, Y., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Chen, J., Shao, C., Li, S., Zhang, X., Zhang, Z., Sun, G., Chen, S., Ohta, T., Varlagin, A., Miyata, A., Takagi, K., Saiqusa, N., & Kato, T. (2014). Estimation of evapotranspiration over the terrestrial ecosystems in China. Ecohydrology, 7, 139-149

Liang, S. (1997). An investigation of remotely sensed soil depth in the optical region. International Journal of Remote Sensing, 18, 3395-3408

Liang, S. (2000). Numerical experiments on spatial scaling of land surface albedo and leaf area index. Remote Sensing Reviews, 19, 225-242

Liang, S. (2001a). Land cover classification methods for multiyear AVHRR data. International Journal of Remote Sensing, 22, 1479-1493

Liang, S. (2001b). Narrowband to broadband conversions of land surface albedo. Remote Sensing of Environment, 76, 213-238

Liang, S. (2001c). An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, 264-274

Liang, S. (2003). A direct algorithm for estimating land surface broadband albedos from MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing, 41, 136-145

Liang, S. (2007). Recent developments in estimating land surface biogeophysical variables from optical remote sensing. Progress in Physical Geography, 31, 501-516

Liang, S. (2017). Remote Sensing of Earth’s Energy Budget: An Overview of Recent Progress. In S. Liang (Ed.), Comprehensive Remote Sensing vol. 5: Earth's Energy Budget (p. in press). Oxford, UK: Elsevier

Liang, S., Fallah-Adl, H., Kalluri, S., JaJa, J., Kaufman, Y.J., & Townshend, J.R.G. (1997a). An operational atmospheric correction algorithm for Landsat Thematic Mapper imagery over the land. Journal of Geophysical Research-Atmospheres, 102, 17173-17186

Liang, S., & Fang, H. (2004). An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery. Ieee Geoscience and Remote Sensing Letters, 1, 112-117

Liang, S., Fang, H., & Chen, M. (2001). Atmospheric Correction of Landsat ETM+ Land Surface Imagery: I. Methods. IEEE Transactions on Geoscience and Remote Sensing, 39, 2490-2498

Liang, S., Fang, H., Chen, M., Shuey, C., Walthall, C., Daughtry, C., Morisette, J., Schaaf, C., & Strahler, A. (2002a). Validating MODIS land surface reflectance and albedo products: Methods and preliminary results. Remote Sensing of Environment, 83, 149-162

Liang, S., Fang, H., Kaul, M., Van Niel, T.G., McVicar, T.R., Pearlman, J., Walthall, C.L., Daughtry, C., & Huemmrich, K.F. (2003a). Estimation of land surface broadband albedos and leaf area index from EO-1 ALI data and validation. IEEE Transactions on Geoscience and Remote Sensing, 41, 1260-1268

Liang, S., Fang, H., Morisette, J., Chen, M., Walthall, C., Daughtry, C., & Shuey, C. (2002b). Atmospheric correction of Landsat ETM+ land surface imagery: Part 2 Validation and Applications. IEEE Transactions on Geoscience and Remote Sensing, 40, 2736-2746

Liang, S., & Lewis, P. (1996). A Parametric Radiative Transfer Model for  Sky Radiance Distribution. Journal of Quantitative Spectroscopy and Radiative Transfer, 55, 181-189

Liang, S., & Mishchenko, M.I. (1997). Calculation of soil hot spot effects using coherent backscattering theory. Remote Sensing of Environment, 60, 163-173

Liang, S., Shuey, C., Fang, H., Russ, A., Chen, M., Walthall, C., Daughtry, C., & Hunt, R. (2003b). Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensing of Environment, 84, 25-41

Liang, S., & Strahler, A. (1994a). Retrival of surface BRDF from multi-angle remotely sensed data. Remote Sensing of Environment, 50, 18-30

Liang, S., Strahler, A., & Walthall, C. (1999). Retrieval of land surface albedo from satellite observations: A simulation study. Journal of Applied Meteorology, 38, 712-725

Liang, S., & Strahler, A.H. (1993a). An analytic BRDF model of canopy radiative transfer and its inversion. IEEE Transactions on Geoscience and Remote Sensing, 31, 1081-1092

Liang, S., & Strahler, A.H. (1993b). The Calculation of the Radiance Distribution of the Coupled Atmosphere-Canopy. IEEE Transactions on Geoscience and Remote Sensing, 31, 491-502

