Introduction to the GLASS product algorithms

1. Leaf area index (LAI)

    The GLASS LAI product from MODIS surface-reflectance time-series data is based on general regression neural networks (GRNNs) (Xiao et al. 2014). Unlike existing neural network methods that use only remote-sensing data acquired at a specific time to retrieve LAI, the preprocessed MODIS reflectance data (Tang et al. 2013) for one-year were inputed into the GRNNs to estimate the one-year LAI profiles. This method was applied to generate LAI product from MODIS surface-reflectance data.

    The similar algorithm has also been applied to generate the LAI product from the Long-Term Data Record (LTDR) AVHRR surface-reflectance data (Xiao et al. 2016). Because of inadequate information available for atmospheric correction, the AVHRR surface reflectance data is highly noisy. An innovative method has been developed to produce the high-quality surface reflectance NDVI products(Xiao et al. 2017b; Xiao et al. 2015b).

    Compared to other long-term LAI products, the GLASS LAI products show higher quality and accuracy (Xiao et al. 2017a; Xu et al. 2018).

References:[1]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

[2]Tang, H., Yu, K., Hagolle, O., Jiang, K., Geng, X., & Zhao, Y. (2013). A Cloud Detection Method Based on Time Series of MODIS Surface Reflectance Images. International Journal of Digital Earth, DOI: 10.1080/17538947.17532013.17833313

[3]Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X., & Song, J. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318

[4]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, 10, 5551-5568

[5]Xiao, Z., Liang, S., Wang, T., & Liu, Q. (2015b). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. Remote Sensing, 7, 9844-9864

[6]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

[7]Xu, B., Li, J., Park, T., Liu, Q., Zeng, Y., Yin, G., Zhao, J., Fan, W., Yang, L., & Knyazikhin, Y. (2018). An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209, 134-151

2. Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

    The GLASS FAPAR is approximated by one minus the transmittance of PAR through the entire canopy, which can be calculated from the GLASS LAI product and other information (Xiao et al. 2015a). This algorithm is efficient to estimate the FAPAR values physically consistent with the LAI product. The GLASS FAPAR product from MODIS data is the instantaneous value at 10:30 am local time, close approximation of daily average PAPAR.

    The GLASS FAPAR product from AVHRR data is similarly generated. The long-term GLASS FAPAR product was also compared with two similar products and shows high quality and accuracy (Xiao et al. 2018).

References:[1]Xiao, Z., Liang, S., Sun, R., Wang, J., & Jiang, B. (2015a). Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf area index product. Remote Sensing of Environment, 171, 105-117

[2]Xiao, Z., Liang, S., & Sun, R. (2018). Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524

3. Fractional Vegetation Coverage (FVC)

    The GLASS FVC product algorithm was based on the machine learning methods using the training samples generated from global distributed high spatial resolution satellite data (Jia et al. 2015). Initially, the GLASS FVC product algorithm for MODIS data was generated using the general regression neural networks (GRNNs) method with training samples data Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) data (Jia et al. 2015). However, in the process of generating the long term global GLASS FVC product, it was found that the computational efficiency of the GRNNs method was not satisfactory. Therefore, four machine learning methods including back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. The MARS method was found to be a suitable algorithm for generating the long term GLASS FVC product from MODIS data (Yang et al. 2016).

    The GLASS FVC algorithm for AVHRR data was also developed to be in concert with the GLASS MODIS FVC product. It was based on the GLASS MODIS FVC product to achieve continuity of FVC estimates from both AVHRR and MODIS data.

    Extensive validation experiments have been conducted using the estimates from high-resolution satellite data and ground measurements (Jia et al. 2016; Jia et al. 2018). The details of the algorithms and the validation results are recently summarized (Jia et al. 2019)

References:[1]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. (2015). 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

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

[3]Jia, K., Liang, S.L., Wei, X.Q., Yao, Y.J., Yang, L.Q., Zhang, X.T., & Liu, D.Y. (2018). Validation of Global LAnd Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region. Remote Sensing Letters, 9, 847-856

[4]Jia, K., Yang, L., Liang, S., Xiao, Z., Zhao, X., Yao, Y., Zhang, X., Jiang, B., & Liu, D. (2019). Long-Term Global Land Surface Satellite (GLASS) Fractional Vegetation Cover Product Derived From MODIS and AVHRR Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, doi:10.1109/jstars.2018.2854293

[5]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

4. Broadband Albedo (Albedo)

    The GLASS albedo products include three spectral ranges: total shortwave, visible and near-IR under actual atmospheric conditions (so-called blue-sky albedos).

