François Jonard

Accurate estimation of crop evapotranspiration (ET) requires resolving how canopy structure and physiological activity regulate land–atmosphere energy exchange. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model using very-high-resolution UAV multi-sensor observations (∼ 15 cm) to estimate ET over a 10 ha field across three seasons (sugar beet, potato, winter wheat). Thermal infrared data provided land surface temperature (LST), while multispectral (MS) and LiDAR data were used to characterize canopy properties, including a fusion-based estimate of the fraction of LAI that is green ( f _g ) from MS-derived green area index (GAI) and LiDAR-derived plant area index (PAI). Results showed strong sensitivity to LST, highlighting the importance of thermal calibration, and demonstrated that while GAI and PAI performed similarly during peak growth, GAI better captured functional decline during stress and senescence, particularly in dense canopies. Incorporating dynamic f _g improved ET estimates across crops and conditions. Modeled instantaneous ET showed good agreement with eddy covariance (EC) measurements, with RMSE ranging from 0.08 mm h ⁻¹ to 0.31 mm h ⁻¹ and R ² values from 0.28 to 0.97 (highest performance excluding senescence). High-resolution UAV-derived LST data and TSEB-PT modeling also enabled early detection of spatial stress patterns not captured by UAV structural or spectral metrics alone. Upscaling instantaneous ET using a reference ET fraction approach produced daily ET estimates with RMSE ranging from 0.96 to 2.30 mm d ⁻¹ and R ² ranging from 0.27 to 0.80 across crops, with improved consistency under energy-balance–closed EC conditions. These daily estimates also enabled assessment of potential crop water stress when compared relative to atmospheric demand (ET _RAD = ET / ET ₀ ) and improved delineation of sub-field variability compared to coarser-resolution methods. These findings demonstrate the value of integrating UAV multi-sensor data with TSEB-PT for robust, high-resolution ET monitoring and practical applications in precision irrigation and water management.

Organic matter decomposition in permafrost soils depends on factors such as soil pH, temperature, and redox conditions. Over lowland permafrost soils, these conditions are shaped by microtopography, which evolves with physical degradation, i.e., lowland thermokarst development. A dynamic quantification of lowland thermokarst development – still poorly constrained – is therefore a critical prerequisite for predictive models of permafrost carbon balance in these areas. Here we provide such a quantification, updated for the Stordalen mire in Abisko, Sweden (68°21 ^′ 20 ^′′ N, 19°02 ^′ 38 ^′′ E), which displays a gradient from well-drained stable palsas to inundated fens, which have undergone ground subsidence. We produced RGB orthomosaics and digital surface models from very high resolution (10 cm) unoccupied aircraft system (UAS) photogrammetry as well as a spatially continuous map of soil electrical conductivity (EC) based on electromagnetic induction (EMI) measurements. We classified the land cover following the degradation gradient using a support vector machine algorithm and derived palsa loss rates. Our findings confirm that topography is a key variable for monitoring palsa loss, nearly doubling the overall accuracy of the classification, while slope enabled the identification of early-stage degradation. We show a clear acceleration of degradation for the period 2019–2021, with a decrease in palsa area of 3.3–3.6 % a ⁻¹ (% reduction per year relative to the initial palsa areal extent) compared to previous estimates of ∼ 0.3 % a ⁻¹ (1970–2000) and ∼ 0.1 % a ⁻¹ (2000–2014). EMI data show that this degradation leads to an increase in soil moisture, which in turn likely decreases organic carbon geochemical stability and potentially increases methane emissions. With a palsa loss of 3.3–3.6 % a ⁻¹ , we estimate accordingly that surface degradation at Stordalen might lead to a pool of 12 × 10 ³ kg of organic carbon exposed annually within the topsoil (23 cm depth), of which ∼ 25 % is mineral-interacting organic carbon. These results demonstrate that UAS monitoring can robustly quantify rapid thermokarst development that coarser methods miss, and provide a transferable framework for tracking permafrost degradation rates at other lowland sites.

