Product demand and climate variability are progressively increasing the need for real-time, scalable crop monitoring to support varietal selection and in-season input optimisation. However, producers still have limited information on the temporal and spatial variability of cotton health and performance beyond point-scale field surveying. In addition, given cotton's high phenotypic plasticity, near real-time derived metrics are essential to improve input efficiency and strengthen long-term sustainability of the cotton industry in Australia. Therefore, we proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m). Scalability was validated by applying the proposed framework to estimate and map cotton yield across commercial fields. To do this, we explored the accuracy of high-resolution multispectral imagery from two platforms (unmanned aerial vehicle (UAV) and PlanetScope (PS)) collected across two 144-plot trials for two cotton seasons. These were designed with a large range in nitrogen rates (N), shading, and two growth-regulator doses, thus, creating variable environments. Sensing metrics were obtained from UAV imagery (1.3-1.6 cm pixel size) acquired once in 2022/23 and eight times in 2023/24, while PS composites (3 m pixel size) provided near-daily coverage in both seasons. Time-series gaps were imputed using Savitzky-Golay smoothing in thermal time (GDD), enabling extraction of growth dynamic metrics (GDMs) as single-date (SD; e.g., peak canopy) and multi-date (MD; e.g., daily average growth rate) metrics. After reducing collinearity and dimensionality, random forest (RF), support vector regression (SVR), and gaussian process regression (GPR) were trained and interpreted with SHAP, for feature contribution. UAV single-date models (SD_ UAV) achieved strong accuracy for height (R2 = 0.77), biomass (R2 = 0.73), and yield (R2 = 0.81). Incorporating UAV time-series metrics (MD_UAV) improved the performance R2 = 0.87, 0.86, and 0.85 for height, biomass and yield, respectively. Application of the derived models using high resolution satellite data (MD_PS) for different farming systems showed highly significant accuracy (R2 = 0.67) to predict cotton yield at aggregated field scale. As such, enabling the detailed spatial prediction of cotton yield within a field. It is anticipated that the proposed functional sensing framework will improve the estimation of key cotton production traits, supporting field- and within-field decision-making, ultimately contributing to more resilient and sustainable cotton production in Australia.

Gross primary productivity (GPP; ecosystem-level photosynthesis) represents the largest terrestrial carbon flux and is highly sensitive to temperature. Despite global warming, the trends and controlling factors of optimum temperature (Topt) and maximum rates of GPP (GPPmax) remain uncertain. We investigated the drivers of Topt and GPPmax trends during 2000-2019 using global observations of ground-based eddy covariance and satellite-based sun-induced chlorophyll fluorescence. Although GPPmax increased worldwide, Topt increased only in tropical and temperate regions but remained unchanged globally and in arid and cold regions. Thermal acclimation via shifting Topt was constrained by atmospheric and soil dryness, explaining less than 20% of the global GPPmax rise. In contrast, GPPmax trends were more strongly driven by stomatal regulation improving water-use efficiency and by enhanced canopy development under dryness constraints. These findings challenge the expectation that thermal acclimation is central to projecting GPP under warming and highlight dynamic physiological-structural shifts that sustain terrestrial carbon uptake.

Accurate tree volume and structure are crucial for forest biomass estimation and ecosystem investigations. While terrestrial laser scanning (TLS) offers non-destructive pathways for the detailed three-dimensional tree recon struction, current methods overestimate small branch volumes and often require tree segmentation and leaf-wood separation as a priori. This study introduces and validates RayExtract, a novel method for reconstructing woody volume from TLS data, utilising tools from the RayCloudTools library, to automate the extraction of tree struc tural metrics from point clouds. Our method incorporates two key morphological rules - Self-Similarity and Leonardo's Rule - to aid branch radius and taper calculations. Likewise, it enables rapid and automated plot-scale reconstruction by integrating tree segmentation and woody structure modelling without requiring leaf point classification. In this study, RayExtract demonstrated high accuracy across four high-quality destructive harvest reference sets with concordance correlation coefficient (CCC) values ranging from 0.82 to 0.97 (n=124). To explore algorithm behaviours under different leaf conditions and point densities, we implement a framework using TLS simulation of highly realistic synthetic trees. Results from the simulation framework show consistent high accuracy of total woody volume, with CCC ranging from 0.97 to 0.98 (n=18) across four distinct scan ning configurations. Fine-scale volumetric analysis revealed that incorporating simple morphological rules can effectively inform branch taper and reduce woody volume overestimation, particularly in smaller components. Furthermore, it identifies a limitation in volumetric accuracy in trees exhibiting significant taper in the lower stem. Analysis of RayExtract's computational efficiency demonstrates that runtime and memory usage scale pre dictably with input data size, primarily driven by point count and the associated structural complexity within the point cloud, positioning the algorithm as well suited for large-scale applications. RayExtract represents a significant advancement in forest reconstruction, biomass estimation, and vegetation structural analysis. The method's efficiency, accuracy, and robustness across varied forest conditions mark a substantial improvement in forest structural assessment techniques using laser scanning and have broad implications for improving re gional biomass estimations, and contributing to the calibration and validation of broad-scale remote sensing observations.