Liang, S., & Strahler, A.H. (1994b). A Four-Stream Solution for Atmospheric Radiative Transfer over an Non-Lambertian Surface. Applied Optics, 33, 5745-5753

Liang, S., & Strahler, A.H. (1995). An analytic radiative transfer model for a coupled atmosphere and leaf canopy. Journal of Geophysical Research, 100, 5085-5094

Liang, S., Strahler, A.H., Barnsley, M.J., Borel, C.C., Diner, D.J., Gerstl, S.A.W., Prata, A.J., & Walthall, C.L. (2000a). Multiangle remote sensing: Past, present and future. Remote Sensing Reviews, 18, 83-102

Liang, S., Strahler, A.H., Jin, X., & Zhu, Q. (1997b). Comparisons of radiative transfer models of vegetation canopies and laboratory measurements. Remote Sensing of Environment, 61, 129-138

Liang, S., Stroeve, J., & Box, J.E. (2005a). Mapping daily snow/ice shortwave broadband albedo from Moderate Resolution Imaging Spectroradiometer (MODIS): The improved direct retrieval algorithm and validation with Greenland in situ measurement. Journal of Geophysical Research-Atmospheres, 110, Art. No. D10109

Liang, S., Stroeve, J., Grant, I., Strahler, A., & Duvel, J. (2000b). Angular corrections to satellite data for estimating Earth's radiation budget. Remote Sensing Reviews, 18, 103-136

Liang, S., & Townshend, J.R.G. (1996a). A modified Hapke model for soil bidirectional reflectance. Remote Sensing of Environment, 55, 1-10

Liang, S., & Townshend, J.R.G. (1996b). A parametric soil BRDF model: A four-stream approximation. International Journal of Remote Sensing, 17, 1303-1315

Liang, S., Wang, D., Cheng, J., He, T., Tao, X., Yao, Y., & Zhang, X. (2017). Methodologies for integrating multiple high-level remotely sensed land products. In M.M. Crawford (Ed.), Comprehensive Remote Sensing Vol. 2 Remote sensing data processing and analysis methodology (p. in Press). Oxford, UK: Elsevier

Liang , S., Wang, K., Wang, W., Wang, D., Gui, S., Zhang, X., Mirmelstein, J., Zhu, X., Kim, H., Du, J., Running, S., Townshend, J., Tsay, S., Wolf, R., Schaaf, C., & Strahler, A. (2009). Mapping High-Resolution Land Surface Radiative Fluxes from MODIS: Algorithms and Preliminary Validation Results. In D. Li, J. Shan, & J. Gong (Eds.), Geospatial Technology for Earth Observation (pp. 141-176): Springer

Liang, S., Wang, K., Zhang, X., & Wild, M. (2010). Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 3, 225-240

Liang, S., Yu, Y., & Defelice, T.P. (2005b). VIIRS narrowband to broadband land surface albedo conversion: Formula and validation. International Journal of Remote Sensing, 26, 1019-1025

Liang, S., Zheng, T., Liu, R., Fang, H., Tsay, S.C., & Running, S. (2006a). Mapping incident Photosynthetically Active Radiation (PAR) from MODIS Data. Journal of Geophysical Research-Atmospheres, 111, Art. No. D15208, doi:15210.11029/12005JD006730.

Liang, S., Zheng, T., Wang, D.D., Wang, K.C., Liu, R.G., Tsay, S.C., Running, S., & Townshend, J. (2007). Mapping high-resolution incident photosynthetically active radiation over land from polar-orbiting and geostationary satellite data. Photogrammetric Engineering and Remote Sensing, 73, 1085-1089

Liang, S., Zhong, B., & Fang, H. (2006b). Improved estimation of aerosol optical depth from MODIS imagery over land surfaces. Remote Sensing of Environment, 104, 416-425

Liu, N., Liu, Q., Wang, L., Liang, S., Wen, J., Qu, Y., & Liu, S. (2013a). A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data. Hydrology and Earth System Sciences, 17, 2121-2129, doi:2110.5194/hess-2117-2121-2013

Liu, Q., Liang, S., Xiao, Z.Q., & Fang, H.L. (2014). Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data. Remote Sensing of Environment, 145, 25-37

