贾立，中国科学院空天信息创新研究院，二级研究员，中国科学院特聘核心骨干研究员。研究方向为水循环、水资源遥感及全球变化，重点开展水循环遥感、 水资源遥感、生态水文遥感、遥感植被和干旱监测、冰冻圈遥感及全球变化等方面研究。先后主持承担科技部国家重点基础研发计划项目课题、国家自然科学基金委员会重大项目课题、面上项目和国际(地区)合作与交流项目(重点)、欧盟第七框架及地平线2020项目、部委和地方科技项目40余项，在国内外学术期刊发表学术论文90余篇，参与出版专著10余部。

教育背景:

2000.09-2004.09, 荷兰瓦赫宁根大学                                                    理学博士

1994.09-1997.06, 中国科学院兰州高原大气物理所(现为中国科学院西北生态环境资源研究院)  理学硕士

1984.09-1988.07, 北京气象学院

工作经历：

2023.04-至今     中国科学院空天信息创新研究院    二级研究员，中国科学院特聘核心骨干

2022.05-2023.03  中国科学院空天信息创新研究院    二级研究员，中国科学院特聘核心骨干，水循环遥感研究室主任

2020.04-2023.03  中国科学院空天信息创新研究院    二级研究员，水循环遥感研究室主任

2009.08-2020.03  中国科学院遥感与数字地球研究所  研究员，水循环遥感研究室主任

2004.12-2009.12  荷兰瓦赫宁根大学与研究中心      研究员

2002.07-2004.11  荷兰瓦赫宁根大学与研究中心      博士后

1999.06-2000.08  荷兰SC-DLO研究中心              访问学者

1988.08-1999.05  中国科学院兰州高原大气物理研究所(现为中国科学院西北生态环境资源研究院)  研究实习员、助理研究员及副研究员

水循环、水资源遥感及全球变化

（1）北非地区水资源及农业用水监测与评估，“一带一路”国际科学组织联盟项目，负责人，2022-2025。

（2）全球陆地生态系统耗水及水分利用效率估算方法及产品，可持续发展大数据国际研究中心卓越科学家项目，负责人，2022-2024。

（3）陆地水循环关键参量时空多尺度智慧化遥感，国家自然基金委重大基金项目课题，负责人，2021-2025.

（4）西风-季风作用区非均匀下垫面地气相互作用机载通量观测试验研究，科技部“第二次青藏高原综合科学考察研究”专项子专题，负责人，2019-2024。

（5）一带一路水循环要素监测，中国科学院战略性先导专项(A类)“地球大数据科学工程”项目子课题，负责人，2018-2022。

（6）OPERANDUM: OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks, 欧盟地平线2020计划项目，Co-PI, 2018-2022.

（7）萨赫勒地区土地覆盖与土地利用变化驱动机制及其影响， 国家自然科学基金国际(地区)合作与交流项目，负责人，2017-2021。

（8）Satellite Observations For Improving Irrigation Water Management (Sat4IrriWater), 中欧龙计划合作第五期项目，负责人，2020-2024。

（9）冰冻圈与陆表水循环遥感，科技部-国家外国专家局项目，负责人，2016-2019。

（10）高亚洲典型环境要素数据集产品生成研究，中国科学院《泛第三极环境与“一带一路”协同发展》项目专题，负责人，2016-2020。

（11）高分辨率陆表能量水分交换过程的机理与尺度转换研究，国家重点基础研究发展计划项目课题, 负责人，2015-2019。

（12）地球系统过程的空间信息模拟，中国科学院“一三五”规划重点培育方向项目，负责人，2013-2015。

（13）黑河流域水-生态-经济系统的集成模拟与预测，国家自然科学基金重大研究计划《黑河流域生态-水文过程集成研究》项目，参加，2015-2018。

（14）基于遥感和数据同化的黑河中-下游植被与陆表水循环的相互作用研究, 国家自然科学基金面上项目，负责人, 2011-2013。

（15）亚欧合作长期观测系统－通过地面/卫星观测和数值模拟研究青藏高原水文气象过程和亚洲季风的关系 (CEOP-AEGIS)，欧盟第七框架项目，负责人，2008-2013。

（1）学术论文

[1]Ren S.T., Jia L.*, Menenti M., Zhang J., 2023, Spatiotemporal variability of glacier surface albedo and driving factors in the Western Nyainqentanglha Mountains in 2001–2020, Journal of Hydrology, 603, Part D, 127145. https://doi.org/10.1016/j.jhydrol.2021.127145.

