How forest structure alters heat signals
Journal of Remote Sensing
image:
HET09. (A) Birch tree nadir and (B) oblique views. (C) Emissivity of HET09 without branches.
view moreCredit: Journal of Remote Sensing
A new study shows that forest heat signals seen by thermal infrared sensors depend strongly on three-dimensional canopy structure, not just leaf or soil properties. Using a detailed 3-dimensional (3D) radiative transfer model across eight realistic forest scenes, researchers found that directional emissivity changes with viewing angle, canopy density, and tree arrangement, offering a more accurate path for forest temperature retrieval and climate-oriented remote sensing.
Land surface temperature and emissivity are essential for understanding ecosystem energy balance, evapotranspiration, vegetation health, and water use. Yet satellites do not directly measure true surface temperature; they record radiance that must be converted through inversion models. This becomes especially difficult in forests, where 3-dimensional (3D) canopy structure makes thermal signals strongly directional. New high-resolution thermal infrared satellite missions are increasing the need for more reliable emissivity estimates, because viewing-angle effects alone can introduce large uncertainties, sometimes several kelvin. Based on these challenges, deeper research is needed on how forest structure controls directional thermal emissivity.
Published (DOI: 10.34133/remotesensing.0738) on 19 February 2026 in Journal of Remote Sensing, the study was conducted by researchers from Université de Toulouse, Beijing Normal University, The University of Hong Kong, Jilin University, Hong Kong Polytechnic University, and partner institutions. The team investigated how forest architecture affects directional thermal infrared emissivity, a key variable for converting satellite radiance into reliable land surface temperature. Their work addresses a major challenge for next-generation thermal missions that seek more precise forest monitoring and climate-relevant observations.
The researchers used the DART 3D radiative transfer model to simulate directional emissivity across eight realistic RAMI-V forest scenes. Two independent DART-based methods produced nearly identical results, confirming the model's reliability. By contrast, three commonly used analytical models were less accurate; FR97 performed best among them, but still showed notable errors at some sites. Across the eight forest types, directional emissivity ranged from 0.972 to 0.996, a spread large enough to produce forest temperature retrieval errors greater than 1 K. The study also found that emissivity generally increased with view zenith angle and remained azimuthally symmetric unless forests were sparse or trees were arranged asymmetrically, such as in rows.
The analysis revealed that canopy architecture strongly controls thermal behavior. Forests with higher leaf area index and more homogeneous tree distribution, such as HET07, HET09, and HET51, showed relatively high mean emissivity values around 0.994. In contrast, row-planted or more open stands such as HET14 and HET16 were closer to 0.985, while winter pine stands with low leaf area index fell to about 0.980 or even 0.974. Reducing tree density in one birch forest case caused emissivity to drop sharply, from about 0.994 to 0.986 or 0.976, depending on removal intensity. The model also showed that neglecting trunks and branches can misrepresent directional behavior, especially at oblique viewing angles. Jacobian analysis further quantified how leaves, wood, and ground each contribute to emissivity changes under different canopy structures.
No direct author quotation is provided in the paper, so the following is a press-style paraphrase based on the authors' discussion: Our results show that forest thermal emissivity cannot be treated as a simple surface constant. It is shaped by canopy architecture, viewing geometry, and the relative contribution of leaves, wood, and ground, making accurate 3D modeling essential for remote sensing and climate applications.
The team modeled eight 100 m × 100 m RAMI-V forest scenes with contrasting structures, including pine, birch, citrus orchard, poplar, savanna, and temperate forest cases. They used DART-Lux, a Monte Carlo version of the DART model, to simulate directional radiance, reflectance, and emissivity over the thermal infrared domain. Simulations were run at 10 μm using fixed emissivity values of 0.98 for leaves, 0.95 for soil, and 0.94 for wood. The study also computed Jacobian maps to measure how changes in reflectance from leaves, ground, and woody components affect directional emissivity.
This work could help improve land surface temperature retrieval for upcoming thermal satellite missions by better accounting for forest structure and observation angle. It also provides a stronger physical basis for multi-angular emissivity correction, especially in forests with sparse cover, low leaf area, or row planting. In the longer term, the approach may support more reliable ecosystem monitoring, climate modeling, and precision observation of forest stress, energy exchange, and land-atmosphere interactions at high spatial resolution.
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References
DOI
Original Source URL
https://spj.science.org/doi/10.34133/remotesensing.0738
Funding information
This work is funded by the National Key Research and Development Program of China (grant no. 2023YFF1303601), the National Science Foundation of China (grant nos. 42090013, 42130104, and 42271338), the China Scholarship Council, and the TOSCA program of the French Space Agency (CNES).
About Journal of Remote Sensing
Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
DART 3D Radiative Transfer Modeling Applied to RAMI Forests – Part 1: Assessing Canopy Structure Effects on Directional TIR Emissivity
Sharper forest insights from spaceborne LiDAR
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Scene and LiDAR configuration diagrams.
view moreCredit: Journal of Remote Sensing
A new study shows that not all simplifications in spaceborne Light Detection and Ranging (LiDAR) modeling are equally reliable. By testing realistic forest canopies, researchers found that some assumptions, such as ignoring woody parts or using one representative leaf spectrum, have only minor effects on simulated waveforms. In contrast, assuming a uniform foliage area volume density can cause much larger errors, especially in clumped canopies. The work offers a clearer route to more accurate forest structure retrieval from satellite LiDAR data.
