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金秀良，担任中国农业科学院作物表型组学研究创新团队首席，主要从事作物表型组学技术及生物育种应用研究，主持十四五重点研发计划青年科学家项目、国家自然科学基金青年/面上项目等22项，担任中国遗传学会作物表型组学分会等委员，NewPhytologist、Precision Agriculture、TheCrop Journal等杂志副主编/编委/咨询编委，瑞士和奥地利科学基金评审专家。获河南省科学科技进步一等奖（3/15）、中国测绘学会测绘科学科技二等奖（4/10）和神农中华农业科技二等奖（11/15）等，以第一或通讯作者发表SCI论文56篇，合作发表SCI论文60余篇，GoogleScholar总被引用5587次，H指数43。
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支持扩展名：.rar .zip .doc .docx .pdf .jpg .png .jpeg1. 主持人：半干旱区玉米估产的AquaCrop模型与遥感数据双变量同化方法研究 （国家自然科学基金-青年科学基金项目，41601369，19.00万）2016.01-2019.12.
2. 主持人：联合植株密度和整齐度的玉米苗情无人机影像评估方法研究（国家自然科学基金-面上项目，42071426，55.00万）2021.01-2024.12.
3. 主持人： 玉米表型与基因型鉴定新技术开发及耐旱全基因组选择应用（国家重点研发计划青年科学家项目任务书，2023YFD1202500， 200.00 万） ， 2023.12-2027.11.
1. Jin X.L.*, Zarco-Tejada, P.J., Schmidhaltere, U., Reynolds, M.P., Hawkesford,M.J., Varshney, R.K., Yang, T., Nie, C.W., Li, Z.H., Ming, B., Xiao, Y.G., Xie,Y.D., Li, SK.* High-throughput estimation of crop traits: A review of groundand aerial phenotyping platforms, IEEE Geoscience and Remote SensingMagazine, 2021, 9(1): 200-231.
2. Cheng, M.,Jiao, X.*, Li, B., Yu, X., Shao, M. and Jin, X.L.* Long time series ofdaily evapotranspiration in China based on the SEBAL model and multisourceimages and validation. Earth System Science Data, 2021b, 13(8):3995-4017.
3. Li, D.L.,Bai, D., Tian, Y., Li, Y.H. *, Zhao, C.S., Wang, Q., Guo, S.Y., Gu, Y.Z., Luan,X.Y., Wang, R.Z., Yang, J.L., Hawkesford, M.J., Schnable, J.C. *, Jin, X.L.*,Qiu, L.J.* Time series canopy phenotyping enables the identification of geneticvariants controlling dynamic phenotypes in soybean, Journalof Integrative Plant Biology,2023, 65: 117-132.
4. Shao, M.C.,Nie, C.W., Zhang, A.J., Shi, L.S., Zha, Y.Y., Xu, H.G., Yang, H.Y., Yu, X.,Bai, Y., Liu, S.B., Cheng, M.H., Lin, T., Cui, N.B., Wu, W.B., Jin X.L.*.Quantifying effect of maize tassels on LAI estimation based on multispectralimagery and machine learning methods, Computers and Electronics inAgriculture, 211: 108029.
5. Yu, X.#,Yin, D.#, Nie, C., Ming, B., Xu, H., Liu, Y., Bai, Y., Shao, M., Cheng, M.,Liu, Y. and Liu, S., Wang, Z., Wang, S., Shi, L.*, Jin, X.* Maize tasselarea dynamic monitoring based on near-ground and UAV RGB images by U-Net model.Computers and Electronics in Agriculture, 2022, 203: 107477.
6. Liu, S., Jin,X.L.*, Nie, C., Wang, S., Yu, X., Cheng, M., Shao, M., Wang, Z., Tuohuti,N., Bai, Y. Estimating leaf area index using unmanned aerial vehicle data:Shallow vs. deep machine learning algorithms. Plant Physiology, 2021, 187(3):1551-1576.
7. Jin, X.L.*, Liu, S.Y., Baret, F., Hemerlé, M., Comar, A.Estimates of plant density of wheat crops at emergence from very low altitudeUAV imagery. Remote Sensing ofEnvironment, 2017, 198:105-114.
8. Jin, X.L.*, Li, Z.H., Yang, G.J.,Yang, H., Feng, H.K., Xu, X.G.,Wang, J.H. Winter wheat yield estimation based on multi-source high-resolutionoptical and radar imagery and AquaCrop model using particle swarm optimizationalgorithm. ISPRSJournal of Photogrammetry and Remote Sensing, 2017, 126:24-37.
9. Jin, X.L., Song, K.S.*, Du, J., Liu, H.J., Wen, Z.D. Comparison of different satellite bands andvegetation indices for estimation of soil organic matter based on simulatedspectral configuration. Agricultural and Forest Meteorology, 2017, 244-245:57-71.
10. Jin, X.L., Du, J., Liu, H.J., Wang, Z.M., Song, K.S.* Remoteestimation of soil organic matter content in the Sanjiang Plain, NorthestChina: the optimal band algorithm versus the GRA-ANN model. Agricultural and ForestMeteorology, 2016, 218-219: 250-260.
11. Liu, T., Li, R., Zhong, X.C.*, Jiang, M., Jin, X.L.*, Zhou, P., Liu, S.P., Sun, C.M.*, Guo, W.S*. Estimates of rice lodging using indicesderived from UAV visible and thermal infrared images. Agricultural and Forest Meteorology, 2018, 252:144-154.
12. Cheng M.H., Penuelas, J., McCabe, F.M., Atzberger, C., Jiao, X.Y. *,Wu, W.B. *, Jin, X.L.* Combining multi-indicators with machine-learningalgorithms for maize yield early prediction at the county-level in China. Agriculturaland Forest Meteorology, 2022, 323: 109057.
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