[2]Zeng Y.L., Jia L.*, Menenti M., Jiang M., Asenso Barnieh B., Bennour A., Lv Y.Z., 2023, Changes in vegetation greenness related to climatic and non-climatic factors in the Sudano-Sahelian region, Regional Environmental Change, 23, 92(2023); https://doi.org/10.1007/s10113-023-02084-5.

[3]Asenso Barnieh B.; Jia L.*; Menenti M.; Yu L.; Nyantakyi E.K.; Kabo-Bah A.T.; Jiang, M.; Zhou J.; Lv, Y.; Zeng Y.; et al., 2023, Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine-Resolution Data in West Africa. Land, 2023, 12, 1032. https://doi.org/10.3390/land12051032.

[4]Mi P.; Zheng C.*; Jia L.; Bai Y., 2023, Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method. Remote Sens. 2023, 15, 2116. https://doi.org/10.3390/rs15082116.

[5]Bai Y., Jia L., Zhao T.J., 2023, A Soil Moisture Retrieval Method for Weakening Topographic Effect: A Case Study on the Qinghai-Tibetan Plateau with SMOS data, IEEE JSTARS, VOL. 16, 4276-4286; Digital Object Identifier 10.1109/JSTARS.2023.3264572.

[6]Chen Q.T., Jia L.*, Menenti M., Hu G.C., Wang K., Yi Z., Zhou J., Peng F., Ma S.X., You Q., Chen X., Xue X., 2023, A data-driven high spatial resolution model of biomass accumulation and crop yield: application to a fragmented desert-oasis agroecosystem, Ecological Modelling, 2023, 475, 110182; https://doi.org/10.1016/j.ecolmodel.2022.110182.

[7]Bennour A., Jia L.*, Menenti M., Zheng C., et al., 2023, Assessing impacts of climate variability and land use/land cover change on the water balance components in the Sahel using Earth observations and hydrological modelling, Journal of Hydrology: Regional Studies, 47, 101370.

[8]Zheng C., Jia L., Zhao T.J., 2023, A 21-year dataset (2000-2020) of gap-free global daily surface soil moisture at 1- km resolution, Scientific Data, 10:139; https://doi.org/10.1038/s41597-023-01991-w.

[9]Jiang M., Jia L.*, Menenti M., Zeng Y., 2022, Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region, Big Earth Data, 1-20; https://doi.org/10.1080/20964471.2022.2146632.

[10]Yi Z., Jia L., Chen Q.*, Jiang M., Zhou D., Zeng Y., 2022, Early-season crop identification in the Shiyang River Basin using a deep learning algorithm and time-series Sentinel-2 data, Remote Sensing, 14(21), 5625; https://doi.org/10.3390/rs14215625.

[11]Khuong H Tran, Massimo Menenti, Li Jia, 2022, Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold, Remote Sensing, 14(22), 5721; DOI: 10.3390/rs14225721.

[12]Xie Q.X., Jia L.*, Menenti M., Hu G.C., 2022, Global Soil Moisture Data Fusion by Triple Collocation Analysis from 2011 to 2018, Scientific Data, 2022, 9:687; https://doi.org/10.1038/s41597-022-01772-x.

[13]Zheng C.L.*, Jia L.*, Hu G.C., 2022, Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations, Journal of Hydrology, 2022, 128444; https://doi.org/10.1016/j.jhydrol.2022.128444.

[14]Bai Y., Zhao T.J.*, Jia L.*, Cosh M. H, Shi J.C., Zhiqing Peng, Xiaojun Li, Jean-Pierre Wignerone, 2022, A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data, 2022, 280, 113190; https://doi.org/10.1016/j.rse.2022.113190.

[15]Lu J., L. Jia, J. Zhou, M. Jiang, Y. Zhong, M. Menenti, 2022, Quantification and Assessment of Global Terrestrial Water Storage Deficit Caused by Drought Using GRACE Satellite Data, IEEE JSTARS, 15, 5001-5012; doi: 10.1109/JSTARS.2022.3180509.