Liu, Q., Wang, L., Qu, Y., Liu, N., Liu, S., Tang, H., & Liang, S. (2013b). Priminary Evaluation of the Long-Term GLASS Albedo Product. International Journal of Digital Earth, 6, 69-95,doi:10.1080/17538947.17532013.17804601

Liu, R., Liang, S., He, H., Liu, J., & Zheng, T. (2008). Mapping photosynthetically active radiation from MODIS data in China. Remote Sensing of Environment, 112, 998-1009

Liu, R., Liang, S., Liu, J., & Zhuang, D. (2006a). Continuous tree distribution in China : A comparison of two estimates from MODIS and Landsat data. Journal of Geophysical Research-Atmospheres, 111, D08101,doi:08110.01029/02005JD006039.

Liu, R.G., Liu, J.Y., & Liang, S. (2006b). Estimation of systematic errors of MODIS thermal infrared bands. Ieee Geoscience and Remote Sensing Letters, 3, 541-545

Lu, N., Liu, R., Liu, J., & Liang, S. (2010). An algorithm for estimating downward shortwave radiation from GMS-5 visible Imagery and its evaluation over China. Journal of Geophysical Research-Atmospheres, 115, D18102, doi:18110.11029/12009JD013457

Lu, X., Liu, R., Liu, J., & Liang, S. (2007). Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products. Photogrammetric Engineering and Remote Sensing, 73, 1129–1139

Ma, H., Liang, S., Xiao, Z., & Shi, H. (2017a). Simultaneous inversion of multiple land surface parameters from MODIS optical–thermal observations. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 240-254

Ma, H., Liu, Q., Liang, S., & Xiao, Z. (2017b). Simultaneous Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation and Surface Albedo from multiple-Satellite Data. IEEE Transactions on Geoscience and Remote Sensing, 55, 4334 - 4354doi:4310.1109/TGRS.2017.2691542

Meng, S., Xie, X., & Liang, S. (2017). Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags. Journal of Hydrology, 550, 568-579

Nolin, A., & Liang, S. (2000). Progress in directional reflectance modeling and applications for surface particulate media: snow and soils. Remote Sensing Reviews, 18, 307-342

Pan, Y.Z., Li, L., Zhang, J.S., Liang, S.L., Zhu, X.F., & Sulla-Menashe, D. (2012). Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment, 119, 232-242

Qin, J., Chen, Z., Yang, K., Liang, S., & Tang, W. (2011a). Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products. Applied Energy, 88, 2480-2489

Qin, J., Liang, S., Dai, Y., Li, X., Wang, J., & Liu, S. (2005). Assimilating MODIS land surface temperature product into Common Land Model using ensemble Kalman filter for estimating evapotranspiration. Remote Sensing of Environment

Qin, J., Liang, S., Li, X., & Wang, J. (2008). Development of the adjoint model of a canopy radiative transfer model for sensivity study and inversion of leaf area index. IEEE Transactions on Geoscience and Remote Sensing, 46, 2028-2037

Qin, J., Liang, S., Liu, R., Zhang, H., & Hu, B. (2007). A weak-constraint based data assimilation scheme for estimating surface turbulent fluxes. Ieee Geoscience and Remote Sensing Letters, 4, 649-653.

Qin, J., Liang, S., Yang, K., Kaihotsu, I., Liu, R.G., & Koike, T. (2009). Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal. Journal of Geophysical Research-Atmospheres, 114, 13

Qin, J., Tang, W.J., Yang, K., Lu, N., Niu, X.L., & Liang, S. (2015). An efficient physically based parameterization to derive surface solar irradiance based on satellite atmospheric products. Journal of Geophysical Research-Atmospheres, 120, 4975-4988

Qin, J., Yan, G., Liu, S., Liang, S., Zhang, H., Wang, J., & Li, X. (2006). Application of ensemble Kalman filter to geophysical parameter retrieval in remote sensing: A case study of kernal-driven BRDF model inversion. Science in China Series D: Earth Sciences, 49, 632-640

Qin, J., Yang, K., Liang, S., & Tang, W. (2012). Estimation of Daily Mean Photosynthetically Active Radiation under All-Sky Conditions Based on Relative Sunshine Data. Journal of Applied Meteorology and Climatology, 51, 150-160

Qin, J., Yang, K., Liang, S., Zhang, H., Ma, Y., Guo, X., & Chen, Z. (2011b). Evaluation of surface albedo from GEWEX-SRB and ISCCP-FD data against validated MODIS product over the Tibetan Plateau. Journal of Geophysical Research, 116, D24116

