About

Kaiguang Zhao is an Associate Professor of Environmental Modeling and Spatial Analysis at The Ohio State University. His research focuses on mapping, monitoring, modeling, and managing terrestrial environments across scales, especially in the context of global environmental changes. He employs a combined toolset including geotechnology, spatial analysis, machine learning, biophysical and climate modeling, ecological modeling, Bayesian statistics, and eddy-covariance to characterize the status and change in ecosystems. His work examines biophysical and ecological responses of terrestrial ecosystems to disturbances and climate change, addressing questions such as carbon storage in forests, ecosystem disturbances, vegetation mapping from air, long-term vegetation dynamics driven by climate, climate regulation services from land use, and crop productivity in warming or dry conditions. A particular emphasis of his research is developing geospatial applications using hyper spectral imaging, high-resolution imagery, remote sensing, and lidar to characterize ecosystem structure and functioning.

Selected publications

• Riverine phosphorus gain and loss across the conterminous United States

  2026-02-12 · 1 citations

  articleOpen access

  Abstract. Excess riverine phosphorus represents a preeminent catalyst for water quality degradation. Spatial mapping and characterization of the net gain and loss of riverine phosphorus help discern the critical source areas. Here, we developed a dataset encompassing phosphate (PO43-) and total phosphorus (TP) gain and loss across catchments in the conterminous United States (CONUS). We compiled 51,394 PO43- and 285,675 TP concentration data points and estimated PO43- and TP loads at 963 and 2,317 stations, respectively. Next, we leveraged the upstream-downstream topology information from the National Hydrography Dataset Plus (NHDPlus) catchment map at the Hydrologic Unit Catalogue-12 (HUC12) level to derive the net gain and loss of riverine phosphorus across catchments in the CONUS. Such maps can be used to estimate potential contributions of point and non-point sources to riverine phosphorus pollution at refined spatial scales, identify different major factors controlling local riverine P gain and loss compared to P loads, and evaluate watershed model’s fidelity for representing riverine P cycling. The resultant dataset is provided in Excel (.xlsx) format, accessible at Figshare (https://doi.org/10.6084/m9.figshare.28509317, Wang et al., 2025). Leveraging the HUC12 information for spatialization, the new datasets aim to address the existing gap in regional characterization of riverine phosphorus and support effective management practices across the CONUS.

  PublisherOA PDFDOI

• Supplementary material to "Riverine phosphorus gain and loss across the conterminous United States"

  2026-02-12

  articleOpen access

  Text S1. Estimation of riverine phosphorus loadThe LOADEST, a FORTRAN program designed for estimating constituent loads in streams and rivers through Adjusted Maximum Likelihood Estimation (AMLE), was used to calculate PO4 3-and TP loads at each hydrological station (Runkel et al., 2004).The Akaike Information Criteria (AIC), which evaluates both maximum likelihood and the number of independently adjusted parameters within each model (Akaike, 1974), was utilized for model selection among nine predefined regression models included in LOADEST (Table S1).A total of 935,069 PO4 3-and 1,220,744 TP observations were compiled as inputs for the LOADEST model.The time span and the number of P observations at each station are provided in the shared dataset.

  PublisherDOI

• Corrigendum to “Integrating proximal sensing data (soil spectra and magnetic susceptibility) with the common covariates to predict soil carbon pools in semiarid regions” [Soil Tillage Res. 256 (2026) 106840]

  Soil and Tillage Research · 2026-02-25

  article
  PublisherDOI

• Trade-offs of Conservation Practices in the US Corn-belt: Balancing Soil Organic Carbon, Water Quality, and Crop Yield