[16]Du, D.; Zheng, C.; Jia, L.*; Chen, Q.; Jiang, M.; Hu, G.; Lu, J. Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model. Remote Sens. 2022, 14, 1722. https://doi.org/10.3390/rs14071722.

[17]Shen C., Jia L.*, Ren S.T., 2022, Inter and Intra Annual Glacier Elevation Change Over High Mountain Asia Based on ICESat-1/2 Data by Elevation-Aspect Bin Analysis Method, Remote Sensing, 2022, 14(7): 1630; DOI: 10.3390/rs14071630.

[18]Bennour A.; Jia L.*; Menenti M.; Zheng,C.; Zeng Y.; Asenso Barnieh B.; Jiang M., 2022, Calibration and validation of SWAT model by using hydrological remote sensing observables in the Lake Chad Basin, Remote Sensing, 2022, 14(6), 1511; https://doi.org/10.3390/rs14061511. Published: 21 March 2022.

[19]Yuan X.T., Jia L.*, Menenti M., Jiang M., 2022, Consistent nighttime light time series in 1992-2020 in Northern Africa by combining DMSP-OLS and NPP-VIIRS data, Big Earth Data, DOI: 10.1080/20964471.2022.2031542.

[20]Barnieh A.B., L. Jia*, M. Menenti, M. Jiang, J. Zhou, Y.Z. Lv, Y.L. Zeng, and A. Bennour, 2022, Quantifying spatial reallocation of land use/land cover categories in West Africa, Ecological Indicators, 135, 108556.

[21]Menenti M., Li X., Jia L., et al., 2021, Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security, Remote Sensing, 2021, 13, 5122. https://doi.org/10.3390/rs13245122.

[22]Zhang J., L. Jia*, M. Menenti, Zhou J, and S.T. Ren, 2021, Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine, Remote Sensing, 2021, 13, 5117. https://doi.org/10.3390/rs13245117.

[23]Cui Y., Jia L.*, 2021, Estimation of evapotranspiration of “soil-vegetation” system with a scheme combining a dual-source model and satellite data assimilation, Journal of Hydrology, December 2021, 603, Part D, 127145; https://doi.org/10.1016/j.jhydrol.2021.127145.

[24]Zhou J.*, Li Jia, Massimo Menenti, Xuan Liu, 2021, Optimal estimate of global biome – specific parameter settings to reconstruct NDVI time series with the Harmonic ANalysis of Time Series (HANTS) method, Remote Sensing, 2021, 13, 4251; https://doi.org/10.3390/rs13214251.

[25]Cui Y.K., Jia L., Fan W.J., 2021, Estimation of Actual Evapotranspiration and Its Components in an Irrigated Area by Integrating the Shuttleworth-Wallace and Surface Temperature-Vegetation Index Schemes using the Particle Swarm Optimization Algorithm, Agricultural and Forest Meteorology, May 24, 2021, 307, 108488; https://doi.org/10.1016/j.agrformet.2021.108488.

[26]Shaoting Ren, Evan S. Miles, Li Jia*, Massimo Menenti, Marin Kneib, Pascal Buri, Michael J. McCarthy, Thomas E. Shaw, Wei Yang, Francesca Pellicciotti, 2021, Anisotropy parameterization development and evaluation for glacier surface albedo retrieval from satellite observations, Remote Sensing, 2021, 13, 1714; https://doi.org/10.3390/rs13091714.

[27]Barnieh A.B., L. Jia*, M. Menenti, M. Jiang, J. Zhou, Y.L. Zeng, and A. Bennour, 2021, Modelling the Underlying Drivers of Natural Vegetation’s Occurrence in West Africa with Binary Logistic Regression, Sustainability 2021, 13(9), 4673; https://doi.org/10.3390/su13094673; 22 Apr 2021.

[28]Zhang J., L. Jia*, M. Menenti and S.T. Ren, 2021, Interannual and Seasonal Variability of Glacier Surface Velocity in the Parlung Zangbo Basin, Tibetan Plateau. Remote Sensing, 2021, 13, 80; https://doi.org/10.3390/rs13010080.

