Restoring and preventing losses of the world’s forests are promising natural pathways to mitigate climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near-surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising the question of whether reforestation contributed to biophysical cooling and slowed local climate change. Using new cross-scale approaches and multiple independent sources of data, our analysis uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground- and satellite-based observations showed that EUS forests cool the land surface by 1-2 °C annually, with the strongest cooling effect during midday in the growing season, when cooling is 2 to 5 °C. Young forests aged 25-50 years have the strongest cooling effect on surface temperature, which extends to the near-surface air, with forests reducing midday air temperature by up to 1 °C. Our analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations predominantly surrounded by reforestation were up to 1 °C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates that reforestation contributed to the historically slow pace of warming in the EUS, highlighting the potential for reforestation to provide local climate adaptation benefits in temperate regions worldwide.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics, as well as limited data availability. We developed a Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable datasets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux datasets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern U.S. rangelands, to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE < 390 g C m-2) than net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE < 180 g C m-2), and captured the spatial variability of surface SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Our RCTM simulations indicated slightly enhanced SOC stocks during the past decade, which is mainly driven by an increase in precipitation. Regression analysis identified slope, soil texture, and climate factors as the main controls on model-predicted C sequestration rate. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of rangeland vegetation biomass, C fluxes, and SOC stocks.

A document by Anam Munir Khan. Click on the document to view its contents.

Monitoring Gross Primary Productivity (GPP), the rate at which ecosystems fix atmospheric carbon dioxide, is crucial for understanding global carbon cycling. Remote sensing offers a powerful tool for monitoring GPP using vegetation indices (VIs) derived from visible and near-infrared reflectance (NIRv). While promising, these VIs often suffer from sensitivity to soil background, moisture, and variations in solar and view zenith angle (SZA and VZA). This study investigates the potential of incorporating shortwave infrared (SWIR) reflectance from MODIS and GOES-R advanced baseline imager (ABI) sensors to improve GPP estimation. We evaluated various formulations for creating SWIR-enhanced Near-InfraRed reflectance of Vegetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux research sites. Our findings reveal that sNIRv improves correlation with GPP for ABI data by up to 0.19 on a half-hourly basis for normalized difference vegetation index (NDVI) values below 0.25, with diminishing gains as NDVI values rise. Using MODIS data, sNIRv matches r values of NIRv for NDVI above 0.25, with a slight 0.05 increase for NDVI below 0.25. Analyses using SCOPE model simulations further support the ability of sNIRv to capture fPAR (fractional photosynthetically active radiation), a proxy for GPP, especially for ecosystems with low LAI. Results highlight that sNIRv-based VIs are less sensitive to soil background, SZA, and VZA compared to NIRv. Shapely Additive Explanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across all different land covers, NDVI ranges, and soil water content (SWC) levels.

Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near-real-time for both cloudy and clear sky conditions at a 5-minute resolution. We compared two machine learning models, Long Short-Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top-of-atmosphere (TOA) observations from the Advanced Baseline Imager (ABI) on the GOES-16 satellite against observations from hundreds of measurement locations for a 5-year period. LSTM outperformed, especially at coarser resolutions and under challenging conditions, with a clear sky R² of 0.96 (RMSE 2.31 K) and a cloudy sky R² of 0.83 (RMSE 4.10 K) across CONUS, based on 10-repeat Leave-One-Out Cross-Validation (LOOCV). GBR maintained high accuracy (R² > 0.90) and ran 5.3 times faster, with only a 0.01-0.02 R² drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized time information in cloudy conditions. A comparative analysis against the physically based ABILST product showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data-driven models for LST estimation and suggests potential pathways for integrating these approaches to enhance the accuracy and coverage of LST products.

The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June-October 2019. The purpose of the study is to examine how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model-data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10×10 km domain of a heterogeneous forest ecosystem in the Chequamegon-Nicolet National Forest in northern Wisconsin USA, centered on the existing Park Falls 447-m tower that anchors an Ameriflux/NOAA supersite (US-PFa / WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft, maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology, and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large eddy simulation and scaling experiments to better understand sub-mesoscale processes and improve formulations of sub-grid scale processes in numerical weather and climate models.