    The GLASS albedo product is based on the integration of two algorithms through a temporal filter scheme (Liu et al. 2013a; Liu et al. 2013b; Qu et al. 2014). One algorithm is based on the surface reflectance that is converted from the top-of-atmosphere (TOA) radiance through atmospheric correction, and another algorithm is based on the estimated surface albedo directly from TOA observations without atmospheric correction. These algorithms are robust and have been applied to many other satellite observations. Recent efforts have extended to include sea ice (Qu et al. 2016) and ocean water(Feng et al. 2016).

References:[1]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

[2]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

[3]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

[4]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

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

5. Broadband Emissivity (BBE)

    The GLASS BBE product represents the emissivity value at 8-13.5 µm since it is the optimal spectral range for estimating surface longwave net radiation (Cheng and Liang 2013).

    The algorithm estimates surface broadband emissivity for land surface types include: water, snow or ice, bare soils (0 < NDVI< 0.156), vegetated surfaces (NDVI>0.156), and transition zone(0.1 < NDVI< 0.2). BBEs of water and snow or ice were set to 0.985 based on a combination of BBE calculated from the emissivity spectra in the ASTER spectral library and the MODIS UCSB spectral library, and BBE values simulated using radiative transfer model (Cheng et al. 2010).

    For bare soils, we estimate BBE using MODIS spectral albedos(Cheng and Liang 2014). This algorithm takes advantage of both ASTER BBE and MODIS shortwave albedo products,as well as the established non-linear relationship between ASTER BBE and seven MDOSI spectral albedos. The rationality of the algorithm was verified by a study (Cheng et al. 2018) that demonstrated the physical linkage between land surface emissive and reflective variables over non-vegetated surfaces.

    For vegetated surfaces, we estimate the BBE by constructing a look-up table (LUT) based on the 4SAIL radiative transfer model(Cheng et al. 2016). The BBE of the vegetated surfaces was derived from the LUT using three inputs: leaf BBE, soil BBE, and LAI.

    The method for estimating transition BBE is similar to that for bare soils. Areas of overlapping bare soils and transition zone as well as of transition zone and vegetated surfaces are used. Their BBE is assumed to be their average values.

    The algorithm for estimating BBE from AVHRR data was similar to that designed for MODIS(Cheng and Liang 2013). The difference is that we use AVHRR surface visible near-infrared reflectance to replace the MODIS spectral albedos.

References:[1]Cheng, J., & Liang, S. (2013). 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

[2]Cheng, J., Liang, S., Weng, F., Wang, J., & Li, X. (2010). 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

[3]Cheng, J., & Liang, S. (2014). Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product. Journal of Geophysical Research: Atmospheres, 119, 614-634

[4]Cheng, J., Liang, S.L., Nie, A.X., & Liu, Q. (2018). Is There a Physical Linkage Between Surface Emissive and Reflective Variables Over Non-Vegetated Surfaces? Journal of the Indian Society of Remote Sensing, 46, 591-596

[5]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

6. Downward Shortwave Radiation (DSR)

    The GLASS DSR (V1.0) was developed based on an improved LUT method using multiple polar-orbiting and geostationary satellite data (Zhang et al. 2014) . Since it too time consuming to process and generate a long-term DSR product using these data. the GLASS DSR product (V2.0) was generated from MODIS TOA reflectances based on a direct-estimation method (Zhang et al. 2019). A machine learning algorithm has been developed to estimate DSR from AVHRR data (Yang et al. 2018).

References:[1]Zhang, X., Liang, S., Zhou, G., Wu, H., & Zhao, X. (2014). Generating Global Land Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sensing of Environment, 152, 318-332

[2]Zhang, X., Wang, D., Liu, Q., Yao, Y., Jia, K., He, T., Jiang, B., Wei, Y., Ma, H., Zhao, X., Li, W., & Liang, S. (2019). An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product from MODIS Data. IEEE Transactions on Geoscience and Remote Sensing, in press

[3]Yang, L., Zhang, X., Liang, S., Yao, Y., Jia, K., & Jia, A. (2018). Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method. Remote Sensing, 10, 185

7. Photosynthetically Active Radiation (PAR)

    The GLASS PAR product is based on the similar algorithms to the GLASS DSR product.