[29]Yi ZW; Jia L; Chen QT, 2020, Crop classification using multi-temporal Sentinel-2 data in Shiyang River Basin of China, Remote Sensing, 12, 4052; https://doi.org/10.3390/rs12244052.

[30]Barnieh A.B., L. Jia*, M. Menenti, J. Zhou, Y.L. Zeng, 2020, Mapping Land Use Land Cover Transitions at Different Spatiotemporal Scales in West Africa, Sustainability, 2020, 12, 8565; doi:10.3390/su12208565.

[31]Zhou J., L. Jia*, M. Menenti, M. van Hoek, J. Lu, C.L. Zheng, X.T. Yuan, Characterizing vegetation response to rainfall at multiple temporal scales in the Sahel-Sudano-Guinean region using transfer function analysis, Remote Sensing of Environment, 2021, 252, 112108, https://doi.org/10.1016/j.rse.2020.112108.

[32]Lu J., L. Jia, C. L. Zheng, R. L. Tang, Y. Z. Jiang, A Scheme to Estimate Diurnal Cycle of Evapotranspiration from Geostationary Meteorological Satellite Observations, Water, 2020, 12(9), 2369.

[33]Ren S.T., M. Menenti, L. Jia*, J. Zhang, J. Zhang, X. Li, 2020, Glacier mass balance in the Nyainqentanglha Mountains between 2000 and 2017 retrieved from ZiYuan-3 stereo images and the SRTM DEM, Remote Sensing, 2020, 12, 864; doi:10.3390/rs12050864.

[34]Zheng C.L.*, L. Jia*, 2020, Global canopy rainfall interception loss derived from satellite earth observations, Ecohydrology, https://doi.org/10.1002/eco.2186.

[35]Xie Q., M. Menenti, L. Jia*, 2019, Improving the AMSR-E/NASA soil moisture data product using in-situ measurements in the Tibetan Plateau, Remote Sensing, 2019, 11, 2748; doi:10.3390/rs11232748.

[36]Yuan X.T., L. Jia*, M.Menenti, J. Zhou, X. Yuan, 2019,  Filtering the NPP-VIIRS Nighttime Light Data for improved detection of settlements in developing Africa, Remote Sensing, 2019, 11, 3002; doi:10.3390/rs11243002.

[37]Chen Q., L. Jia*, M. Menenti, R. Hutjes, G. Hu, C. Zheng, K. Wang, 2019, A Numerical Analysis of Aggregation Error in Evapotranspiration Estimates Due to Heterogeneity of Soil Moisture and Leaf Area Index, Agricultural and Forest Meteorology, Volumes 269–270, 15 May 2019, Pages 335-350. https://doi.org/10.1016/j.agrformet.2019.02.017.

[38]van Hoek M, J. Zhou, L. Jia*, J. Lu, C. Zheng, G. Hu and M. Menenti, 2019, A prototype web-based analysis platform for drought monitoring and early warning, International Journal of Digital Earth, DOI: 10.1080/17538947.2019.1585978.

[39]Zhang J., L. Jia*, M. Menenti, and G. Hu, 2019, Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study, Remote Sensing, 2019, 11, 452; doi:10.3390/rs11040452. 

[40]Sun Y., L. Jia*, Q. Chen, and C. Zheng, Optimizing Window Length for Turbulent Heat Flux Calculations from Airborne Eddy Covariance Measurements under Near Neutral  to Unstable Atmospheric Stability Conditions, Remote Sensing, 2018, 10, 670; doi:10.3390/rs10050670.

[41]Lu J., L. Jia*, M. Menenti, Y. Yan, C. Zheng, and J. Zhou, 2018, Performance of the Standardized Precipitation Index Based on the TMPA and CMORPH Precipitation Products for Drought Monitoring in China, IEEE  Journal of Selected Topics In Applied Earth Observations And Remote Sensing (IEEE JSTARS), 11(5), 1387-1396; DOI: 10.1109/JSTARS.2018.2810163。

[42]Wang N., L. Jia*, C. Zheng, M. Menenti, 2017, Estimation of subpixel snow sublimation from multispectral satellite observations, Journal of Applied Remote Sensing, 11(4), 046017 (2017), doi: 10.1117/1.JRS.11.046017.