Rice (Oryza sativa) is a major staple food crop in India occupying about 44 million ha (Mha) of cropped land in meeting food requirements for about 65% of the population. As water scarcity has become a major concern in changing climatic scenarios precise measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are needed to better manage the limited water resources and improve irrigation scheduling. The eddy covariance (EC) method was used to determine ETa and Kc of tropical lowland rice in eastern India over two years. Reference evapotranspiration (ETa) was estimated by four different approaches– the Food and Agriculture Organization-Penman-Monteith (FAO-PM) method, the Hargreaves, and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements of turbulent and available energy fluxes were taken using EC during two rice growing seasons: dry season (January-May) and wet season (July-November) and also in the fallow period where no crop was grown. Results demonstrated that the magnitude of average ETa during dry seasons (2.86 and 3.32 mm d-1 in 2015 and 2016, respectively) was higher than the wet seasons (2.3 and 2.2 mm d-1) in both the years of the experiment. The FAO-PM method best-represented ETa in this lowland rice region of India as compared to the other three methods. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet seasons (42–48%) and wet fallows (33-69%) period of both the years of study, suggesting that lateral heat transport was an important term in the energy balance calculation. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages; namely, initial, crop development, reproductive, and late-season were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76 and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in dry seasons, whereas Epan; performed better in wet seasons. The results further demonstrated that the Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation planning.

Surface-atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub-grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub-kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES-16 and ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio-temporal variation compared to geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.

Soil water is essential for maintaining global food security and for understanding hydrological, meteorological, and ecosystem processes under climate change. Successful monitoring and forecasting of soil water dynamics at high spatio-temporal resolutions globally are hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil water monitoring schemes via station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via data fusion of remote sensing (Sentinel-1 and Soil Moisture Active and Passive Mission - SMAP) and land surface parameters (e.g. soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed moderately well across the globe under cropland, grassland, savanna, barren, and forest soils (R = 0.53, RMSE = 0.08 m m). SSM was retrieved and mapped at 100 m every 6-12 days in selected irrigated cropland and rainfed grassland in the OZNET network, Australia. It was concluded that the high-resolution SSM maps can be used to monitor soil water content at the field scale for irrigation management. The SSM model is an additive and adaptable model, which can be further improved by including soil moisture network measurements at the field scale. Further research is required to improve the temporal resolution of the model and map soil water content within the root zone.

Gross Primary Productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and inter-annual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites - R series (GOES-R) to test the ability of sub-daily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We optimized parameters for three models to estimate GPP. A light response curve (LRC) achieved the lowest test mean absolute error for winter (1.82 µmol CO2 m-2 s-1), spring (2.51 µmol CO2 m-2 s-1), summer (1.45 µmol CO2 m-2 s-1), and fall (1.25 µmol CO2 m-2 s-1). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation towards the morning hours during the dry summers. The LRC and the light-use efficiency model were in agreement with these patterns of increasing shift of GPP towards the morning hours during the summer months. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

Climate change is intensifying the hydrologic cycle and altering ecosystem function, including water flux to the atmosphere through evapotranspiration (ET). ET is made up of evaporation (E) via non-stomatal surfaces, and transpiration (T) through plant stomata which are impacted by global changes in different ways. E and T are difficult to measure independently at the ecosystem scale, especially across sites that represent different land use and land management strategies. To address this gap in understanding, we applied flux variance similarity to quantify how E and T differ across 12 different ecosystems measured using eddy covariance in a 10 × 10 km2 area from the CHEESEHEAD19 experiment in northern Wisconsin, USA. The study sites included seven deciduous broadleaf forests, three evergreen needleleaf forests, and two wetlands. Net radiation explained on average 68% of the variance of half-hourly T, which decreased from summer to autumn. Average T/ET for the study period was 55% in forested sites and 46% in wetlands. Deciduous and evergreen forests showed similar E trajectories over time despite differences in vegetation phenology. E increased dramatically after large precipitation events in loam soils but the response in sandy soils was more muted, consistent with the notion that lower infiltration rates temporarily enhance E. Results suggest that E and T partitioning methods are promising for comparing ecosystem hydrology across multiple sites to improve our process-based understanding of ecosystem water flux.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple time scales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study (ChEAS) Ameriflux core site cluster in the upper Great Lakes region of the USA from 1997-2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying exposure and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower CHEESEHEAD19 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became decoupled with time. NEE at the tall tower transitioned from carbon source to sink to a more variable period over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. A declining snowpack offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. No direct CO2 fertilization trend was noted in gross primary productivity, but influenced maximum net assimilation. Direct upscaling of stand-scale sites led to a larger net sink than the landscape tower. These results highlight the value of clustered, long-term carbon flux observations for understanding the diverse links between carbon and climate and the challenges of upscaling observations.