Satellite earth observation (EO) data plays a vital role quantifying vegetation structural and functional metrics across spatio-temporal scales. However, the degree of coupling between satellite derived spectral signals and the rate of photosynthesis, as estimated by Gross Primary Productivity (GPP), both before and after bushfire remain understudied, yet these are a critical part of the global carbon cycle. This study evaluated a combination of passive optical and active LiDAR satellite data to quantify the disturbance and recovery of photosynthesis from a major fire event. The work was completed at the Tumbarumba long-term tall eucalypt flux site following a catastrophic bushfire in December 2019. TROPOMI solar-induced fluorescence (SIF) and Sentinel 2 derived greenness and burn severity metrics (NDVI, EVI, NIRv, and NBR) were investigated, termed 'spectral metrics' herewith. Detailed in-situ observations from leaf-to-canopy scales were utilised to examine variations in vegetation structural-functional parameters. We found the rate of vegetation spectral metrics recovery largely outpaced GPP recovery at the one- and twoyear post-fire mark. Specifically, SIF recovered to 80-90 % compared to pre-fire levels, whereas GPP recovered only 45-50 %. This indicated that separate SIF:GPP functions were required for pre- and post-fire data to account for different recovery trajectories due to changes in canopy structure and species composition. The use of TROPOMI SIF for monitoring canopy productivity at seasonal (monthly) time-scales was advantageous over traditional greenness-based indices, as SIF tracked GPP seasonality both pre- and post-fire. Spaceborne GEDI LiDAR data effectively captured post-fire changes in forest structure, albeit at sparse spatio-temporal sampling intervals, revealing a significant reduction in overstorey vegetation density and a concurrent increase in understorey vegetation density. This contributed to reduced carbon uptake, compared to pre-fire, due to the lower light use efficiency of understorey species, which was verified with in-situ gas exchange measurements. Overall, this study highlights the importance of accounting for disturbance history and the relative abundance of overstorey and understorey vegetation for tracking GPP from satellite platforms. Our results also highlight the crucial role of longitudinal field-based data for calibration and validation of EO data, ultimately enhancing our understanding of forest recovery processes.

Cotton (Gossypium spp.) growth and yield are influenced by genetic, environmental, and management (G x E x M) interactions, requiring efficient and scalable monitoring tools to support timely management decisions. This study leverages remote sensing (RS) and structural vegetation indices (VIs) to develop algorithms for monitoring cotton growth dynamics and predicting morphological (i.e. height) and productivity (i.e. biomass and yield) traits. These tools support data-driven management strategies aimed at enhancing productivity and sustainability in cotton farming systems. The experiment was conducted at the University of Queensland's Gatton campus during the 2023-2024 growing season. Direct observations included the canopy height and the dry matter from the biomass cuts, just prior to crop defoliation, to capture peak biomass conditions. Eight multispectral images from unmanned aerial vehicle (UAV) were captured throughout the season. To address temporal gaps between UAV flights, we applied a Savitzky-Golay (SG) filter to fit growth curves for each plot and each VI. Derivative-based functions were used on the fitted curves to generate the curve-derived metrics (CDMs). CDMs from both single and multiple dates of the OSAVI index were used as predictors into machine learning (ML) models for the final plant height (m), dry matter (Mg/ha), and yield (kg/ha), achieving strong predictive performance (R-2 = 0.85, 0.87, 0.84; RMSE = 0.05 m, 0.73 Mg/ha, and 118 kg/ha, respectively). Together, these results demonstrate that UAV-derived VIs and SG-based CSMs offer a scalable, accurate framework for real-time monitoring and prediction of key cotton traits-laying the groundwork for their integration into precision management and breeding programs.

A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or 'proximal' remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site-level eddy-covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high-spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar-induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information-rich, enabling us to address key ecological questions unanswerable from space-based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere-critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (gsc) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven gsc model; (3) a gsc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of gsc, finding that AET models with SIF-driven gsc models correlated well with in-situ measured AET (R2 = 0.64). Modelled AET with dynamic variations of gsc generated lower prediction error (0.85 mm day-1), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.