[43]Huang T., L. Jia*, M. Menenti, J. Lu, J. Zhou and G. Hu, 2017, A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat-GLAS Footprints, Sensors, 2017, 17, 1803; doi:10.3390/s17081803.

[44]Lu S., L. Jia, L. Zhang, Y. Wei, M. H. A. Baig, Z. Zhai, J. Meng, X. Li, G. Zhang, 2017, Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product, Remote Sensing Letters, 8(3), 224-233, 2017. http://dx.doi.org/10.1080/2150704X.2016.1260178,

[45]Li N.N., L. Jia*, J. Lu, M. Menenti, J. Zhou, 2017, Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semi-arid basin, Journal of Applied Remote Sensing, 11(1), 016028 (Feb 17, 2017). doi: 10.1117/1.JRS.11.016028.

[46]Gao B., H. Gong, T. Wang, and L. Jia, Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition, Remote Sensing, 2016, 8(9), 727; doi:10.3390/rs8090727.

[47]Shang H.L., L. Jia*, M. Menenti*, 2016, Modeling and Reconstruction of Time Series of Passive Microwave Data by Discrete Fourier Transform Guided Filtering and Harmonic Analysis, Remote Sensing, 2016, 8, 970; doi: 10.3390/rs8110970.

[48]Zhou J., Jia L.*, Menenti M., Gorte B., 2016, On the performance of remote sensing time series reconstruction methods – a spatial comparison, Remote Sensing of Environment, 187, 367–384.

[49]Song C., Jia L.*, 2016, A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia, Remote Sensing, 8, 703; doi:10.3390/rs8090703.

[50]Roupioz L., L. Jia, F. Nerry, M. Menenti, 2016, Estimation of Daily Solar Radiation Budget at Kilometer Resolution over the Tibetan Plateau by Integrating MODIS Data Products and a DEM, Remote Sensing, 2016, 8(6), 504; doi:10.3390/rs8060504.

[51]van Hoek M, L. Jia*, J. Zhou, C. Zheng, M., Menenti, 2016, Early Drought Detection by Spectral Analysis of Satellite Time Series of Precipitation and Normalized Difference Vegetation Index (NDVI). Remote Sensing, 8(422): 1-17. doi:10.3390/ rs8050422.

[52]Roupioz L., J. Colin, L. Jia, F. Nerry, and M. Menenti, 2015, Quantify the impact of cloud cover on radiation fluxes ground measurements from hemispherical images, International Journal of Remote Sensing, Vol. 36, Nos. 19–20, 5087–5104, http://dx.doi.org/10.1080/01431161.2015.1084440.

[53]Zhou J., L. Jia*, and M. Menenti, 2015, Reconstruction of Global MODIS Vegetation Index Time Series: Performance of Harmonic ANalysis of Time Series (HANTS), Remote Sensing of Environment, 163, 217-228; 10.1016/j.rse.2015.03.018.

[54]Shang H.L., L. Jia*, M. Menenti, 2015, Analyzing the Inundation Pattern of the Poyang Lake Floodplain by Passive Microwave Data. Journal of Hydrometeorology, 16(2), 652–667; doi: http://dx.doi.org/10.1175/JHM-D-14-0022.1.

[55]Li N.N., L. Jia*, J. Lu, 2015, An improved algorithm to estimate the surface soil heat flux over a heterogeneous surface: a case study in the Heihe River Basin, Science in China, 58(7), 1169-1181; doi: 10.1007/s11430-014-5041-y.

[56]Chen Q.T., L. Jia*, R. Hutjes, M. Menenti, 2015, Estimation of Aerodynamic Roughness Length over Oasis in the Heihe River Basin by Utilizing Remote Sensing and Ground Data, Remote Sensing, 2015, 7(4), 3690-3709; doi:10.3390/rs70403690.

[57]Hu G.C. and L. Jia*, 2015, Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations, Remote Sensing, 7(3), 3056-3087; doi:10.3390/rs70303056.

[58]Li Z.S., L. Jia*, G. C. Hu, J. Lu, Q.T. Chen, J.X. Zhang and K. Wang, 2015, Estimation of Growing Season Daily ET in the Middle Stream and Downstream Areas of the Heihe River Basin Using HJ-1 Data, IEEE Geoscience and Remote Sensing Letters, 12(5), 948 – 952, DOI:  10.1109/LGRS.2014.2368694.