Qin, W., & Liang, S. (2000). Plane-parallel canopy radiation transfer modeling: recent advances and future directions. Remote Sensing Reviews, 18, 281-306

Qu, Y., Liang, S., Liu, Q., Li, X., Feng, Y., & Liu, S. (2016). Estimating Arctic sea-ice shortwave albedo from MODIS data. Remote Sensing of Environment, 186, 32-46

Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., & Liu, S. (2014). Improved direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52, 907-919

Ren, H., Liang, S., Yan, G., & Cheng, J. (2013). Empirical Method to Map Global Broadband Emissivities over Vegetated Surfaces. IEEE Transactions on Geoscience and Remote Sensing, 51, 2619-2631

Román, M.O., Gatebe, C.K., Shuai, Y., Wang, Z., Gao, F., Masek, J.G., He, T., Liang, S., & Schaaf, C.B. (2013). Use of in situ and airborne multiangle data to assess MODIS-and Landsat-based estimates of directional reflectance and albedo. IEEE Transactions on Geoscience and Remote Sensing, 51, 1393-1404

Schroeder, T.A., Hember, R., Coops, N.C., & Liang, S. (2009). Validation of solar radiation surfaces from MODIS and reanalysis data over topographically complex terrain. Journal of Applied Meteorology and Climatology, 48, 2441-2458

Shi, H., Xiao, Z., Liang, S., & Zhang, X. (2016a). Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model. Remote Sensing of Environment, 184, 40-57

Shi, L., Liang, S., Cheng, J., & Zhang, Q. (2016b). Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter. International Journal of Digital Earth, 1-19

Stroeve, J., Box, J.E., Gao, F., Liang, S.L., Nolin, A., & Schaaf, C. (2005). Accuracy assessment of the MODIS 16-day albedo product for snow: Comparisons with Greenland in situ measurements. Remote Sensing of Environment, 94, 46-60

Su, L., Li, X., Liang, S., & Strahler, A.H. (2003). Simulation of scaling effects of thermal emission from non-isothermal pixels with the typical three-dimensional structure. International Journal of Remote Sensing, 24, 3743-3753

Sun, L., Liang, S., Yuan, W., & Chen, Z. (2013). Improving a Penman–Monteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas. International Journal of Digital Earth, 6, 134-156,DOI:110.1080/17538947.17532013.17783635

Sun, L., Sun, R., Li, X., Liang, S., & Zhang, R. (2012). Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information. Agricultural and Forest Meteorology, 166-167, 175-187

Sun, W., & Liang, S. (2008). Methodologies for mapping plant functional types. In S. Liang (Ed.), Advances in Land Remote Sensing: System, Modeling, Inversion and Application (pp. 369-393). New York: Springer

Sun, W., Liang, S., Xu, G., Fang, H., & Dickinson, R. (2008). Mapping plant functional types from MODIS data using multisource evidential reasoning. Remote Sensing of Environment, 112, 1101-1024

Taberner, M., Pinty, B., Govaerts, Y., Liang, S., Verstraete, M., Gobron, N., & Widlowski, J.L. (2010). Comparison of MISR and MODIS land surface albedos: Methodology. Journal of Geophysical Research: Atmospheres, 115

Tang, W., Qin, J., Yang, K., Niu, X., Min, M., & Liang, S. (2017). An efficient algorithm for calculating photosynthetically active radiation with MODIS products. Remote Sensing of Environment, 194, 146-154

Tao, X., Liang, S., He, T., & Jin, H. (2016). Estimation of fraction of absorbed photosynthetically active radiation from multiple satellite data: Model development and validation. Remote Sensing of Environment, 184, 539-557

Tao, X., Liang, S., He, T., & Wang, D. (2017). Integration of satellite fraction of absorbed photosynthetically active radiation products: Method and validation IEEE Transactions on Geoscience and Remote Sensing, revised

Townshend, J.G.R., Huang, C., Kalluri, S., DeFries, D., Liang, S., & Yang, K. (2000). Beware of per-pixel characterization of land cover. International Journal of Remote Sensing, 21, 839-843

Van Niel, T., McVicar, T., Fang, H., & Liang, S. (2003). Calculating environmental moisture for per-field discrimination of rice crops. International Journal of Remote Sensing, 24, 885-890