To reduce its greenhouse gas (GHG) impact, the United States government plans GHG Measurement, Monitoring, and Information System (MMIS) activities to better quantify GHG sources and sinks in natural, forested, and agricultural ecosystems. The national strategy discusses several areas where a robust MMIS can be strengthened by near-surface remote sensing (RS) technology—i.e. non-contact measurement of electromagnetic signals by sensors mounted near the ground, on towers, or on drones. Here, we outline specific applications of near-surface RS for an MMIS, using tools presently available and offering guidance on improvements needed for an expansion of their applications. Near-surface RS can help quantify carbon stocks by assessing vegetation structure and function, and it can inform cross-scale understanding of ecosystem carbon processes and properties. The integration of near-surface RS tools into an MMIS will overcome some limitations and uncertainties of current carbon cycle accounting methods and carbon project implementation. Development of robust, standardized near-surface RS systems can be accomplished through transdisciplinary partnerships among government agencies, academics, land managers, and the private sector. The result will hasten achievement of MMIS objectives of improved bottom-up and top-down GHG estimation and improved accessibility and standardization of GHG data and measurements.

In this study, we demonstrated how spectral images of vegetation canopies generated by physically-based radiative transfer modeling can be used for machine learning of convolutional neural networks (CNN) capable of mapping plant biochemical functional traits. Results of ResNet50 CNN, applied on multispectral reflectance images of maize and sugar beet crop canopies simulated with the Discrete Anisotropic Radiative Transfer (DART) model, indicated acceptable accuracies of leaf chlorophyll a+b content estimates for early growth stages of maize and sugar beet crops (R-2 = 0.83, RMSE = 6.9 and 6.6 mu g.cm(-2)). Estimations for the later and across different growth stages of both crops were, however, inaccurate. U-Net CNN, trained on DART reflectance images of a tall eucalyptus forest and applied on a drone-based hyperspectral image, retrieved content of leaf chlorophylls comparably to a random forest, but estimates of carotenoids and anthocyanin were underestimated.

A long‐standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes, and improving their representations in existing biogeochemical models. Here, we compared an optimality‐based model and a semi‐empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (a) each site–year, (b) each site with an additional constraint on IAV (), (c) each site, (d) each plant–functional type, and (e) globally. This was followed by forward runs using calibrated parameters, and model evaluations using Nash–Sutcliffe efficiency (NSE) as a model‐fitness measure at different temporal scales across 198 eddy‐covariance sites representing diverse climate–vegetation types. Both models simulated hourly GPP better (median normalized NSE: 0.83 and 0.85) than annual GPP (median normalized NSE: 0.54 and 0.63) for most sites. Specifically, the optimality‐based model substantially improved from NSE of −1.39 to 0.92 when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi‐empirical model produced statistically better hourly simulations than the optimality‐based model, and site–year parameterization yielded better annual model performance. Annual model performance did not improve even when parameterized using . Furthermore, both models underestimated the peaks of diurnal GPP, suggesting that improving predictions of peaks could produce better annual model performance. Our findings reveal current modeling deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.

Cotton significantly contributes to global agriculture and provides livelihoods for approximately 100 million farmers in 80 countries. Therefore, new approaches are needed to better inform producers, in near-real time, for optimising crop management practices, increasing profitability and sustainability. Here, we investigated the potential of proximal sensing metrics, derived from multispectral and thermal bands onboard an Unmanned Aerial Vehicles (UAVs), to estimate variability in cotton production due to different agronomic practices. We employed three main approaches, including (i) multilinear regression (MR), (ii) random forest (RF) and (iii) partial least square (PLS). All methods showed significantly strong relationship with lint yield. Specifically, the MR approach explained around 88% (R-2 = 0.88, RMSE = 322 kg/ha) of the variance in final yield across all plots. Further research is currently underway to explore the ability of multi-temporal, hyperspectral and radiative transfer models (RTM) to understand variability across different phenological stages in cotton management.