[59]Hu G.C., L. Jia*, Menenti M., 2015, Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011. Remote Sensing of Environment, 156, 510–526, doi:10.1016/j.rse.2014.10.017.

[60]Li Z.S., L. Jia*, J. Lu, 2015, On Uncertainties of the Priestley-Taylor/LST-Fc Feature Space Method to Estimate Evapotranspiration: Case Study in An Arid/Semiarid Region in Northwest China, Remote Sensing, 7(1), 447-466, doi:10.3390/rs70100447.

[61]Cui Y.K., L. Jia*, G.C. Hu, and J. Zhou, 2015, Mapping of Interception Loss of Vegetation in the Heihe River Basin of China Using Remote Sensing Observations, IEEE Geoscience and Remote Sensing Letters, 12(1), 23 – 27; doi:10.1109/LGRS.2014.2324635.

[62]Roupioz L, F. Nerry, L. Jia, and M. Menenti, 2014, Improved Surface Reflectance from Remote Sensing Data with Sub-Pixel Topographic Information, Remote Sensing, 2014, 6(11), 10356-10374; doi: 10.3390/rs61110356.

[63]Cui Y.K., L. Jia*, 2014, A Modified Gash Model for Estimating Rainfall Interception Loss of Forest Using Remote Sensing Observations at Regional Scale, Water, 2014, 6(4), 993-1012; doi:10.3390/w6040993.

[64]Song CY, L. Jia*, M. Menenti, 2014, Retrieving High-Resolution Surface Soil Moisture by Downscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3), 935 – 942; doi: 10.1109/JSTARS.2013.2272053.

[65]Gao, B., L. Jia*, M. Menenti, 2014, An improved method of retrieving land surface albedo over rugged terrain, IEEE Geoscience and Remote Sensing Letters, 11(2): 554 - 558, doi: 10.1109/LGRS.2013.2275072.

[66]Jia L., H. Shang, G. Hu, and M. Menenti, 2011, Phenological response of vegetation to upstream river flow in the Heihe River basin by time series analysis of MODIS data, Hydrology and Earth System Sciences, 15, 1047–1064.

[67]Jia L., G. Xi, S. Liu, C.  Huang, Y. Yan, G. Liu, 2009, Regional estimation of Daily to Annual Regional Evapotranspiration with MODIS data in the Yellow River Delta wetland, Hydrology and Earth System Sciences, 13, 1775-1787.

[68]Verhoef W., L. Jia, Q. Xiao, Z. Su, 2007. Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, no.6, 1808-1822.

[69]Jia L., Z.-L. Li, M. Menenti, Z. Su, W. Verhoef and Z. Wan, 2003, A practical algorithm to infer soil and foliage component temperatures from bi-angular ATSR-2 data, International Journal of Remote Sensing, International Journal of Remote Sensing, 24 (23), 4739–4760.

[70]Jia L., Su Z., van den Hurk B. J.J.M., Menenti M., et al, 2003, Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements, Journal of Physics and Chemistry of the Earth, 28, 75-88.

[71]郑超磊, 贾立, 胡光成, 高分一号卫星遥感数据驱动ETMonitor模型估算16 m分辨率蒸散发及验证．遥感学报，2023, 27(3): 758-768. DOI： 10.11834/jrs.20232477.

[72]谢秋霞, 贾立*, 陈琪婷, 尹燕旻, M. Menenti, 2021，闪电河流域农牧交错带微波遥感土壤水分产品评价．遥感学报，25(4): 974-989.

[73]卢善龙, 贾立, 蒋云钟, 王宗明, 段洪涛, 沈明, 田雨, 卢静. 2021. 联合国可持续发展目标6（清洁饮水与卫生设施）监测评估：进展与展望．中国科学院院刊, 36(8): 904-913.

[74]郑超磊, 胡光成, 陈琪婷, 贾立, 2021.遥感土壤水分对蒸散发估算的影响．遥感学报，25(4)：990-999.

[75]卢静, 贾立, 郑超磊, 胡光成, 2019.遥感水分收支对区域水资源估算潜能．遥感技术与应用，2019，34(3): 630-638. doi:10.11873/j.isnn.1004-0323.2019.3.0630.