8. Net Long-wave Radiation (LWNR)

    Under the general framework of the hybrid method, we developed linear and dynamic learning neural network (DLNN) models for estimating the global 1-km instantaneous clear-sky long-wave upwelling radiation (LWUP) from the top-of-atmosphere radiance of MODIS TIR channels 29, 31, and 32(Cheng and Liang 2016).

    At the same time, we developed an efficient hybrid method for estimating 1 km instantaneous clear-sky LWDN from MODIS TIR observations and the MODIS near-infrared column water vapor (CWV) data product(Cheng et al. 2017).

    The LWDN was formulated as a nonlinear function of LWUP estimated from the MODIS TOA radiance of channels 29, 31, and 32, as well as CWV and the MODIS TOA radiance of channel 29. Because the LWDN is overestimated over high-elevation area with extremely high CWV, we developed a power function relating LWDN to CWV as a complementary method.

    Regarding the longwave radiation at cloudy-sky, we estimated the LWDN using the single layer cloud model from MODIS cloud product; the cloud-sky LWUP was calculated from LST in MOD06/MYD06 and GLASS BBE product.

    Before 2000, we adopted the parameterization schemes to calculate clear-sky LWDN from reanalysis data (Guo et al. 2019), and using GLASS LST and BBE product to calculate clear sky LWUP.

References:[1]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

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

[3]Guo, Y., Cheng, J., & Liang, S. (2019). Comprehensive assessment of parameterization methods for estimating clear-sky surface downward longwave radiation. Theoretical and Applied Climatology, 1-14

9. Land Surface Temperature (LST)

    The GLASS LST product uses a multi-algorithm ensemble approach based on the combination of nine split-window algorithms (SWA) with the Bayesian Model Averaging (BMA) model (Zhou et al. 2019). The purpose of the multi-algorithm ensemble approach is to obtain a stable estimate of LST. The individual SWAs were determined through global training, sensitivity analysis, and the global test of 17 candidate SWAs that are widely accepted by the scientific communities(Huang et al. 2016). We found that 11 of the 17 candidate SWAs have good accuracy in training; 9 of the 11 SWAs have low sensitivity to the uncertainties of the inputted land surface emissivity (LSE) and atmospheric column water vapor content (CWV)

References:[1]Huang, F., Zhou, J., Tao, J., Tan, X., Liang, S., & Cheng, J. (2016). PMODTRAN: A parallel implementation based on MODTRAN for massive remote sensing data processing. International Journal of Digital Earth, 9, 819-834

[2]Zhou, J., Liang, S., Cheng, J., Wang, Y., & Ma, J. (2019). The GLASS Land Surface Temperature Product. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 12, 10.1109/JSTARS.2018.2870130

10. Net Radiation (NR)

    The GLASS net radiation product is based on a relationship between all-wave net radiation and incident shortwave radiation in conjunction with other information (Jiang et al. 2018). The earlier version of the algorithm focused on day-time net radiation using a linear relationship (Jiang et al. 2015). After comprehensive evaluation of different machine learning techniques for the nonlinear relationship (Jiang et al. 2014), the multivariate adaptive regression splines (MARS) model was selected as the GLASS net radiation product algorithm. The results of validation using ground measurements collected from 142 sites distributed worldwide show that the overall accuracy of the GLASS daytime Rn product was satisfactory, with an R2 of 0.80, root-mean-square error of 51.35 Wm-2, and mean bias error of 0.11 Wm-2(Jiang et al. 2018).

References:[1]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

[2]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

[3]Jiang, B., Liang, S., Jia, A., Xu, J., Zhang, X., Xiao, Z., Zhao, X., Jia, K., & Yao, Y. (2018). Validation of the Surface Daytime Net Radiation Product From Version 4.0 GLASS Product Suite. Ieee Geoscience and Remote Sensing Letters, 1-5

11. Evapotranspiration (ET)

    GLASS ET product algorithm is based on the multi-model ensemble method, that is the Bayesian model averaging (BMA) method which merges five process-based ET algorithms to improve ET estimate (Yao et al. 2014). The five process-based ET algorithms include MODIS ET product algorithm (MOD16) (Mu et al., 2011), revised remote-sensing-based Penman-Monteith ET algorithm (RRS-PM) (Yuan et al., 2010), Priestley-Taylor-based ET algorithm (PT-JPL) (Fisher et al., 2008), modified satellite-based Priestley-Taylor ET algorithm (MS-PT) (Yao et al. 2013) and semi-empirical Penman ET algorithm of the University of Maryland (UMD-SEMI) (Wang et al., 2010).