Plant functional diversity (FD) is a component of biodiversity linking plant functional traits to ecosystem processes (e.g., photosynthesis) and services (e.g., gross primary production). Development of remote sensing capabilities to monitor forest FD across various spatio-temporal scales is critical, especially in view of increasing global climate and anthropogenic pressures. Here, we focus on investigating the capability of unoccupied aerial systems (UAS), acquiring imaging spectroscopy data of high spatial (pixel size <= 0.1 m) and spectral (band-width < 5 nm between 400 and 1000 nm) resolutions, to map two trait-based FD metrics, namely, richness and divergence, of two open sclerophyll forests at the plot-scale (<0.2 km(2)). An emerging scalable kernel-based trait probability density (TPD) approach was implemented to compute spatially explicit metrics of FD at different areal extents and pixel sizes through spatially resampled products. Narrow-band spectral indices were utilized as proxies of selected plant functional traits, including photoprotective zeaxanthin-to-antheraxanthin transformation ratio (VAZ), and foliar pigments of chlorophylls and anthocyanins (C-ab and C-ant). The combination of high-resolution imagery and TPDs presents a suitable alternative to the traditional need for taxonomic information and alleviates pixel-based spectral mixing issues known to affect pixel-based FD metrics. A moving kernel (6 x 6 m) applied to UAS data, allowed to capture fine and medium-scale drivers of functional richness and divergence, including within-crown and complex branching variance, topography, sun aspect, and speciation. For the same kernel size, functional richness computed from coarsened pseudo-airborne products (pixel size of 2 m) was found to be 57-68% of that derived from UAS products. Functional divergence did not portray substantial differences across scales and resolutions, even though this metric further emphasized the complexity of the surveyed open-forest sclerophyll sites. UAS have the potential to become an efficient tool for monitoring FD linked with ecosystem processes at key monitoring sites, and for the validation and support of large-scale but less detailed airborne and satellite products. Finally, this study highlights the sensitivity of FD metrics to variations in scale, resolution, and TPD parametrization suggesting that more research is needed to standardize remote sensing protocols for the quantification of FD across spatial and temporal scales.

Canopy temperature (Tc) plays an important role in regulating the rates of mass and energy fluxes at the leaf surface. Better understanding of the relationship between Tc and water availability may enable more accurate monitoring of ecosystem functioning in a changing climate. Here, we used high spatiotemporal resolution thermal infrared cameras deployed at three eddy covariance flux tower sites along a water- to energy-limited gradient - including a predominately water-limited grassland/shrubland site, a seasonally water-limited evergreen needleleaf forest, and a predominantly energy-limited deciduous broadleaf forest - to determine Tc seasonality and its relationship with gross primary productivity (GPP) and environmental drivers. We found midday Tc was generally warmer than air temperature (Tair) during the growing season (Tc:Tair slope: 1.14-1.27) for all sites. Water-limited sites exhibited higher positive Tc deviations from Tair (2.30 +/- 0.06 degrees C) compared to the energy-limited site (1.29 +/- 0.09 degrees C) partly due to their reduced latent heat fluxes during water-limited periods. We further found that the Tc:Tair slope increased with site aridity, namely for 1.14 slope for the grassland, 1.15 for the evergreen forest, and 1.27 for the broadleaf forest. Peak GPP occurred when Tc was higher than Tair across all sites, with peak GPP at the grassland site occurring at +1.1 degrees C (Tc-Tair) and peak GPP at the broadleaf evergreen site occurring at +2.2 degrees C (Tc-Tair). Tc-Tair dynamics were mostly associated with soil water content at water-limited sites where canopies undergo a substantial cooling during the transition from dormancy to the peak GPP, while net radiation played a crucial role at the energy-limited site where the canopy heats up compared to Tair over the same phenological transition. Our findings provide novel insights into Tc-ecosystem water availability links, highlighting the drivers of Tc-Tair across diverse ecosystems in various phenological stages, which has implications for ecosystem management in a changing climate.

Mapping in-situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented up-scaling framework, FLUXCOM-X. The X-BASE products comprise of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP) as well as evapotranspiration (ET) and, for the first time, a novel fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at -5.75 ± 0.33 Pg C ⋅ yr-1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site-years resulting in a wider coverage of bio-climatic conditions. However, X-BASE global net ecosystem exchange shows a very low inter-annual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C ⋅ yr-1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x10³ ± 0.9x10³ km3 globally for the years 2001–2020, but exceeds precipitation in many dry areas likely indicating overestimation in these regions. On average 57 % of evapotranspiration are estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous up-scaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy-covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes.

