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Selected Publications by Our Scientists
The scientists who lead HabiTerre also lead the field. If you want to delve deeper into the technologies, take a look at some of the papers co-authored by HabiTerre scientists.
An overarching framework for HabiTerre’s system of systems solution for field-level GHG quantification:
Guan, K., Jin, Z., …, Peng, B., et al. (2023) “A framework for scalably quantifying field-level agricultural carbon outcomes” Earth-Science Reviews, 104462.
- Wu, G., Guan, K., et al. (2023) “Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean” Journal of Experimental Botany
- Wu, G., Guan, K., et al. (2023) “Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?“. Agricultural and Forest Meteorology, 338, 109532.
- Zhou, Q., Guan, K., et al. (2022) “Recent rapid increase of cover crop adoption across the US Midwest detected by fusing multi‐source satellite data” Geophysical Research Letters, p.e2022GL100249.
- Wang, S., Guan, K., et al. (2022) “Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning” Remote Sensing of Environment.
- Zhou, Q., …, and Guan, K. (2022) “Operational atmospheric correction of airborne hyperspectral imaging spectroscopy: algorithm evaluation, key parameter analysis, and machine learning emulators” ISPRS Journal of Photogrammetry and Remote Sensing.
- Wang, S., Guan, K., et al. (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.
- Wu, G., Guan, K., et al. (2022). “Attributing differences of solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationships between two C4 crops: corn and miscanthus” Agricultural and Forest Meteorology, 323, 109046.
- Wu, G., …, and Guan, K. (2022). “Difference in seasonal peak timing of soybean SIF and GPP explained by canopy structure and chlorophyll content” Remote Sensing of Environment, 279, 113104.
- Wang, S., Guan, K.,et al. (2022) “Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing“. Remote Sensing of Environment, 271, p.112914.
- Zhou, Q., Guan, K., et al. (2022) “Recent rapid increase of cover crop adoption across the US Midwest detected by fusing multi‐source satellite data” Geophysical Research Letters, p.e2022GL100249.
- Kimm, H., Guan, K., et al. (2021) “Quantifying high-temperature stress on soybean canopy photosynthesis: the unique role of sun-induced chlorophyll fluorescence”, Global Change Biology.
- Jong, M., Guan, K., et al. (2022) “Improving field boundary delineation in ResUnets via adversarial deep learning” International Journal of Applied Earth Observation and Geoinformation, 112, 102877.
- Jiang, C., Guan, K., et al. (2021) “Assessing marginal land availability based on land use change information in the Contiguous United States”. Environmental Science & Technology.
- Jiang, C., Guan, K., et al. (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.
- Wang, S., Guan, K., et al. (2021) “Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling“. International Journal of Applied Earth Observation and Geoinformation.
- Kimm, H., Guan, K., et al. (2021) “A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt“. Environmental Research Letters.
- Wang, S., Guan, K., et al. (2020) “Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy”, Journal of Experimental Botany.
- Kimm,H., Guan,K., et al. (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.
- Peng, B., Guan, K., et al. (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.
- Cai,Y., Guan,K., 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.
- Wu,G., Guan,K., et al. (2019) “Radiance-based NIRv as a proxy for GPP of corn and soybean“, Environmental Research Letters, 15.
- Miao,G., Guan,K., et al. (2018) “Sun-induced chlorophyll fluorescence, photosynthesis, and light use efficiency of a soybean field from seasonally continuous measurements“, Journal of Geophysical Research-Biogeosciences, 123, 610-623.
- Cai,Y., Guan,K., et al. (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.
- Peng, B., Guan, K., et al. (2018) “Benefits of seasonal climate prediction and satellite data for forecasting US maize yield“, Geophysical Research Letters, 45.
- 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.
- Guan,K., et al. (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.
- Guan,K., et al. (2015) “Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence“, Global Change Biology, 22(2).
- Potash, E., Guan, K., et al. (2023) “Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields“. Geoderma. 438, 116587.
- Ye, L., Guan, K., et al. (2023) “Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model-data fusion“. Environmental Research Letters.
- Guan, K., Jin, Z., Peng, B., et al. (2023) “A scalable framework for quantifying field-level agricultural carbon outcomes“. Earth-Science Reviews. 104462.
- Qin, Z., Guan, K., et al. (2023) “Assessing long-term impacts of cover crops on soil organic carbon in the central U.S. Midwestern agroecosystems” Global Change Biology
- Yang, Y., Liu, L., Zhou, W., Guan, K., et al. (2022) “Distinct driving mechanisms of non-growing season N2O emissions call for spatial-specific mitigation strategies in the US Midwest” Agricultural and Forest Meteorology, 324, p.109108.
