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Selected Publications by Our Scientists

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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.

Zhou, W., Guan, K., Peng, B. 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.

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.

Zhang, J., Guan, K., Peng, B. et al. (2021). Assessing different plant‐centric water stress metrics for irrigation efficacy using soil‐plant‐atmosphere‐continuum simulation. Water Resources Research, p.e2021WR030211.

Zhou, W., Guan, K., Peng, B. 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.

Zhang, J., Guan, K., Peng, B. et al. (2021). Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand. Nature Communications.

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.

Zhang, J., Guan, K., Peng, B. et al. (2021). Challenges and opportunities in precision irrigation decision-support systems for center pivots. Environmental Research Letters.

Jiang, C., Guan, K., Wu, G., Peng, B., and Wang, S. (2020). A daily, 250 m, and real-time gross primary productivity product (2000–present) covering the Contiguous United States. Earth System Science Data, 1-28.

Peng, B., Guan, K. et al. (2020). Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 6(4), 338-348.

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.

Jiang, C., Guan, K., Pan, M., Ryu, Y., Peng, B., and Wang, S. (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.

Kimm, H., Guan, K., Gentine, P., Wu,J., Lin, C., Bernacchi, C.J., and Sulman, B.N. (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.

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.

Wu,G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang,X., Wang,S., Suyker, A.E.,Bernacchi, C. and Moore, C.E. (2019). Radiance-based NIRv as a proxy for GPP of corn and soybean. Environmental Research Letters, 15.

Li,Y., Guan, K., Schnitkey, G., DeLucia, E.H., & Peng, B. (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.

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.

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.

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.

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.

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.

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).

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