Walthall, C., Dulaney, W., Anderson, M., Norman, J., Fang, H., & Liang, S. (2004). A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sensing of Environment, 92, 465-474

Wan, H., Wang, J., Liang, S., & Qin, J. (2009). Estimating Leaf Area Index by fusing MODIS and MISR Data. Spectroscopy and Spectral Analysis, 29, 3106-3111

Wang, D., & Liang, S. (2011). Integrating MODIS and CYCLOPES Leaf Area Index Products Using Empirical Orthogonal Functions. IEEE Transactions on Geoscience and Remote Sensing,, 49, 1513-1519

Wang, D., & Liang, S. (2014). Improving LAI mapping by integrating MODIS and CYCLOPES LAI products using optimal interpolation. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 445-457

Wang, D., & Liang, S. (2016). Estimating high-resolution top of atmosphere albedo from Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 178, 93-103

Wang, D., Liang, S., He, T., Cao, Y., & Jiang, B. (2015a). Surface Shortwave Net Radiation Estimation from FengYun-3 MERSI Data. Remote Sensing, 7, 6224-6239

Wang, D., Liang, S., He, T., & Shi, Q. (2015b). Estimating clear-sky all-wave net radiation from combined visible and shortwave infrared (VSWIR) and thermal infrared (TIR) remote sensing data. Remote Sensing of Environment, 167, 31-39

Wang, D., Liang, S., He, T., & Yu, Y. (2013). Direct Estimation of Land Surface Albedo from VIIRS Data: Algorithm Improvement and Preliminary Validation. Journal of Geophysical Research, 118, 12577-12586

Wang, D., Liang, S., He, T., Yu, Y.Y., Schaaf, C., & Wang, Z.S. (2015c). Estimating daily mean land surface albedo from MODIS data. Journal of Geophysical Research-Atmospheres, 120, 4825-4841

Wang, D., Liang, S., Liu, R., & Zheng, T. (2010a). Estimation of daily-integrated PAR from sparse satellite observations: comparison of temporal scaling methods. International Journal of Remote Sensing, 31, 1661-1677

Wang, D., Liang, S., & Tao, H. (2014). Mapping High-Resolution Surface Shortwave Net Radiation From Landsat Data. Geoscience and Remote Sensing Letters, IEEE, 11, 459-463

Wang, D., Liang, S., Zhou, Y., He, T., & Yu, Y. (2017a). A New Method for Retrieving Daily Land Surface Albedo From VIIRS Data. IEEE Transactions on Geoscience and Remote Sensing, 55, 1765-1775

Wang, D., Wang, J., & Liang, S. (2008a). Retrieving Crop Leaf Area Index by Assimilation of MODIS Data into the Coupled Crop Radiative Transfer and Growth Model. Remote Sensing of Environment, submitted

Wang, D., Wang, J., & Liang, S. (2010b). Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model. Science China Earth Sciences, 53, 721-730

Wang, K., Dickinson, R.E., Wild, M., & Liang, S. (2009a). Evidence for decadal variation in terrestrial evapotranspiration between 1982 and 2002. Part 2: Results. Journal of Geophysical Research-Atmospheres, in review

Wang, K., Dickinson, R.E., Wild, M., & Liang, S. (2010c). Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development. Journal of Geophysical Research: Atmospheres, 115

Wang, K., & Liang, S. (2009a). Estimation of Surface Net Radiation from Solar Shortwave Radiation Measurements. Journal of Applied Meteorology and Climatology, 48, 634-643

Wang, K., & Liang, S. (2009b). Evaluation of ASTER and MODIS land surface temperature and emissivity products using surface longwave radiation observations at SURFRAD sites. Remote Sensing of Environment, 113, 1156-1165

Wang, K., & Liang, S. (2009c). Global atmospheric downward longwave radiation over land surface under all-sky conditions from 1973 to 2008. Journal of Geophysical Research-Atmospheres, 114, D19101, doi:19110.11029/12009JD011800

Wang, K., Liang, S., Schaaf, C., & Strahler, A. (2010d). Evaluation of MODIS land surface shortwave and visible albedo products at FLUXNET sites. Journal of Geophysical Research-Atmospheres, 115, D17107, doi:17110.11029/12009JD013101

Wang, K.C., Dickinson, R.E., Wild, M., & Liang, S. (2010e). Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development. Journal of Geophysical Research-Atmospheres, 115, D20112, DOI:20110.21029/22009jd013671