- Zhou, W., Kaiyu, G., et al. (2022).”How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest?” Geoderma.
- Li, Z., Guan, K., et al. (2022). “Assessing the impacts of pre-growing-season weather conditions on soil nitrogen dynamics and corn productivity in the US Midwest” Field Crops Research, 284, 108563.
- Liu, L., Xu, S., Tang, J., Guan, K., et al. (2022) “KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments” Geoscientific Model Development 15, no. 7 (2022): 2839-2858.
- Potash, R., Guan, K., et al. (2022) “How to estimate soil organic carbon stocks of agricultural fields? Perspectives using ex ante evaluation“. Geodema, 411, 115693.
- Vardon, D. R., Sherbacow, B. J., Guan, K., et al. (2022) “Realizing “Net-Zero-Carbon” sustainable aviation fuel“. Joule.
- Zhou, W., Guan, K., et al. (2021) “Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems“. Agricultural and Forest Meteorology, 307, 108521.
- Qin, Z., Guan, K, et al. (2021) “Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems“. Field Crops Research.
- Zhang, J., Guan, K., et al. (2021) “Assessing different plant‐centric water stress metrics for irrigation efficacy using soil‐plant‐atmosphere‐continuum simulation“. Water Resources Research, p.e2021WR030211.
- Zhang, J., Guan, K., et al. (2021) “Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand”. Nature Communications.
- Zhang, J., Guan, K. et al. (2021) “Challenges and opportunities in precision irrigation decision-support systems for center pivots”, Environmental Research Letters.
- Kimm,H., Guan,K., et al. (2020) “Redefining droughts for the U.S. Corn Belt: The dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of Maize and Soybean“, Agricultural and Forest Meteorology, 287.
- Peng,B., Guan,K., et al. (2020) “Towards a multiscale crop modelling framework for climate change adaptation assessment“, Nature Plants, 6(4), 338-348.
- Zhou, W., Guan, K., et al. (2020) “Connections between the hydrological cycle and crop yield in the rainfed U.S. Corn Belt“, Journal of Hydrology, 125398.
- Jiang,C., Guan,K., et al. (2020) “BESS-STAIR: a framework to estimate daily, 30-meter, and allweather crop evapotranspiration using multi-source satellite data for the U.S. Corn Belt“, Hydrology and Earth System Science, 24, 1251-1273.
- Li,Y., Guan,K., et al. (2020)”Quantifying irrigation cooling benefits to maize yield in the Midwest US“, Global Change Biology, 00, 1-14.
- Li,Y., Guan,K. et al. (2019) “Towards building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S.“, Field Crop Research, 234, 55-66.
- Peng,B., Guan,K., et al. (2018) “Improving Maize Growth Processes in the Community Land Model: Implementation and Evaluation“, Agricultural and Forest Meteorology, 250-251, 64-89.
- Sultan,B., Guan,K., et al. (2014) “Robust features of future climate change impacts on sorghum yields in West Africa“, Environmental Research Letters, 9(10).
- Ma, Z., Guan, K., et al. (2023) “Agricultural nitrate export patterns shaped by crop rotation and tile drainage” Water Research 229, 119468.
- Deines, J. M., Guan, K., et al. (2022) “Recent cover crop adoption is associated with small maize and soybean yield losses in the United States” Global Change Biology.
- Zhou, W., Guan, K., et al. (2021) “A generic risk assessment framework to evaluate historical and future climate-induced risk for rainfed corn and soybean yield in the U.S. Midwest“. Weather and Climate Extremes, 100369.
- Li,Y., Guan,K., et al. (2019) “Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States“, Global Change Biology, 25, 2325- 2337.
- Guan,K., et al. (2017) “Assessing climate adaptation options and uncertainties for cereal systems in West Africa“, Agricultural and Forest Meteorology, 232, 291-305.
- Sheffield,J., Wood,E.F., Chaney,N., Guan,K., et al. (2014) “A drought monitoring and forecarsting system for Sub-Sahara African water resources and food security“, Bulletin of the American Meteorological Society, 95(6), 2014.
- Guan,K., et al. (2011) “Spatio-temporal scaling of hydrological and agro-chemical export dynamics in a tile-drained Midwestern watershed“, Water Resource Research, 47, W00J02.
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