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International Journal of Digital Earth 2023年第1期16卷 2002-2012页

作者： Chuan Zhan shunlin liang School of Resources and Environmental Engineering Wuhan University of Science and TechnologyWuhanPeople's Republic of China Department of Geography The University of Hong KongPokfulamPeople's Republic of China

Top-of-atmosphere(TOA)outgoing longwave radiation(OLR),a key component of the Earth’s energy budget,serves as a diagnostic of the Earth’s climate system response to incoming solar radiation.However,existing products are typically estimated using broadband sensors with coarse spatial resolutions.This paper presents a machine learning method to estimate TOA OLR by directly linking Moderate Resolution Imaging Spectroradiometer(MODIS)TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System(CERES)and other information,such as the viewing geometry,land surface temperature and cloud top temperature determined by Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2).Models are built separately under clear-and cloudy-sky conditions using a gradient boosting regression tree.Independent test results show that the root mean square errors(RMSEs)of the clear-sky and cloudy-sky models for estimating instantaneous values are 4.1 and 7.8 W/m^(2),respectively.Real-time conversion ratios derived from CERES daily and hourly OLR data are used to convert the instantaneous MODIS OLR to daily results.Inter-comparisons of the daily results show that the RMSE of the estimated MODIS OLR is 8.9 W/m^(2) in East Asia.The developed high resolution dataset will be beneficial in analyzing the regional energy budget.

关键词： TOA outgoing longwave radiation MODIS CERES machine learning Earth’s energy budget

Simultaneous retrieval of land surface temperature and emissivity from the FengYun-4A advanced geosynchronous radiation imager

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International Journal of Digital Earth 2022年第1期15卷 198-225页

作者： Weihan Liu Jiancheng Shi shunlin liang Shugui Zhou Jie Cheng State Key Laboratory of Remote Sensing Science Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute of Remote Sensing Science and Engineering Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal UniversityBeijingPeople’s Republic of China National Space Science Center Chinese Academy of SciencesBeijingPeople’s Republic of China Department of Geographical Science University of MarylandCollege ParkMDUSA School of the Geo-Science & Technology Zhengzhou UniversityZhengzhouPeople’s Republic of China

This paper extends a new temperature and emissivity separation(TES)algorithm for retrieving land surface temperature and emissivity(LST and LSE)to the Advanced Geosynchronous Radiation Imager(AGRI)onboard Fengyun-4A,China’s newest geostationary meteorological satellite.The extended TES algorithm was named the AGRI TES algorithm.The AGRI TES algorithm employs a modified water vapor scaling(WVS)method and a recalibrated empirical function over vegetated surfaces.In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE.LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and−0.30 K and 2.18 K at nighttime,respectively;the AGRI official LST is systematically underestimated.Compared with the MODIS LST and LSE products(MYD21),the average bias and RMSE of AGRI TES LST are−0.26 K and 1.65 K,respectively.The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity.This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE,and the potential of the AGRI TES algorithm in producing operational LST and LSE products.

关键词： Land surface temperature emissivity temperature and emissivity separation 4SAIL water vapor scaling geostationary satellite

Variability and trends in land surface longwave radiation fluxes from six satellite and reanalysis products

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International Journal of Digital Earth 2023年第1期16卷 2912-2940页

作者： Jianglei Xu shunlin liang Tao He HanMab Yufang Zhang Guodong Zhang Hui liang School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China Department of Geography University of Hong KongHong KongPeople’s Republic of China

Earth surface longwave radiation(SLR),including downward(DLR),upward(ULR),and net longwave radiation(NLR),significantly impacts the surface radiation budget and global climate evolution.However,the spatiotemporal variation in SLR remains poorly understood.In this study,three satellite products(GLASS-MODIS V40,GLASS-AVHRR,and CERES-SYN)and three reanalysis datasets(ERA5,MERRA-2,and GLDAS)were validated using ground measurements from 288 sites at seven observation networks.The mean biases and root mean square errors of the monthly DLR(ULR,NLR)estimates from the six products were-6.36(-3.56,-2.86)Wm^(-2) and 16.63(14.33,13.38)Wm^(-2),respectively.Large differences in the spatial distribution of the SLR were mainly observed at high-latitude,high-altitude and desert/barren-covered regions.Large interannual variability was detected at high latitudes.GLASS-AVHRR and ERA5 better captured the long-term variability in DLR and ULR,whereas GLASS-AVHRR and MERRA-2 better detected trends in NLR.An increasing trend in DLR and ULR was observed between 1982 and 2015,followed by a decreasing trend from 2016 to 2021;the NLR flux did not exhibit a significant trend.Overall,the GLASS-AVHRR and ERA5 SLR estimates were more accurate and stable than those of the,other products in accuracy,spatiotemporal distribution,and trend analysis.