References:[1]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

[2]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. (2014). Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119, 2013JD020864

12. Gross Primary Production (GPP)

    GLASS-GPP algorithm originates from EC-LUE model (Eddy Covariance – Light Use Efficiency) (Yuan et al. 2007). On the basis of the theory of light use efficiency, the EC-LUE model relies on two assumptions: first, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The original EC-LUE is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress) (Yuan et al., 2007).

    The later version of EC-LUE substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET (Yuan et al. 2010).

    To accurately indicate the long-term changes of GPP, the GLASS-GPP product used the latest version of EC-LUE, which integrates the impacts of several environmental variables: atmospheric CO2 concentration, radiation components and atmospheric water vapor pressure (VPD) (Yuan et al., in preparation).

    The EC-LUE model has been validated widely throughout North America, Europe and East Asia by using the measurements of eddy covariance towers (Yuan et al., 2007; 2010)(Li et al. 2013; Yuan et al. 2014). These validations showed that the EC-LUE model can successfully reproduce the spatial and temporal variabilities of GPP over the various ecosystem types.

    Several model comparisons also indicate the better performance of EC-LUE than other LUE models. Previous study compared EC-LUE model and MODIS-GPP products based on the measurements of eddy covariance towers at southeastern China, and found the EC-LUE model performed better than the MODIS algorithms (Xu et al., 2013). A recent study compared eight satellite-based GPP models over various major grassland ecosystem types and found the EC-LUE model performed best (Jia et al., 2018).

References:[1]Yuan, W.P., Liu, S., Zhou, G.S., Zhou, G.Y., Tieszen, L.L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A.H., Goulden, M.L., Hollinger, D.Y., Hu, Y., Law, B.E., Stoy, P.C., Vesala, T., Wofsy, S.C., & AmeriFlux, C. (2007). Deriving a light use efficiency model from eddy covariance flux data for predicting dailygross primary production across biomes. Agricultural and Forest Meteorology, 143, 189-207

[2]Yuan, W.P., Liu, S.G., Yu, G.R., Bonnefond, J.M., Chen, J.Q., Davis, K., Desai, A.R., Goldstein, A.H., Gianelle, D., Rossi, F., Suyker, A.E., & Verma, S.B. (2010). Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114, 1416-1431

[3]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

[4]Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., & Beringer, J. (2014). Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agricultural and Forest Meteorology, 192, 108-120

13. Snow Cover Extent (SCE)

    The GLASS SCE product from AVHRR data is based on the revised Normalized Difference Snow Index (NDSI) by replacing the shortwave infrared reflectance at 1.64 μm by the reflectance at 3.7 μm, with an effective cloud screening algorithm and variable thresholds according to the climate zones (Chen et al. 2018). Multi sources of information are also been used, as demonstrated in previous studies (Chen et al. 2016; Chen et al. 2015; Chen et al. 2017).

References:[1]Chen, X., Liang, S., Cao, Y., He, T., & Wang, D. (2015). Observed contrast changes in snow cover phenology in northern middle and high latitudes from 2001–2014. Scientific Reports, 5, 16820

[2]Chen, X., Liang, S., Cao, Y., & He, T. (2016). Distribution, attribution, and radiative forcing of snow cover changes over China from 1982 to 2013. Climatic Change, 137, 363-377

[3]Chen, X., Long, D., Hong, Y., Liang, S., & Hou, A. (2017). Observed radiative cooling over the Tibetan Plateau for the past three decades driven by snow cover induced surface albedo anomaly. Journal of Geophysical Research: Atmospheres, 122, 6170-6185

[4]Chen, X., Long, D., Liang, S., He, L., Zeng, C., Hao, X., & Hong, Y. (2018). Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data. Remote Sensing of Environment, 215, 284-299

14. Global Aboveground Biomass (AGB)

References:[1]Zhang, Y., & Liang, S. (2020). Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map. Remote Sensing, 12(16), 2559.

[2]L. Yang, s. liang and Y. Zhang, "A new method for generating a global forest aboveground biomass map from multiple high-level satellite products and ancillary information," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2020.2987951