  2025-03-15

  preprintOpen accessSenior author

  Agricultural conservation practices (e.g. conservation tillage, cover crops) are critical measures to mitigate nutrient loss and greenhouse gas emissions, enhance soil organic carbon (SOC), and maintain crop yield. Despite these benefits, recent studies indicate that switching to conservation tillage (e.g. no-till) can inadvertently increase nitrate leaching, thereby degrading water quality.  This study presents a meta-analysis of field experiments to elucidate the conflicting outcomes of conservation tillage—increasing SOC levels but simultaneously exacerbating nitrate loss. For instance, SOC in the top 30 cm of soil under no-till (NT) was 14.2% and 4.7% higher than under high-intensity tillage (HT) and intermediate-intensity tillage (IT), respectively. In contrast, nitrate leaching under NT exceeded that under HT and IT by 4.9% and 0.6%, respectively.By leveraging high-resolution datasets of soil characteristics, weather, water quality, land use, and topography, we utilized a comprehensive watershed model, the Terrestrial-Aquatic Sciences Convergence (TASC) to evaluate the combined effects of tillage and cover crops (e.g., winter wheat, rye, and oats) on SOC sequestration, nitrate loading, and crop yield in the Upper Mississippi River Basin (492,000km2). We found that conservation tillage  and cover crops could complement each other. The combined adoption significantly affects water availability, nitrate leaching, SOC, and crop yield. While the integration of cover crops enhances biomass production and SOC, their ability to absorb soil inorganic nitrogen during the non-growing season helps mitigate nitrate leaching. Notably, crop yield under scenarios combining tillage and cover crops surpasses those involving only tillage. However, cover crops can also enhance evapotranspiration, which could potentially aggravate the water availability issues for crop production under future climate conditions. These results underscore the critical need for careful evaluation of the trade-offs between conservation tillage and cover crops when developing policies to address environmental challenges in agricultural ecosystems over the coming decades.

  PublisherDOI

• Soil erosion and lateral carbon fluxes from corn stover-derived biofuel

  Scientific Reports · 2025-05-26 · 1 citations

  articleOpen access

  Abstract Crop residues hold promise to alleviate food vs. fuel competition and contribute to biofuel production. However, the impacts of lateral sediment and carbon fluxes caused by residue removal are not fully understood. Here we employ agroecosystem modeling to conservatively estimate lateral sediment and carbon fluxes resulting from partial corn stover removal in the U.S. Midwest. Results show substantial increases in soil erosion resulting from corn stover removal. For example, the area of continuous corn and corn soybean cropping systems exceeding soil erosion tolerance threshold could increase from 1.1 to 13.3% because of 66% corn stover removal. Depending on removal intensity, conservation, and crop rotation, the stover removal-induced increases in eroded soil organic carbon is equivalent to 3.9–12.5 gCO 2 e MJ −1 , which is comparable to other components of the life cycle impacts of corn stover-derived biofuel. Our findings highlight the need to consider the soil erosion and lateral carbon fluxes impacts of corn stover removal in designing supply chains for cellulosic biofuel production.

  PublisherOA PDFDOI

• Riverine Phosphorus Gain and Loss across the conterminous United States

  Figshare · 2025-12-08

  datasetOpen access

  This dataset provides catchment-scale estimates of riverine phosphorus (both phosphate and total phosphorus) net gain and loss across the conterminous United States (CONUS), based on compiled historical concentration data from the USGS and upstream-downstream catchment topology from the NHDPlus. The compiled observations span multiple decades, with phosphate data from 1952 to 2022 and total phosphorus data from 1958 to 2023, and thus represent long-term, multi-decadal conditions rather than a specific time period. Results include estimates of phosphorus load at USGS stations and watershed gain or loss.The dataset can support water-quality assessments, identification of critical phosphorus source and sink areas at regional scale, load-budget analysis for nutrient management, validation or calibration of watershed models, and effective phosphorus management practices across the CONUS.Related resources: This dataset underlies the manuscript “Riverine phosphorus gain and loss across the conterminous United States,” including the description of methods and data.