Mapping in situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05 degrees) and temporal (hourly) resolution. X-BASE estimates the global NEE at -5.75 +/- 0.33 Pg C yr(-1) for the period 2001-2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 +/- 2.1 Pg C yr(-1) for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x10(3) +/- 0.9x10(3) km(3) globally for the years 2001-2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision making by policymakers. This study, addressing one of the scientific goals of the EU Horizon 2020 Land Use Based Mitigation for Resilient Climate Pathways (LANDMARC) project, examines the feasibility of using satellite solar-induced fluorescence (SIF) in combination with other satellite data as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 eucalyptus wildfire in southeast Australia and (2) gradual forest gain resulting from reforestation efforts in northern China in 2007–2012. The cases are monitored using SIF from the TROPOspheric Monitoring Instrument (TROPOMI) and Global Ozone Monitoring Experiment-2A (GOME-2A), respectively. Comparing the temporal variability in SIF across the affected areas and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land-use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in the SIF signal of 0.6 mW m−2 sr−1 nm−1 (−72 %) over the eucalypt forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby representative eddy covariance Tumbarumba site and through the FluxSat product, we estimate that the Australian fire led to a loss in GPP of 130–200 GgC in the first 8 months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing, and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m−2 sr−1 nm−1, coinciding with reforestation efforts between 2007 and 2012. This increase in the SIF signal is likely driven by a combination of increasingly favourable natural conditions and by the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest-cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that increases in both forest cover and soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km2 of additional forest cover, SIF increases by 0.1 mW m−2 sr−1 nm−1 between 2007 and 2012. Our study highlights that the use of satellite-based SIF, together with supporting in situ, modelled, and satellite data, allows us to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent.

Transpiration (T) represents plant water use, while sun-induced chlorophyll fluorescence (SIF) emitted during photosynthesis, relates well to gross primary production. SIF can be influenced by vegetation structure, while uncertainties remain on how this might impact the relationship between SIF and T, especially for open and sparse woodlands. In this study, a method was developed to map T in riverine floodplain open woodland environments using satellite data coupled with a radiative transfer model (RTM). Specifically, we used FluorFLiES, a three-dimensional SIF RTM, to simulate the full spectrum of SIF for three open woodland sites with varying fractional vegetation cover. Five specific SIF bands were selected to quantify their correlation with field measured T derived from sap flow sensors. The coefficient of determination of the simulated far-red SIF and field measured T at a monthly scale was 0.93. However, when comparing red SIF from leaf scale to canopy scale to predict T, performance declined by 24%. In addition, varying soil reflectance and understory leaf area index had little effect on the correlation between SIF and T. The method developed can be applied regionally to predict tree water use using remotely sensed SIF datasets in areas of low data availability or accessibility.

Radiative transfer models that use spatially explicit 3D models to represent forest structure can simulate highly realistic Earth Observation (EO) data. Such simulations at the forest stand scale (>= 1-ha) allow for more direct calibration and validation of EO products. Explicitly reconstructing 3D forest structures at scales that can be compared directly with satellite EO data (i.e., dozens to hundreds of meters) is challenging. Reconstructing large forest areas (>= 1-ha) using a representative subset (i.e., forest subsampling) is a potentially more practical and feasible method. However, the impacts of forest subsampling on radiative transfer (RT) modeling were never formally tested in the spatially explicit forest scene.This study quantified the trade-offs involved in two main subsampling approaches when reconstructing the spatially explicit scene of a real forest for RT modeling. The two subsampling approaches were: (1) subplot subsampling - area-based, using the subplot (i.e. a fixed area) as the basic sampling and reconstruction unit; and (2) tree library subsampling - tree-based, using the individual tree as the basic sampling and reconstruction unit. We used the Discrete Anisotropic Radiative Transfer Model (DART) to simulate the Bidirectional Reflectance Factor (BRF) of the completely reconstructed 1-ha 3D-explicit forest scene, as well as the simplified forest scenes built from various subsets of the same forest. The simulated reflectance deviation of the simplified forest scenes was evaluated by comparing it with the fully reconstructed forest scene.The results showed that for subplot subsampling, as the sampling fraction increased from 10% to 90%, the normalized mean BRF deviation of radiative transfer simulations decreased from -2.7% to -0.0034% and its standard deviation decreased from 7.7% to 0.54%. Additionally, as the sampling fraction increased from 10% to 90%, the normalized mean BRF deviation of tree library subsampling decreased from -7.4% to -1.3% and its standard deviation decreased from 2.8% to 0.51%.Overall, sampling 20% of the forest area using the subplot subsampling method was an effective reconstruction strategy for the temperate deciduous forest. This strategy ensured a normalized mean BRF deviation of -2.9% and a standard deviation of 3.7% in the forest stand studied, with a relatively low reconstruction effort.This study shows that the forest subsampling approach allows for reconstructing 3D-explicit forest scenes for RT modeling at the forest stand scale (>= 1-ha). Accordingly, highly realistic EO data can be modeled at the forest stand scale, which allows for more direct calibration and validation of EO products.