Spaceborne Light Detection and Ranging (LiDAR) has become a powerful tool for observing forest height, biomass, carbon stocks, and vertical canopy structure. Yet turning waveform signals into detailed structural information remains difficult because canopy elements are complex in shape, density, and spatial arrangement. Many analytical models therefore rely on simplified assumptions about leaves, branches, and canopy density. The problem is that these assumptions are not always tested against realistic forest scenes, making it hard to know which ones are safe and which ones distort the signal. Based on these challenges, deeper research is needed into how canopy structure influences LiDAR waveforms.
Researchers from Beijing Normal University, Université de Toulouse, the University of Hong Kong, and Hong Kong Polytechnic University published (DOI: 10.34133/remotesensing.0737) this study in the Journal of Remote Sensing on February 19, 2026. The team examined how structural assumptions in LiDAR waveform models affect the interpretation of forest canopies, aiming to improve the accuracy of satellite-based forest observation. Their work addresses a practical challenge in remote sensing: how to simplify forest structure enough for efficient modeling without losing the key features that control the LiDAR return signal.
Using the DART 3D radiative transfer model and eight realistic RAMI forest scenes, the study found that several common approximations are more robust than expected. Removing woody components and using a single representative leaf spectrum produced only very small waveform errors in nadir observations at 1,064 nm, with RMSE values at or below 7.0 × 10⁻⁶ mJ under the tested conditions. By contrast, assuming constant foliage area volume density, or FAVD, caused much stronger deviations, with RMSE values ranging from 7.4 × 10⁻⁶ to 9.0 × 10⁻⁵ mJ for voxel sizes from 0.1 to 1.0 m. The results also showed that vertical crown profiles and between-crown gaps play a major role in shaping the LiDAR signal.
The researchers simulated waveforms for eight structurally realistic forest scenes from the Radiative Transfer Model Intercomparison program. They tested the effects of excluding branches, replacing multiple leaf spectra with one spectrum, and converting realistic canopies into voxelized scenes of different sizes. When actual FAVD distributions were preserved, voxelization had little impact for voxel sizes up to 1 m, with RMSE staying at or below 1.8 × 10⁻⁵ mJ. But when FAVD was homogenized, errors increased because dense inner foliage was effectively redistributed toward canopy edges, reducing gap probability and weakening the ground return. The study further showed that broad canopy height variation can create multiple waveform peaks, while more uniform canopies produce smoother, simpler returns. These comparisons helped identify which canopy features most strongly control waveform shape and energy distribution.
Adapted press-style comment based on the paper’s conclusions: “Our results suggest that future LiDAR analytical models should pay much closer attention to how foliage is distributed in space, rather than relying on overly uniform canopy descriptions. Better representation of crown profiles and canopy gaps could significantly improve the retrieval of forest structural information from spaceborne observations.”
The team used the Discrete Anisotropic Radiative Transfer model, or DART, to simulate full-waveform LiDAR signals. They reconstructed eight RAMI benchmark forest scenes and tested both facet-based realistic canopies and voxel-based canopy representations. Simulations used a 1,064-nm wavelength, a 0.1-mJ pulse energy, a 12.5-m footprint radius, and 500,000 photons per pulse. The researchers then compared waveform agreement using RMSE and coefficient of determination values under different structural assumptions.
This work could improve how satellites retrieve canopy height profiles, foliage structure, and biomass-related traits from LiDAR waveforms. It also provides guidance for designing faster analytical models that remain physically reliable. In the longer term, the findings may support more accurate global forest monitoring, carbon accounting, and ecosystem assessment by helping future remote-sensing systems better distinguish which forest details truly matter in waveform interpretation.
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References
DOI
Original Source URL
https://doi.org/10.34133/remotesensing.0737
Funding Information
This work is funded by the National Key Research and Development Program of China (Grant No. 2023YFF1303601), the National Science Foundation of China (Grant Nos. 42130104, 42090013, and 42271338), the China Scholarship Council, and the TOSCA program of the French Space Agency (CNES).
About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
DART 3D Radiative Transfer Modeling Applied to RAMI Forests – Part 2: Lidar Waveform Simulation and Canopy Structure Analysis
Better drought monitoring for dryland ecosystems
image:
Time series of GPP and tower- and satellite-derived SIF in 2019 and 2020, with satellite observation footprints. (A and B) Approximately monthly (±14-d moving window) time series of GPP (μmol CO2 m−2 s−1), tower SIF/π (mW m−2 nm−1 sr−1) TROPOMI SIF (mW m−2 nm−1 sr−1), OCO-2, and OCO-3 SIF (mW m−2 nm−1 sr−1), as well as CSIF (mW m−2 nm−1 sr−1) in (A) 2019 and (B) 2020. (C) Footprints of satellite SIF observations (for reference, the spatial footprint of CSIF is 0.5 × 0.5 km, whereas the TROPOMI footprint is 16 × 16 km). To align with the satellite SIF, we divided SIF by π to correct for the hemispherical SIF measured at the tower.
view moreCredit: Journal of Remote Sensing
A new field study shows that combining multiple optical signals can reveal not only how much carbon a semiarid grassland absorbs, but also how drought alters its internal functioning. By tracking solar-induced fluorescence, near-infrared vegetation reflectance, and a pigment-sensitive reflectance index together, the researchers found a more reliable way to follow seasonal productivity and drought stress than relying on a single indicator alone.
