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A novel fine-resolution snow depth retrieval model to revealdetailed spatiotemporal patterns of snow cover in NortheastChina

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International Journal of Digital Earth 2023年第1期16卷 1164-1185页

作者： Yanlin Wei Xiaofeng Li Lingjia Gu Xingming Zheng Tao Jiang Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchunPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Changchun Jingyuetan Remote Sensing Experiment Site Chinese Academy of SciencesChangchunPeople’s Republic of China College of Electronic Science&Engineering Jilin UniversityChangchunPeople’s Republic of China

Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.

关键词： Passive microwave remote sensing snow depth inversion machine learning fine resolution Northeast China

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fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

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International Journal of Digital Earth 2017年第3期10卷 307-323页

作者： Yanjun Su Qin Ma Qinghua Guo Sierra Nevada Research Institute School of EngineeringUniversity of California at MercedMercedCAUSA State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijingPeople’s Republic of China

Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution.In this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(LiDAR)data.Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements,and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)footprints.Finally,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate data.The results show that our fine-resolution SN tree height product has a good correspondence with field measurements.The coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.

关键词： Tree height Sierra Nevada LiDAR integration fine resolution

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