[76]黄田进, 梁丁丁, 贾立*, 张静潇, 卢静, 周杰，2017. 青藏高原地区湖泊面积插补迭代自动提取．遥感技术与应用，2017, 32 (2) : 289-298.

[77]王宁, 贾立*, 李占胜, 李娜娜, 胡光成, 2016. 非参数化蒸散发方法在黑河流域的适用性分析. 高原气象, 35(1): 118- 128。

[78]李娜娜, 贾立*, 卢静，2015. 复杂下垫面地表土壤热通量算法改进:以黑河流域为例. 中国科学: 地球科学, 2015, 45(4): 494-507。

[79]潘颖琪, 贾立*, 2015. 基于MTSAT-1R数据的地表温度反演．遥感技术与应用, 30(4): 626 - 637。

[80]杨锦鑫, 贾立*, 2014. 基于双角度AATSR遥感数据的组分温度反演．遥感技术与应用, 2014, 29 (2): 247-257。

[81]贾立, 王介民, M.Menenti, 1999. 卫星遥感结合地面资料对区域表面动量粗糙度的估算．大气科学，1999, 23(5): 632-640。

[82]贾立, 王介民, 1999. 绿洲和沙漠复合地表条件下的局地和有效粗糙度. 气象学报, 1999, 57(3): 346-357。

[83]贾立, 王介民, 胡泽勇，2000. 干旱区热力学粗糙度特征及对感热通量估算的影响. 高原气象, 2000, 19(4): 495-503。

[84]贾立, 王介民，1999. 黑河实验区地表植被指数的区域分布及季节变化.高原气象, 1999, 18(2): 245-249。

[85]贾立, 王介民, 刘巍，1994. 黑河试验区春小麦田间的环境因子对蒸腾和光合作用的影响. 高原气象, 1994, 13(3):359-368。

（2）专著（参与编写）

[1]《陆表能量与水分交换过程的遥感观测与模拟》，科学出版社，2023，ISBN 978-7-03-074897-3. （施建成，贾立，卢麾，蒋玲梅著）

[2]《地球大数据支撑可持续发展目标报告（2019）》，科学出版社，2020.

[3]《地球大数据支撑可持续发展目标报告（2020）：一带一路篇》，科学出版社，2021.

[4]《地球大数据支撑可持续发展目标报告（2021）：中国篇》，科学出版社，2022.

[5]《地球大数据支撑可持续发展目标报告（2021）：一带一路篇》，科学出版社，2022.

[6]遥感监测绿皮书《中国可持续发展遥感监测报告（2016）》,社会科学文献出版社,2017.

[7]遥感监测绿皮书《中国可持续发展遥感监测报告（2017）》,社会科学文献出版社,2018.

[8]遥感监测绿皮书《中国可持续发展遥感监测报告（2019）》,社会科学文献出版社,2020.

[9]遥感监测绿皮书《中国可持续发展遥感监测报告（2021）》,社会科学文献出版社,2022.(副主编)

[10]遥感监测绿皮书《中国可持续发展遥感监测报告（2022）》,社会科学文献出版社,2022.(副主编)

[11]Jia L., C. Zheng, G.C. Hu, and M. Menenti, 2017. Evapotranspiration, Chapter in book ‘Comprehensive Remote Sensing’, edited by S. Laing et al., Elsevier, ISBN-10: 0128032200, ISBN-13: 978-012803220; pp.3134.

[12] Jia, L., M. Menenti, 2010. Thermal infrared observations of heterogeneous soil-vegetation systems, Chapter in book ‘Remote Sensing Optical Observations of Vegetation Properties’, Editors: Maselli, F., Menenti, M., and Brivio, A., Research Signpost, Kerala, India, pp. 227-274, 2010.

[13] Jia, L., 2004. Modeling heat exchanges at the land-atmosphere interface using multi-angular thermal infrared measurements, Wageningen University, ISBN 90-8504-041-8, pp.199.

(1)测绘科学技术奖, 一等奖, 部委级, 2021

(2)非均匀下垫面地表蒸散发观测与遥感估算的理论与方法, 二等奖, 部委级, 2017