Wang, K.C., Dickinson, R.E., Wild, M., & Liang, S. (2010f). Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 2. Results. Journal of Geophysical Research-Atmospheres, 115

Wang, K.C., & Liang, S. (2008). An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture. Journal of Hydrometeorology, 9, 712-727

Wang, W., & Liang, S. (2009d). Estimating High-Spatial Resolution Clear-Sky Land Surface Downwelling and Net Longwave Radiation from MODIS Data. Remote Sensing of Environment, 113, 745-754

Wang, W., & Liang, S. (2010). A Method for Estimating Clear-sky Instantaneous Land Surface Longwave Radiation from GOES Sounder and GOES-R ABI Data. Ieee Geoscience and Remote Sensing Letters, 7, 708-712

Wang, W., Liang, S., & Augustine, J.A. (2009b). Estimating clear-sky land surface longwave upwelling radiation from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 47, 1555-1570

Wang, W., Liang, S., & Meyer, T. (2008b). Validating MODIS land surface temperature products. Remote Sensing of Environment, 112, 623-635

Wang, X., Jia, K., Liang, S., Li, Q., Wei, X., Yao, Y., Zhang, X., & Tu, Y. (2017b). Estimating Fractional Vegetation Cover From Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model. IEEE Transactions on Geoscience and Remote Sensing, 55, 5539-5546

Wu, H., Liang, S., Tong, L., He, T., & Yu, Y. (2012a). Bidirectional reflectance for multiple snow-covered land types from MISR products. Ieee Geoscience and Remote Sensing Letters, 9, 994-998

Wu, H., Zhang, X., Liang, S., Yang, H., & Zhou, G. (2012b). Estimation of clear-sky Land Surface Longwave Radiation from MODIS data products by Merging Multiple Models. Journal of Geophysical Research, in press

Xia, J., Chen, Y., Liang, S., Liu, D., & Yuan, W. (2015). Global simulations of carbon allocation coefficients for deciduous vegetation types. Tellus B: Chemical and Physical Meteorology, 67, 28016

Xia, J., Liang, S., Chen, J., Yuan, W., Liu, S., Li, L., Cai, W., Zhang, L., Fu, Y., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Zhou, G., Asanuma, J., Chen, S., Du, M., Davaa, G., Kato, T., Liu, Q., Liu, S., Li, S., Shao, C., Tang, Y., & Zhao, X. (2014a). Satellite-Based Analysis of Evapotranspiration and Water Balance in the Grassland Ecosystems of Dryland East Asia. Plos One, 9, e97295

Xia, J., Liu, S., Liang, S., Chen, Y., Xu, W., & Yuan, W. (2014b). Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006. Remote Sensing, 6, 1783-1802

Xiao, Z., Liang, S., & Jiang, B. (2017a). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230

Xiao, Z., Liang, S., Tian, X., Jia, K., Yao, Y., & Jiang, B. (2017b). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI10.1109/JSTARS.2017.2744979

Xiao, Z., Liang, S., Wang, J., Jiang, B., & Li, X. (2011). Real-time inversion of leaf area index from MODIS time series data. Remote Sensing of Environment, 115, 97-106

Xiao, Z., Liang, S., Wang, J., Song, J., & Wu, X. (2009). A temporally integrated inversion method for estimating leaf area index from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 47, 2536-2545

Xiao, Z., Liang, S., Wang, J., Xie, D., Song, J., & Fensholt, R. (2015). A Framework for the Simultaneous Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation and Albedo from MODIS Time Series Data. IEEE Transactions on Geoscience and Remote Sensing, 53, 3178-3197

Xiao, Z., Liang, S., Wang, J., & Zhao, X. (2016a). Long Time Series Global Land Surface Satellite (GLASS) Leaf Area Index Product Derived from MODIS and AVHRR Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, DOI:10.1109/TGRS.2016.2560522

Xiao, Z., Liang, S., Wang, T., & Jiang, B. (2016b). Retrieval of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from VIIRS Time-Series Data. Remote Sensing, 8, 351

Xiao, Z., Wang, J., Liang, S., & al., e. (2012). Variational retrieval of leaf area index from MODIS time series data: Examples from the Heihe River Basin, North-west China. International Journal of Remote Sensing, 33, 730-745

Xiao, Z., Wang, T., Liang, S., & Sun, R. (2016c). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337