  PublisherDOI

• From basin to gulf: Conservation tillage improves soil health but exacerbates hypoxia

  npj Sustainable Agriculture · 2025-08-28 · 3 citations

  articleOpen access

  Abstract Agricultural management practices such as conservation tillage is promoted in the U.S. Midwest for improving soil health, mitigating nutrient loss, and reducing hypoxia in the Gulf of America (GOA). However, large-scale evaluations of tillage impact on soil organic carbon (SOC), water quality, and the implications for hypoxia in the Gulf are lacking. By combining a meta-analysis of field experiments with watershed modelling, this study finds that by 2050, no-till (NT) farming could enhance SOC by ~5.4 MgC ha − 1 , increase streamflow by 17.3%, and reduce soil erosion by ~4.9%, compared to high-intensity tillage (HT). However, widespread NT adoption could raise nitrogen loss, thus expand summer hypoxia of the GOA to 16,500 km², 21.5% larger than the HT scenario. Despite its soil health benefits, conservation tillage may complicate efforts to reduce hypoxic zones to the targeted 5000 km² by 2035. These tradeoffs underscore the need for balanced approaches in future conservation strategies.

  PublisherOA PDFDOI

• Beyond radiation use efficiency: A mechanistic biochemical photosynthesis model for crop growth simulation and agroecosystem modeling

  Computers and Electronics in Agriculture · 2025-03-05 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

• Local Prediction of Temperate Forest Structure in Eastern North America Using LiDAR, Radar, and Optical Data

  2025-10-30

  articleOpen accessSenior author

  Abstract Forest structure underpins the emergence of ecological patterns and processes yet remains costly to measure directly at broad scales. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provides three-dimensional LiDAR measurements at discrete footprints, leaving spatial gaps that complicate wall-to-wall mapping. Few studies have produced high-resolution, broad-extent predictions of multiple GEDI-derived metrics while explicitly accounting for spatial nonstationarity in predictor–response relationships. We addressed this gap by developing a local modeling framework to predict 11 GEDI-based structural metrics at 30-m resolution across temperate broadleaf and mixed forests of eastern North America (1.17 million km 2 ) for 2019–2022. Using Google Earth Engine, we first integrated Landsat and Sentinel-2 multispectral imagery, Sentinel-1 synthetic aperture radar, and auxiliary variables (topography, land cover, leaf traits, and soil properties) to derive 93 environmental covariates. We then partitioned the study area into 1,693 overlapping tiles of 60 km by 60 km each, trained tile-specific random forest (RF) models, and mosaiced tile-level predictions to the full region using distance- and performance-based weights. Local tile-specific predictions of the 11 metrics correlated well with GEDI measurements (Pearson’s r > 0.65). Assessments with independent test data showed that median R 2 of local models exceeded 0.4 for seven metrics, with canopy height and canopy cover both reaching 0.63. The most important predictors included Sentinel-2, topography, and Landsat, identified in at least 69.6% of local RF models. Compared with global full-region models, local models performed better in 56.7% of cases overall, with stronger gains in more heterogeneous tiles and in settings where global models performed relatively poorly. Our results show that, despite overall moderate predictive performance, integrating spaceborne LiDAR with multisource environmental covariates in a local modeling framework can generate continuous, fine-resolution predictions of forest structure across broad geographic regions.

  PublisherOA PDFDOI

• Riverine Phosphorus Gain and Loss across the conterminous United States

  Figshare · 2025-12-08

  datasetOpen access

  This dataset provides catchment-scale estimates of riverine phosphorus (both phosphate and total phosphorus) net gain and loss across the conterminous United States (CONUS), based on compiled historical concentration data from the USGS and upstream-downstream catchment topology from the NHDPlus. The compiled observations span multiple decades, with phosphate data from 1952 to 2022 and total phosphorus data from 1958 to 2023, and thus represent long-term, multi-decadal conditions rather than a specific time period. Results include estimates of phosphorus load at USGS stations and watershed gain or loss.The dataset can support water-quality assessments, identification of critical phosphorus source and sink areas at regional scale, load-budget analysis for nutrient management, validation or calibration of watershed models, and effective phosphorus management practices across the CONUS.Related resources: This dataset underlies the manuscript “Riverine phosphorus gain and loss across the conterminous United States,” including the description of methods and data.

  PublisherDOI

Awards & honors

• 2022 Best Paper award