Peer-Reviewed Publications at Dr. Kaiyu Guan's group (Google Scholar):

Area 1: Satellite technology innovation and applications                                                                                                                                   

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Featured Publications:

●    Zhou, J., Yang, Q., Liu, L., Kang, Y., Jia, X., Chen, M., Ghosh, R., Xu, S., Jiang, C., Guan, K. , Kumar, V., Jin, Z.* (2023) "A deep transfer learning framework for mapping high spatiotemporal resolution LAI". ISPRS Journal of Photogrammetry and Remote Sensing. 206, 30-48.

●    Chongya Jiang*, Guan, K.*, Yizhi Huang, Maxwell Jong (2023) "A vehicle imaging approach to acquire ground truth data for upscaling to satelite data: A case study for estimationg harvesting dates". Remote Sensing of Environment, 300, 113894.

●   Wang, S.*, Guan, K.*, Zhang, C., Zhou, Q., Wang, S., Wu, X., Jiang, C., Peng, B., Mei, W., Li, K., Li, Z., Yang, Y., Zhou, W. and Ma, Z. (2022) "Cross-scale sensing of field-level crop residue fraction and tillage intensity: integrating field photos, airborne hyperspectral imaging, and satellite data" Remote Sensing of Environment.

●   Zhou, Q., Guan, K.*, Wang, S.*, Jiang, C., Huang, Y., Peng, B., Chen, Z., Wang, S., Hipple, J., Schaefer, D., Qin, Z., Stroebel, S., Coppess, J., Khanna, M., and Cai, Y. (2022) "Recent rapid increase of cover crop adoption across the US Midwest detected by fusing multi‐source satellite data" Geophysical Research Letters, p.e2022GL100249.

●      Jong, M., Guan, K.*, Wang, S., Huang, Y., and Peng, B. (2022) "Improving field boundary delineation in ResUnets via adversarial deep learning" International Journal of Applied Earth Observation and Geoinformation, 112, 102877.

●      Cai,Y., Guan,K.*, Lobell,D.B., Potgieter,A. et al. (2019) "Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches,  Agricultural and Forest Meteorology, 274, 144-159.

●       Peng, B.*, Guan, K.*, Pan, M., and Li, Y. (2018) "Benefits of seasonal climate prediction and satellite data for forecasting US maize yield", Geophysical Research Letters, 45.

●       Peng, B.*, Guan, K.*, Zhou, W., Jiang, C., Frankenberg, C., Sun, Y., He, L., and Kohler, P. (2020) "Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction", International Journal of Applied Earth Observation and Geoinformation, 90, 102126.

●       Guan,K.*, Lobell,D.B., Berry,J.,  Joiner,J., Guanter,L., Zhang,Y., and Badgley,G. (2015) "Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence", Global Change Biology, 22(2).

●       Guan,K.*, Wu,J., Kimball,J., Anderson,M., Li,B., and Lobell,D.B. (2017) "The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields", Remote Sensing of Environment., 199, 333-349.

●       Luo,Y., Guan,K.*, and Peng,J.* (2018) "STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product", Remote Sensing of Environment, 214, 87-99. 

●       Jiang, C.*, Guan, K.*, Wu, G., Peng, B., and Wang, S. (2021) “A daily, 250 m, and real-time gross primary productivity product (2000–present) covering the Contiguous United States”, Earth System Science Data, 13, 281-298, 2021.

●       Kimm,H.*, Guan,K.*, Jiang,C., Peng,B., Gentry,L.F., Wilkin,S.C., Wang,S., Cai,Y., Bernacchi,C.J., Peng,J., and Luo,Y. (2020) "Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data", Remote Sensing of Environment, 239.

●       Jiang, C.*, Guan, K.*, Khanna, M.*, Chen, L. and Peng, J. (2021) “Assessing marginal land availability based on land use change information in the Contiguous United States”. Environmental Science & Technology.

●       Cai,Y., Guan,K.*, Peng,J.*, Wang,S., Seifert,C., Wardlow,B., and Li,Z. (2018) "A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach", Remote Sensing of Environment,210, 35-47.

Other Publications:

●   Cheng, T., Ma, W., Guan, K.*, Torralba, A., Wang, S. (2023) "Structure from Duplicates: Neural Inverse Graphics from a Single Image". NeurlPS 2023.

●      Dechant, B., Ryu, Y., Badgley, G., Köhler, P., Rascher, U., Migliavacca, M., Zhang, Y., Tagliabue, G., Guan, K., Rossini, M., Goulas, Y., Frankenberg, C., and Berry, J. A. (2021) "NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales". Remote Sensing of Environment.

●       Li, K., Guan, K.*, Jiang, C.*, Wang, S., Peng, B., and Cai, Y. (2021) "Evaluation of four new land surface temperature (LST) products in the U.S. Corn Belt: ECOSTRESS, GOES-R, Landsat, and Sentinel-3". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

●       Lin, C., Jin, Z., Mulla, D., Ghosh, R., Guan, K., Kumar, V., and Cai, Y., (2021) "Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco". Remote Sensing, 13(9), 1740.