关键词： Surfacelongwaveradiation satellite remote sensing spatiotemporal distributions annual mean value long-term variability

Estimating global land surface broadband thermal-infrared emissivityusing advanced very high resolution radiometer optical data

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International Journal of Digital Earth 2013年第S1期6卷 34-49页

作者： Jie Cheng shunlin liang State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina Department of Geographical Science University of MarylandCollege ParkMDUSA

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.

关键词： earth observation broadband emissivity global land surface satellite soil taxonomy advanced very high resolution radiometer moderate-resolution imaging spectroradiometer

Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

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International Journal of Digital Earth 2022年第1期15卷 1784-1816页

作者： Shaopeng Li Bo Jiang shunlin liang Jianghai Peng Hui liang Jiakun Han Xiuwan Yin Yunjun Yao Xiaotong Zhang Jie Cheng Xiang Zhao Qiang Liu Kun Jia The State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesBeijingPeople’s Republic of China Beijing Engineering Research Center for Global Land Remote Sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Department of Geographical Sciences University of MarylandCollege ParkMDUSA Beijing Engineering Research Center for Global Land Remote Sensing Products College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingPeople’s Republic of China

The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high *** had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.

关键词： Net radiation energy balance mid-low latitude model comparison machine learning deep learning MODIS ERA5

Significant discrepancies of land surface daily net radiation among ten remotely sensed and reanalysis products

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International Journal of Digital Earth 2023年第1期16卷 3725-3752页

作者： Xiuwan Yin Bo Jiang shunlin liang Shaopeng Li Xiang Zhao Qian Wang Jianglei Xu Jiakun Han Hui liang Xiaotong Zhang Qiang Liu Yunjun Yao Kun Jia Xianhong Xie The State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesBeijingPeople’s Republic of China China and Beijing Engineering Research Center for Global Land Remote Sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Department of Geography University of Hong KongHong KongPeople’s Republic of China School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China Institute of Geography University of Bern BernSwitzerland Oeschger Centre for Climate Change Research University of Bern BernSwitzerland Pengcheng Labotory ShenzhenPeople’s Republic of China

Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.

关键词： All-wave net radiation remote sensing reanalysis evaluation spatio-temporal variation product

Improving a PenmanMonteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas

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International Journal of Digital Earth 2013年第S1期6卷 134-156页

作者： liang Sun shunlin liang Wenping Yuan Zhongxin Chen State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing ApplicationsChinese Academy of ScienceBeijingChina College of Global Change and Earth System Science Beijing Normal UniversityBeijingChina Department of Geography University of MarylandCollege ParkMDUSA Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijingChina

PenmanMonteith(PM)theory has been successfully applied to calculate land surface evapotranspiration(ET)for regional and global scales.However,soil surface resistance,related to soil moisture,is always difficult to determine over a large region,especially in arid or semiarid areas.In this study,we developed an ET estimation algorithm by incorporating soil moisture control,a soil moisture index(SMI)derived from the surface temperature and vegetation index space.We denoted this ET algorithm as the PM-SMI.The PM-SMI algorithm was compared with several other algorithms that calculated soil evaporation using relative humidity,and validated with Bowen ratio measurements at seven sites in the Southern Great Plain(SGP)that were covered by grassland and cropland with low vegetation cover,as well as at three eddy covariance sites from AmeriFlux covered by forest with high vegetation cover.The results show that in comparison with the other methods examined,the PM-SMI algorithm significantly improved the daily ET estimates at SGP sites with a root mean square error(RMSE)of 0.91 mm/d,bias of 0.33 mm/d,and R^(2) of 0.77.For three forest sites,the PM-SMI ET estimates are closer to the ET measurements during the non-growing season when compared with the other three algorithms.At all the 10 validation sites,the PMSMI algorithm performed the best.PM-SMI 8-day ET estimates were also compared with MODIS 8-day ET products(MOD16A2),and the latter showed negligible bias at SGP sites.In contrast,most of the PM-SMI 8-day ET estimates are around the 1:1 line.