This study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework. Among the evaluated single sensor approaches, LiDAR features consistently provided the strongest performance, demonstrating the complementary value of crop height, vertically distributed canopy density, and normalized LiDAR intensity for characterizing canopy structure and within-canopy variability. Multi-layer GF improved AGB estimation relative to conventional ground-based GF approaches, highlighting the importance of incorporating the vertical distribution of canopy density. Multi-sensor fusion produced only modest additional improvements, indicating limited benefits relative to the increased acquisition and processing requirements. Temporal analysis showed that structural LiDAR features were most informative during early crop development, whereas normalized intensity, spectral reflectance, and thermal observations became increasingly valuable during canopy maturation and senescence. Comparisons with destructively measured plant area index (PAI), leaf area index (LAI), green leaf area index (GLAI), and green fraction of LAI further demonstrated that normalized LiDAR intensity (903 nm) was more closely associated with green canopy components than purely structural LiDAR metrics. Overall, the results demonstrate that fully exploiting both the structural and spectral information contained within LiDAR observations can substantially improve UAV-based biomass estimation, while multispectral and thermal observations provide complementary information whose contribution varies with crop development and monitoring objectives.

Field-scale estimation of evapotranspiration (ET) using high-resolution data supports water conservation and yield optimization by enabling localized water use monitoring and early detection of crop stress. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023). Key inputs included thermal infrared (TIR) for land surface temperature (LST), multispectral (MS) and LiDAR data for canopy characterization, and a fusion of MS derived green area index (GAI) and LiDAR derived plant area index (PAI) to derive the fraction of green LAI (f_g). Model outputs were validated against eddy covariance (EC) flux data using footprint modeling. Results showed high sensitivity to LST, emphasizing the importance of accurate thermal calibration. While both GAI and PAI provided comparable LAI inputs during peak growth, GAI better captured functional canopy decline during stress and senescence, especially in winter wheat, where dense structure led to cooling effects unrelated to transpiration. Dynamic f_g improved ET accuracy across all crops, particularly under declining canopy function. Overall, TSEB-PT showed strong agreement with EC measurements (RMSE = 0.14 mm/h, R² = 0.49; R² = 0.81 excluding senescence). UAV TIR based ET maps also revealed early stress signals prior to changes in MS or LiDAR based metrics. This study demonstrates the value of integrating very-high-resolution UAV data with the TSEB-PT model for multi-crop and season-long ET monitoring and early stress detection.

Automated Individual Tree Stem Detection (ITSD) is an important technique for forest management and conservation. In this study, a new method is tested, using unoccupied aerial vehicle (UAV)-derived point clouds from RGB imagery depicting deciduous dense forests in Germany. For the ITSD, the Vertical Complexity Index (VCI) was applied on five different datasets, including four Structure-from-Motion point clouds of a site in the Hainich National Park and one UAV LiDAR point cloud located at the Research Centre Jülich in Germany for comparison. First, four parameters were calibrated on one dataset and then used on the remaining point clouds to test the robustness of the respective method. The results showed that, for the SfM datasets, up to 82% of overstory trees could be detected with a Precision of up to 0.82 and F1-Score of up to 0.78. The LiDAR point cloud achieved an F1-Score of 0.69 using the same parameter set, but performance could be improved up to an F1-Score of 0.86 when adjusting one parameter. False negatives of the SfM datasets can be traced back to leaning trees and a low diameter at breast height (DBH). The application of this new method proved to be robust across different datasets and study sites, showing promising results for RGB-derived point clouds, and considering the low computational power, holding great potential for future analysis. Especially in forest management, the application of this method on low-cost RGB point clouds would be beneficial compared to the more expensive LiDAR imaging.

This study presents the first integration of UAV LiDAR structure (canopy height (CH), multi-layer gap fraction (GF)) and intensity features with multispectral (MS) and thermal infrared (TIR) data for aboveground biomass (AGB) estimation in winter wheat. A shallow artificial neural network (ANN), trained on a limited but high-quality destructive dataset, enabled direct integration of multi-sensor features without complex parameterization, supporting systematic evaluation of individual and combined sensor performance. Among single-sensor inputs, LiDAR features were most effective. LiDAR alone, combining all of its features such as CH, multi-layer GF, and INT, achieved a testing RMSE of 1.73 t/ha (18.27% error) and R² = 0.87, surpassing the common reliance on CH or MS features in UAV-based AGB studies. Multi-layer GF also improved accuracy compared to conventional ground-return GF and was successfully used as a direct ANN input. Fusion with other sensors further enhanced performance, with the best model (LiDAR INT + MS + TIR) reaching a testing RMSE of 1.47 t/ha (16.3% error) and R² = 0.91. Notably, this outperformed fusion models that included LiDAR CH or GF, indicating that INT is a particularly information-rich predictor likely encoding both structural and physiological canopy properties. Furthermore, sensor contributions varied seasonally, with CH and GF most informative during early growth and canopy closure, while MS and TIR became dominant during senescence and stress, with rankings providing practical guidance for sensor selection based on monitoring periods or economic constraints. Results from nitrogen treatments indicated that UAV data captured management effects more effectively than destructive sampling, highlighting the value of spatially comprehensive observations, an advantage that can be further enhanced through the fusion of emerging UAV sensor products. Overall, the findings position LiDAR’s dual structural and spectral information, particularly INT, as a promising breakthrough for improving UAV-based AGB monitoring, with strong potential to advance multi-sensor fusion approaches as algorithms and crop applications broaden.