●       Hao, D., Asrar, R. G., Zeng, Y., Yang, X., Li, X., Xiao, J., Guan, K., Wen, J., Xiao, Q., Berry A., J., and Chen, M., (2021) “Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis”, Global Change Biology.

●       Kong, J., Ryu, Y., Huang, Y., Dechant, B., Houborg, R., Guan, K., and Zhu, X. (2021). “Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape",Agricultural and Forest Meteorology, 297, 108255.

●       Luo, Y., Guan, K.*, Peng, J., Wang, S., and Huang, Y. (2020) “Stair 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10m, Daily, and Cloud-/Gap-Free Surface Reflectance Product”, Remote Sensing, 12(19), 3209.

●       Paul, R. F., Cai, Y., Peng, B., Yang, W. H., Guan, K., & DeLucia, E. H., (2020) "Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images", Remote Sensing, 12(12), 1942.

●       Franz,E.T., Pokal,S., Gibson,P.J., Zhou,Y., Gholizadeh,H., TenorioA.F., Rudnick,D.,Heeren,D., McCabe,M., Ziliani,M., Jin,Z., Guan,K., Pan,M, Gates,J., and Wardlow,B., (2020) "The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield", Field Crops Research, 252.

●       He,M., Kimball,J., Yi,Y., Running,S., Guan,K., Jencso,K., Maxwell,B., and Maneta,M. (2019), "Impacts of the 2017 flash drought in the US Northern Plains informed by satellite-based evapotranspiration and solar-induced fluorescence", Environmental Research Letters, 14.

●       He,M., Kimball,J.S., Yi,Y., Mu,Q., Running,S., Guan,K., Maneta,M., Moreno,A., and Wu,X. (2019) "Satellite data-driven modeling of field scale evapotranspiration in croplands using the MOD16 algorithm framework ", Remote Sensing of Environment, 230.

●       Xu,Z., Guan,K., Casler,N., Peng,B., and Wang,S. (2018) "A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery", ISPRS Journal of Photogrammetry and Remote Sensing, 114, 423-434.

●       Guan,K.*, Li,Z., Rao,N., Feng,G, etc (2018) "Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2/PALSAR-2", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 2238-2252.

●       Song,L., Guanter,L., Guan,K., et al. (2018) "Satellite chlorophyll fluorescence captures heat stress for the winter wheat in the Indo-Gangetic Plains, India", Global Change Biology, 24, 4023- 4037.

●       Urban,D.*, Guan,K.*, and Jain,M. (2018) "Estimating sowing dates from satellite data over the U.S. Midwest: a comparison of multiple sensors and metrics", Remote Sensing of Environment, 211, 400-412.

●       Zhang,Y., Guanter,L., Joiner,J., Lian,S., and Guan,K. (2018) "Spatially-explicit monitoring of crop photosynthetic capacity through space-based chlorophyll fluorescence data", Remote Sensing of Environment, 210, 362-374.

●       Madani, N., Kimball,J., Jones,L., Parazoo,N.C., and Guan,K. (2017) "Global analysis of bioclimatic controls on ecosystem productivity using satellite observations of solar-induced chlorophyll fluorescence", Remote Sensing, 9(6), 530.

●       He,M., Kimball,J.S., Running,S, Ballantyne,A., Guan,K., and Heummrich,F. (2016) "Satellite detection of soil moisture related impacts on ecosystem productivity using the MODIS-based Photochemical Reflectance Index", Remote Sensing of Environment, 186, 173-183.

●       Guan,K.*, Medvigy,D., Wood,E.F. , Caylor,K.K. ,Li,S. and Jeong,S.J. (2014) "Deriving vegetation phenological time and trajectory information over Africa using SEVIRI daily LAI", IEEE transactions on Geoscience and Remote Sensing, 53(2),1113-1130.

●       Gao,Y., Wang,S., Guan,K., Wolanin,A., You,L., Ju,W., and Zhang,Y. (2020) "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", Remote Sensing, 12(7), 1111.

●       He,L.,Magney,T.,Dutta,D.,Yin,Y.,Kohler,P.,Grossmann,K.,Stutz,J.,Dold,C.,Hatfield,J., Guan,K., Peng,B., and Frankenberg,C. (2020) "From the ground to space: Using solar-induced fluorescence (SIF) to estimate crop productivity", Geophysical Research Letters, 47(7), e2020GL087474.

●       Becker-Reshef, I. et al. (including Guan, K.) (2022). "The NASA Harvest Program on Agriculture and Food Security. In: Vadrevu, K.P., Le Toan, T., Ray, S.S., Justice, C. (eds) Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries". Springer, Cham. 

●       Wang,C.*, Guan,K.*, Peng,B.,Chen,M., Jiang,C.,Zeng,Y., Wu,G., Wang,S., Wu,J., Yang,X., Frankenberg,C., Kohler,P., Berry,J., Bernacchi,C., Zhu,K, Alden,C., and Miao,G. (2020) "Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest", Remote Sensing of Environment, 241.