As droughts become more frequent and intense, scientists need better ways to monitor how ecosystems respond. One of the most important measures is gross primary productivity, or gross primary productivity (GPP), which reflects how much carbon plants fix through photosynthesis. Yet GPP cannot be directly observed across large areas. Traditional remote sensing methods often capture visible changes in vegetation structure, such as greener leaves, but miss physiological changes such as reduced light use efficiency under stress. In drylands, where grasses and shrubs shift dominance with water availability, this limitation becomes especially serious. Based on these challenges, deeper research is needed on how different optical signals capture ecosystem productivity and drought stress.
Published (DOI: 10.34133/remotesensing.1034) on 20 February 2026 in the Journal of Remote Sensing, the study was led by researchers from the University of Arizona, working with collaborators from Indiana University, the USDA Agricultural Research Service, the University of Iowa, the University of Virginia, the University of Montana, and Peking University. Focusing on a semiarid grassland in southeastern Arizona, the team tested whether ground-based optical measurements could better track productivity and drought responses in ecosystems where water stress rapidly changes which plants dominate and how efficiently they photosynthesize.
The researchers found that no single optical metric captured the whole story. Across both study years, solar-induced fluorescence (SIF) and NIRvP performed similarly in tracking biweekly GPP, with R² values of 0.77 and 0.79. But during the critical 2020 transition from a wet spring to an extreme summer drought, SIF and the photochemical reflectance index (PRI) outperformed NIRvP. SIF tracked GPP with an R² of 0.82 and zero bias, while PRI reached an R² of 0.80. These results show that metrics sensitive to plant physiology, not just greenness, become especially valuable when drought weakens photosynthesis without immediately changing canopy structure.
The study took place at the Walnut Gulch Kendall Grassland in Arizona during the 2019 and 2020 growing seasons. The site includes both grasses and woody shrubs, and 2020 provided a natural stress test because a wet spring was followed by an exceptionally hot, dry summer. In 2019, GPP followed a more typical bimodal seasonal pattern, peaking at about 4 μmol CO₂ m⁻² s⁻¹ in spring and 9 μmol CO₂ m⁻² s⁻¹ in summer. In 2020, spring GPP rose to about 6 μmol CO₂ m⁻² s⁻¹, then steadily declined as drought intensified. During that shift, grasses remained dormant while shrubs became more important in ecosystem function. Biweekly analysis showed that PRI tracked light use efficiency best, with an R² of 0.81 and 14% bias, while normalized SIF also performed well, with an R² of 0.69 and only 4% bias. The team also compared tower data with satellite products and found that TROPOMI SIF aligned best with tower observations, reaching an R² of 0.51 and capturing major seasonal peaks better than CSIF.
Draft quote for press use: “Our results show that drought does not just reduce plant greenness—it changes the physiology of how ecosystems function. By combining fluorescence and reflectance signals, we can better detect when plants are under stress and improve the way dryland productivity is monitored and modeled.” This wording reflects the paper’s central conclusion that integrated optical indicators provide complementary insight into drought-driven ecophysiological change.
The team combined eddy covariance measurements of carbon exchange with ground-based optical sensing from April to October in 2019 and 2020. They measured SIF using a FluoSpec2 system and derived reflectance-based indices including NDVI, NIRv, NIRvP, and PRI from spectral sensors mounted above the canopy. They also estimated light use efficiency from photosynthetically active radiation and carbon uptake data, then compared ground observations with satellite SIF products from TROPOMI, OCO-2, OCO-3, and CSIF. Analyses were conducted at daily, weekly, and biweekly scales.
The findings suggest that integrating SIF, PRI, and NIRvP could improve drought monitoring and carbon-cycle modeling in drylands, where conventional greenness indicators often miss physiological stress. This matters because semiarid ecosystems cover vast regions and play a major role in global carbon dynamics. The study also points to future opportunities from emerging satellite missions and hyperspectral observations, which may allow these complementary signals to be tracked more consistently across larger landscapes. Better monitoring could ultimately support climate forecasting, ecosystem management, and drought-response planning worldwide.
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References
DOI
Original Source URL
https://doi.org/10.34133/remotesensing.1034
Funding Information
This work was supported by NASA grant 80NSSC23K0109. X.W. acknowledges funding from NASA Future Investigators grant 80NSSC19K1335. W.K.S. and M.P.D. also acknowledge support from NASA grant 80NSSC20K1805.
About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
Proximal Measurements of Solar-Induced Fluorescence and Surface Reflectance Capture Seasonal Productivity and Drought Stress Dynamics in a Semiarid Grassland Ecosystem
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