Forest water dynamics can be assessed on large spatio-temporal scales using satellite-based remote sensing. Vegetation optical depth (VOD), which indicates the vegetation's attenuation of microwaves, contains mainly information on the dry biomass, structure, and water content of the vegetation. Water dynamics can be reflected in short-term variations in VOD. Currently, VOD is operationally retrieved using passive microwave sensors, such as AMSR-2, SMAP, and SMOS, or radar-based sensors, such as ASCAT, with a coarse spatial resolution (tens of kilometers), which hinders the understanding of complex landscapes and is a major obstacle for VOD validation using ground sensors. To overcome this shortcoming of spatial resolution, we utilize synthetic aperture radar (SAR) sensors, such as Copernicus Sentinel-1 (S1) C-band (5.504 GHz), which enables a much higher spatial resolution (tens of meters).This study aims to estimate spatially high-resolution SAR-based VOD across different forest ecosystems, using both VH and VV polarizations, and ultimately assess forest water dynamics. We employ soil and vegetation physical scattering models (De Roo et al., 2001; Ulaby & Long, 2014) and constrain the effective scattering albedo (ω), which indicates the ratio of scattering to absorption of vegetation. In our dual-channel approach, we utilize in situ soil moisture from forest ecological observatories and co-polarized S1 backscatter as direct model inputs, and characterize the vegetation structure (ω) using cross-polarized S1 backscatter to estimate SAR-based VOD. We test our approach across two deciduous broadleaf and three evergreen needleleaf forest ecosystems in Central Europe for up to three years (2023-2025). In addition, we compare our SAR-based VOD with VOD estimates from Global Navigation Satellite System-Transmissometry (GNSS-T), derived from a pair of in situ receivers: one located at the top of the canopy and one on the ground for each test site (Brede et al., 2025). We validate our approach using in situ plant gravimetric moisture content (mg; [kgwater/kgwet biomass]) measurements of the tree canopy and remote sensing-based leaf area index. We will also transfer our dual-channel approach to the agricultural site of the Land-Atmosphere Feedback Initiative (LAFI) and other ecosystems in a later step. In the end, spatially high-resolution satellite-based SAR-based VOD enables not only analyses of forest water dynamics but also small-scale up to stand-based assessments of plant hydraulics. ReferencesBrede, B., Schellenberg, K., Camps, A., Chaparro, D., Damm, A., Forkel, M., Frankenberg, C., Ghosh, A., Hartmann, H., Herold, M., Humphrey, V., Jagdhuber, T., Konings, A., Kurum, M., Niederberger, M., Schmullius, C., Stassin, T., Steele-Dunne, S., Borght, N., …, Jonard, F. (2025). VODnet: a virtual GNSS-T VOD network for monitoring of forest water budget and structure. https://doi.org/10.13140/RG.2.2.17146.35522.De Roo, R. D., Du, Y., Ulaby, F. T., Dobson, M. C. (2001). A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion. IEEE Transactions on Geoscience and Remote Sensing, 39(4), 864–872. https://doi.org/10.1109/36.917912.Ulaby, F. T., Long, D. G. (2014). Microwave radar and radiometric remote sensing. University of Michigan Press.