关键词： ET PenmanMonteith soil moisture MODIS

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

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International Journal of Digital Earth 2016年第11期9卷 1098-1116页

作者： Linpeng Shi shunlin liang Jie Cheng Quan Zhang State Key Laboratory of Remote Sensing Science School of GeographyBeijing Normal UniversityBeijingPeople’s Republic of China Department of Geographical Sciences University of MarylandCollege ParkMDUSA

In this study,the multi-resolution Kalman filter(MKF)algorithm,which can handle multi-resolution problems with high computational efficiency,was used to blend two emissivity products:the Global LAnd Surface Satellite(GLASS)(BBE)product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)narrowband emissivity(NBE)product.The ASTER NBE product was first converted into a BBE product.A new detrending method was used to transfer the BBEs into a process suitable for the MKF.The new detrending method was superior to the two existing methods.Finally,both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale.Field measurements collected in North America were used to validate the integrated BBEs.Visually,the fusion map showed good continuity,with the exception of the border areas,and the quality of the fusion map was better than that of the original maps.The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale.In addition,the MKF was capable of recovering missing pixels at a finer scale.

关键词： Earth observation broadband emissivity MKF optimal interpolation data fusion GLASS MODIS ASTER

Remote sensing of earth’s energy budget:synthesis and review

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International Journal of Digital Earth 2019年第7期12卷 737-780页

作者： shunlin liang Dongdong Wang Tao He Yunyue Yu Department of Geographical Sciences University of MarylandCollege ParkMDUSA School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China NOAA/NESDIS/Center for Satellite Applications and Research RiverdaleMDUSA

The Earth’s climate is largely determined by its energy budget.Since the 1960s,satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere(TOA)and the surface.Besides the broadband sensors that have been traditionally used for monitoring Earth’s Energy Budget(EEB),data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components.This paper provides a comprehensive review of the satellite missions,state-of-the art estimation algorithms and the satellite products,and also synthesizes current understanding of the EEB and spatiotemporal variations.The TOA components include total solar irradiance,reflected shortwave radiation/planetary albedo,outgoing longwave radiation,and energy imbalance.The surface components include incident solar radiation,shortwave albedo,shortwave net radiation,longwave downward and upwelling radiation,land and sea surface temperature,surface emissivity,all-wave net radiation,and sensible and latent heat fluxes.Some challenges,and outlook such as virtual constellation of different satellite sensors,temporal homogeneity tests of long time-series products,algorithms ensemble,and products intercomparison are also discussed.

关键词： Earth’s energy budget surface radiation budget satellite remote sensing climate change

A disaggregation approach for estimating high spatial resolution broadband emissivity for bare soils from Landsat surface reflectance

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International Journal of Digital Earth 2018年第7期11卷 691-702页

作者： Jie Cheng shunlin liang Hao Liu Aixiu Nie Qiang Liu State Key Laboratory of Remote Sensing Science Faculty of GeographyBeijing Normal UniversityBeijingPeople’s Republic of China Department of Geographical Sciences University of MarylandCollege ParkMDUSA State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingPeople’s Republic of China

High spatial resolution land surface broadband emissivity(BBE)is not only useful for surface energy balance studies at local scales,but also can bridge the gap between existing coarser resolution BBE products and point-based field measurements.This study proposes a disaggregation approach that utilizes the established BBE–reflectance relationship for estimating high spatial resolution BBE for bare soils from Landsat surface reflectance data.The disaggregated BBE is compared to the BBE calculated from spatial–temporal matched Advanced Spaceborne Thermal Emission and Reflectance Radiometer emissivity product.Comparison results show that better agreement is achieved over relative homogeneous areas,but deteriorated over heterogeneous and cloud-contaminated areas.In addition,field-measured emissivity data over large homogeneous desert were also used to validate the disaggregated BBE,and the bias is 0.005.The comparison and validation results indicated that the disaggregation approach can obtain high spatial resolution BBE with better accuracy for homogeneous area.

关键词： Broadband emissivity ASTER TM bare soil disaggregation