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Improving potential of nitrogen linked gray water footprint in China's intensive cropping systems
Zhang, Y., Liu, X., You, L. & Zhang, F.
Journal of Cleaner Production, 2020, 122307.
DOI:10.1016/j.jclepro.2020.122307
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Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection
Zhang, J., Xie, T., Yang, C., Song, H., Jiang, Z., Zhou, G., Zhang, D., Feng, H. & Xie, J.
Remote Sensing, 2020, 12, 1403.
DOI:10.3390/rs12091403
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Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors
Zhang, J., Wang, C., Yang, C., Jiang, Z., Zhou, G., Wang, B., Shi, Y., Zhang, D., You, L. & Xie, J.
Precision Agric, 2020.
DOI:10.1007/s11119-020-09710-w
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Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring
Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G. & Xie, J.
Remote Sensing, 2020, 12, 1207.
DOI:10.3390/rs12081207
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Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory
Zhao, J., Li, J., Liu, Q., Xu, B., Yu, W., Lin, S. & Hu, Z.
International Journal of Applied Earth Observation and Geoinformation, 2020, 90, 102112.
DOI:10.1016/j.jag.2020.102112
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The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA
Gao, Y., Wang, S., Guan K., Wolanin, A., You, L., Ju, W. & Zhang, Y.
Remote Sensing, 2020, 12(7), 1111.
DOI:10.3390/rs12071111
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Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery
Hu, Q., Yang, J., Xu, B., Huang, J., Memon, S. M., Yin, G., Zeng, Y., Zhao, J. & Liu, K.
Remote Sensing, 2020, 12, 912-935.
DOI:10.3390/rs12060912
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A radiative transfer model for solar induced fluorescence using spectral invariants theory
Zeng, Y., Badgley, G., Chen, M., Li, J., Anderegg, L. D. L., Kornfeld, A., Liu, Q., Xu, B., Yang, B., Yan, K. & Berry, J. A.
Remote Sensing of Environment, 2020, 240, 111678.
DOI:10.1016/j.rse.2020.111678
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Blockchain Technology for Agriculture: Applications and Rationale
Xiong, H., Dalhaus, T., Wang, P., & Huang, J.
Frontiers in Blockchain, 2020, 3, 7.
DOI:10.3389/fbloc.2020.00007
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Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt
Wolanin, A., Mateo-García, G., Camps-Valls, G., Gómez-Chova, L., Meroni, M., Duveiller, G., You, L. & Guanter, L.
Environmental Research Letters, 2020, 15(2), 24019.
DOI:10.1088/1748-9326/ab68ac
语言：
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