Climate change is expected to significantly alter regional fire regimes and forest vulnerability in temperate regions, including western Europe. In Belgium, wildfires have historically been relatively rare, but recent regional studies show a potential threat to population due to an increase of fire-prone climate conditions (Cerac, 2025). In this study, we assess future fire occurrence and tree mortality in Belgium over the 21st century using high-resolution, downscaled climate projections from the CMIP6 ensemble. Daily temperature, radiation, precipitation, humidity, and wind fields are dynamically downscaled by the regional climate model MAR (Grailet et al, 2025) to drive the dynamical vegetation model CARAIB (Verma et al, 2025) to derive occurrence of vegetation ignition and tree mortality for selected widespread species in Belgium. The simulations are performed for multiple baseline emission scenarios from IPCC (SSP2-4.5, SSP3-7.0 and SSP 5-8.5).We show the main behavior of fire ignition occurrence and tree mortality obtained from the modeling exercise, first with a verification of the capabilities on the past period (1980-2025) when possible, and then with the future modelled trends (till 2100), especially in relation with the increase in the frequency and duration of summer drought periods and of the compound heat-dry events. Limitations of this exercise will be discussed to frame our future work.This work provides one of the first climate-driven assessment of future fire risk and forest mortality for Belgium in the wake of the national climate downscaling experiment Cordex.Be2 (https://cordex.meteo.be/). It highlights emerging threats to temperate belgian forest ecosystems and offers a frame for quantitative information to support long-term forest management and adaptation strategies.Cerac (2025): https://www.cerac.be/sites/default/files/media/files/2025-02/rpt_wildfire_risks_in_belgium_20250228_cerac_ngi_en_v2.0.pdfVerma et al (2025): https://www.sciencedirect.com/science/article/pii/S0301479725003056Grailet et al (2025): https://gmd.copernicus.org/articles/18/1965/2025/

Permafrost is degrading, raising concerns about its impact on global climate. Taliks, year-round unfrozen layers of ground in permafrost environments, affect Arctic hydrological and carbon dynamics and contribute to permafrost degradation. Despite their importance, they are seldom studied because of the difficulty in identifying them. Water tracks (zones overlying permafrost concentrating water flow) have an important role in controlling catchment hydrology, yet their contribution to talik formation remains poorly understood. The objective of this study is to detect and characterize suprapermafrost taliks in three-dimensions at high spatial resolution (dm-scale), with a specific focus on those located beneath water tracks. Fieldwork was conducted in a discontinuous permafrost area near Eight Mile Lake, Alaska (USA), during winter using a Ground-penetrating radar with a 600 MHz antenna. Ground-penetrating radar proved to be a reliable tool for imaging talik depth and extent. While talik tops were clearly detected with a root mean square error of 17 cm, their bottoms were less identifiable due to limited signal penetration. Both isolated and lateral suprapermafrost taliks were observed at the site. Importantly, taliks were more present, shallower and thicker in water tracks than in their adjacent areas. Additionally, thicker snow cover and topographic depressions were significantly associated with shallower taliks (respective significant correlations of -0.65 and 0.53). Water tracks thus appear to be hotspots for talik formation and expansion, with important implications for winter subsurface hydrological connectivity. These findings highlight the need for increased attention to the processes occurring during the winter in the water tracks environments.

Climate change significantly impacts vegetation ecosystems, and their modification may create feedback loops exacerbating regional effects of global warming. Accurately simulating vegetation dynamics and their interactions with the atmosphere is crucial for understanding and mitigating these impacts. Regional earth system models offer the possibility to study the retroaction between the atmosphere and the vegetation at regional to continental scale by incorporating vegetation dynamics in climate models. In this study, we quantify the sensitivity of the Modèle Atmosphérique Régional (MAR) to vegetation representation at daily to annual scales over a temperate region of Europe, by means of both synthetic experiments and realistic studies. Our sensitivity study on the Leaf Area Index (LAI) dynamics reveals non-linear responses on meteorological variables, with asymmetric effects relative to the direction of the change. For example, a 92 % reduction in LAI led to an 83.4 % decrease in evapotranspiration and an 88.9 % drop in evaporation. Conversely, a 178.4 % increase in LAI resulted in smaller, yet significant, increases of 29.8 %, 27.4 % respectively. At the seasonal-time scale, evapotranspiration and albedo have the strongest shifts in summer and winter, while relative humidity and rainfall responded more prominently in spring. Furthermore, we assessed the model performance in simulating daily evapotranspiration and daily maximum temperature during extreme events by comparing simulations incorporating 8-day MODIS LAI data with those based on climatological LAI. Although the improvements were more subtle than those resulting from a change in LAI source, the 8-day observation-based LAI enhanced the model’s capacity to capture shorter events compared to the static LAI. This refinement also helped understanding how various vegetation types respond to extreme events. The findings highlight the need to integrate dynamic vegetation into regional climate models to enhance their representation of biosphere- atmosphere interactions and provide more accurate tools to assess the impacts of climate change on natural ecosystems.