Xiao, Z.Q., Liang, S., Wang, J.D., Chen, P., Yin, X.J., Zhang, L.Q., & Song, J.L. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223

Xie, X., Meng, S., Liang, S., & Yao, Y. (2014). Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy. Hydrol. Earth Syst. Sci., 18, 3923-3936

Xu, T., Bateni, S., & Liang, S. (2015). Estimating turbulent heat fluxes with a weak-constraint data assimilation scheme: A case study (HiWATER-MUSOEXE). Ieee Geoscience and Remote Sensing Letters, 12, 68-72

Xu, T., Bateni, S.M., Liang, S., Entekhabi, D., & Mao, K. (2014). Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites. Journal of Geophysical Research: Atmospheres, 119, 10,780-710,798, doi:710.1002/2014JD021814

Xu, T., Liang, S., & Liu, S. (2011a). Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter. J. Geophys. Res, 116, DOI: 10.1029/2010JD015150

Xu, T., Liu, S., Liang, S., & Qin, J. (2011b). Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into Common Land Model. Journal of Hydrometeorology, 12, 227-244

Yang, L., Jia, K., Liang, S., Liu, J., & Wang, X. (2016). Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data. Remote Sensing, 8, 682

Yang, L., Jia, K., Liang, S., Wei, X., Yao, Y., & Zhang, X. (2017). A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data. Remote Sensing, 9, 857

Yao, Y., Liang, S., Li, X., Chen, J., Liu, S., Jia, K., Zhang, X., Xiao, Z., Fisher, J.B., & Mu, Q. (2017a). Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242, 55-74

Yao, Y., Liang, S., Li, X., Chen, J., Wang, K., Jia, K., Cheng, J., Jiang, B., Fisher, J.B., Mu, Q., Grünwald, T., Bernhofer, C., & Roupsard, O. (2015). A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes. Remote Sensing of Environment, 165, 216-233

Yao, Y., Liang, S., Li, X., Hong, Y., Fisher, J.B., Zhang, N., Chen, J., Cheng, J., Zhao, S., Zhang, X., Jiang, B., Sun, L., Jia, K., Wang, K., Chen, Y., Mu, Q., & Feng, F. (2014a). Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119, 2013JD020864

Yao, Y., Liang, S., Li, X., Zhang, Y., Chen, J., Jia, K., Zhang, X., Fisher, J.B., Wang, X., & Zhang, L. (2017b). Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method. Journal of Hydrology, 553, 508-526

Yao, Y., Liang, S., Qin, Q., Wang, K., & Zhao, S. (2010a). Monitoring global land surface drought based on a hybrid evapotranspiration model. International Journal of Applied Earth Observation and Geoinformation, 12, S266-S776

Yao, Y., Liang, S., Xie, X., Cheng, J., Jia, K., Li, Y., & Liu, R. (2014b). Estimation of the terrestrial water budget over northern China by merging multiple datasets. Journal of Hydrology, 519, Part A, 50-68

Yao, Y., Liang, S., Yu, J., Zhao, S., Lin, Y., Jia, K., Zhang, X., Cheng, J., Xie, X., Sun, L., Wang, X., & Zhang, L. (2017c). Differences in estimating terrestrial water flux from three satellite-based Priestley-Taylor algorithms. International Journal of Applied Earth Observation and Geoinformation, 56, 1-12

Yao, Y., Liang, S., Zhao, S., Zhang, Y., Qin, Q., Cheng, J., Jia, K., Xie, X., Zhang, N., & Liu, M. (2014c). Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration. Remote Sensing, 6, 880-904

Yao, Y.J., Liang, S., Cheng, J., Liu, S.M., Fisher, J.B., Zhang, X.D., Jia, K., Zhao, X., Qing, Q.M., Zhao, B., Han, S.J., Zhou, G.S., Zhou, G.Y., Li, Y.L., & Zhao, S.H. (2013). MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm. Agricultural and Forest Meteorology, 171, 187-202

Yao, Y.J., Liang, S., Li, X.L., Liu, S.M., Chen, J.Q., Zhang, X.T., Jia, K., Jiang, B., Xie, X.H., Munier, S., Liu, M., Yu, J., Lindroth, A., Varlagin, A., Raschi, A., Noormets, A., Pio, C., Wohlfahrt, G., Sun, G., Domec, J.C., Montagnani, L., Lund, M., Eddy, M., Blanken, P.D., Grunwald, T., Wolf, S., & Magliulo, V. (2016). Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations. Agricultural and Forest Meteorology, 223, 151-167

Yao, Y.J., Liang, S., Qin, Q.M., & Wang, K.C. (2010b). Monitoring Drought over the Conterminous United States Using MODIS and NCEP Reanalysis-2 Data. Journal of Applied Meteorology and Climatology, 49, 1665-1680

Yuan, W., Li, X., Liang, S., Cui, X., Dong, W., Liu, S., Xia, J., Chen, Y., Liu, D., & Zhu, W. (2014a). Characterization of locations and extents of afforestation from the Grain for Green Project in China. Remote Sensing Letters, 5, 221-229

Yuan, W., Liang, S., Liu, S., Weng, E., Luo, Y., Hollinger, D., & Zhang, H. (2012a). Improving model parameter estimation using coupling relationships between vegetation production and ecosystem respiration. Ecological Modelling, 240, 29-40

Yuan, W., Liu, S., Dong, W., Liang, S., Zhao, S., Chen, J., Xu, W., Li, X., Barr, A., Andrew Black, T., Yan, W., Goulden, M.L., Kulmala, L., Lindroth, A., Margolis, H.A., Matsuura, Y., Moors, E., van der Molen, M., Ohta, T., Pilegaard, K., Varlagin, A., & Vesala, T. (2014b). Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat Commun, 5

Yuan, W., Liu, S., Liang, S., Tan, Z., Liu, H., & Young, C. (2012b). Estimations of evapotranspiration and water balance with uncertainty over the Yukon River Basin. Water Resources Management, 26, 2147-2157

Yuan, W., Luo, Y., Liang, S., Yu, G., Niu, S., Stoy, P., Chen, J., Desai, A., Lindroth, A., & Gough, C. (2011). Thermal adaptation of net ecosystem exchange. Biogeosciences, 8, 1453-1463

Zhang, X., Liang, S., Song, Z., Niu, H., Wang, G., Tang, W., Chen, Z., & Jiang, B. (2016a). Local Adaptive Calibration of the Satellite-Derived Surface Incident Shortwave Radiation Product Using Smoothing Spline. Geoscience and Remote Sensing, IEEE Transactions on, 54, 1156-1169

Zhang, X., Liang, S., Wang, G., Yao, Y., Jiang, B., & Cheng, J. (2016b). Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations. Remote Sensing, 8, 225

Zhang, X., Liang, S., Zhou, G., Wu, H., & Zhao, X. (2014a). Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sensing of Environment, 152, 318-332

Zhang, Y., Liang, S., & Sun, G. (2014b). Mapping forest biomass with GLAS and MODIS data over Northeast China. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 7, 140-152

Zhang, Y., Qu, Y., Wang, J., & Liang, S. (2012). Estimating leaf area index from MODIS and surface meteorological data using a dynamic Bayesian network. Remote Sensing of Environment, 127, 30-43

Zhang, Z.Y., Kalluri, S.N.V., Jaja, J., Liang, S.L., & Townshend, J.R.G. (1998). Models and high-performance algorithms for global BRDF retrieval. IEEE Computational Science and Engineering, 5, 16-29

Zhao, X., Liang, S., Liu, S., Wang, J., Qin, J., Li, Q., & Li, X. (2008). Modified dark object atmospheric correction method for hyperspectral remote sensed data. Science in China D - Earth Sciences, 51, 349-356

Zheng, T., & Liang, S. (2011). A Bayesian approach to integrate satellite-estimated instantaneous photosynthetically active radiation product for daily value calculation. Journal of Geophysical Research, 116, D15202

Zheng, T., Liang, S., & Wang, K.C. (2008). Estimation of incident PAR from GOES imagery. Journal of Applied Meteorology and Climatology, 47, 853-868

Zhong, B., Liang, S., & Holben, B. (2007). Validating a new algorithm for estimating aerosol optical depths from MODIS imagery. International Journal of Remote Sensing, 28, 4207-4214

Zhou, H., Wang, J., Liang, S., & Xiao, Z. (2017). Extended Data-Based Mechanistic Method for Improving Leaf Area Index Time Series Estimation with Satellite Data. Remote Sensing, 9, 533

Zhou, Y., Wang, D., Liang, S., Yu, Y., & He, T. (2016). Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps. Remote Sensing, 8, 137