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High-spatiotemporal-resolution leaf area index (LAI) data are essential for sustainable agro-ecosystem management and precise disturbance detection. Previous LAI products were primarily derived from satellite data with limited spatiotemporal or spectral resolutions, which could be overcome with the use of Sentinel-2. While hybrid methods that integrate PROSAIL simulations with machine learning offer advantages in extracting high-spatiotemporal-resolution LAI from Sentinel-2, they still face challenges due to confounding factors related to canopy structure, leaf biochemistry, and soil background. To reduce impacts of these confounders, we developed an efficient hybrid method for Sentinel-2-based LAI retrieval. Our approach consists of random forest models trained on simulated datasets generated by PROSAIL-5B with two refinements: variable canopy fraction of fully senescent leaves (FS) and soil bidirectional reflectance factor (BRF) simulated by Brightness-Shape-Moisture (BSM) model. We corrected canopy BRF using near-infrared reflectance of vegetation (NIR V) and vegetation cover within mixed pixels (V C). For validation, we used ground measurements across different vegetation types from the Copernicus Ground Based Observations for Validation (GBOV) and Korea flux (KoFlux) sites during 2019–2023. Our results showed that coupling BSM and FS with PROSAIL-5B simulations improved hybrid LAI estimates, reducing RMSE by 10.8%–73.8%. Utilizing NIR V and V C to correct canopy BRF better quantified LAI in most vegetation types, with RMSE reduced by 15.3%–64.8%. Our hybrid method showed robust agreement with ground validation data from GBOV (R2 = 0.88, RMSE = 0.71) and KoFlux (R2 = 0.80, RMSE = 0.75). Overall, our method (R2 = 0.58–0.93, RMSE = 0.04–0.83) outperformed both the benchmark Sentinel Application Platform (R2 = 0.11–0.85, RMSE = 0.28–1.67) and data-driven (R2 = 0.09–0.85, RMSE = 0.29–0.93) algorithms in producing precise seasonal LAI data at finer resolutions. Our findings underscore the potential of the proposed hybrid method for high-spatiotemporal-resolution LAI retrieval across diverse ecosystems. [Display omitted] • Canopy fraction of senescent leaves and soil BRF model enhanced PROSAIL simulations. • Correcting BRF by NIR V and accounting for mixed pixels increased sensitivity to LAI. • Sentinel-2 BRF/NIR V , BRF M and BRF M /NIR VM outperformed BRF for hybrid LAI estimation. • Our hybrid method outperformed other relevant high-resolution LAI estimation methods. • Prior knowledge of LAI ranges and leaf angles improved hybrid LAI estimation. [ABSTRACT FROM AUTHOR]

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIR V) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIR V to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIR V effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIR V improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources. • Correcting BRF by NIR V mitigated Fs, ALA, and Cm effects and improved Chl estimation. • BRF/NIR V outperformed BRF to retrieve Chl from Sentinel-2 and Landsat-7/8 imagery. • Hybrid method yielded best Chl estimation (RRMSE = 17.3%) from Sentinel-2 BRF/NIR V. • Canopy BRF with red-edge bands and a higher spatial resolution better estimated Chl. • Sentinel-2 Chl showed more consistent temporal-spatial variations with phenology. [ABSTRACT FROM AUTHOR]

Recent remote-sensing-based global carbon, water and energy budgets over land still include considerable uncertainties. Most existing flux products of terrestrial carbon, water and energy components were developed individually, despite the inherently coupled processes among them. In this study, we present a new set of global daily surface downwelling shortwave radiation (SW), net radiation (R net), evapotranspiration (ET), gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) datasets at 0.05° resolutions from 1982 to 2019, by improving a satellite-based and coupled-process model—the Breathing Earth System Simulator (BESS). The new version of BESS (v2.0) integrated a newly developed ecosystem respiration module, an optimality-based maximum carboxylation rate (V cmax) model, and extended the temporal coverage of flux datasets from 1982 to 2019. We evaluated BESS products against the FLUXNET2015 dataset at the site scale, and against several remote sensing and/or machine learning products on a global scale. At the site scale, BESS products agreed well with FLUXNET measurements, capturing 84%, 53%, 65%, 51% and 31% of daily variation in R net , ET, GPP, TER and NEE, respectively. Interannual variation in BESS NEE showed relatively low consistency with the FLUXNET measurements, while the rest fluxes explained approximately half of the interannual variation. On a global scale, we found marked discrepancies in spatio-temporal patterns between BESS and several benchmark products. Over the period 1982–2019, BESSv2.0 estimated the mean annual global R net , ET, GPP, TER and NEE to be 340.97 ± 5.22 ZJ yr−1 (mean ± 1 SD), 67.67 ± 0.71 103 km3 yr−1, 125.74 ± 5.95 Pg C yr−1, 109.30 ± 3.16 Pg C yr−1, and − 16.28 ± 2.95 Pg C yr−1, respectively, with significant annual linear trends (P < 0.01) by −0.05 103 km3 yr−2 for ET, by 0.52 Pg C yr−2 for GPP, by 0.28 Pg C yr−2 for TER, and by −0.25 Pg C yr−2 for NEE. We further evaluated various coupled processes derived by BESS in terms of functional properties (i.e., Budyko relation, carbon-use efficiency, water-use efficiency, and light-use efficiency), which agreed well with FLUXNET observations, unlike the benchmark products. Overall, BESSv2.0 can serve as a set of reliable and independent products from other global satellite products, facilitating studies related to global carbon, water and energy budgets in a coupled and comprehensive manner. • Generated a set of long-term radiation, energy and carbon flux products using BESSv2.0. • Agreed with FLUXNET and benchmark datasets. • Revealed functional relationships between water and carbon fluxes. [ABSTRACT FROM AUTHOR]

Abstract Quantifying global terrestrial photosynthesis is essential to understanding the global carbon cycle and the climate system. Remote sensing has played a pivotal role in advancing our understanding of photosynthesis from leaf to global scale; however, substantial uncertainties still exist. In this review, we provide a historical overview of theory, modeling, and observations of photosynthesis across space and time for decadal intervals beginning in the 1950s. Then we identify the key uncertainties in global photosynthesis estimates, including evaluating light intercepted by canopies, biophysical forcings, the structure of light use efficiency models and their parameters, like photosynthetic capacity, and relationships between sun-induced chlorophyll fluorescence and canopy photosynthesis. Finally, we review new opportunities with big data and recently launched or planned satellite missions. Graphical abstract Unlabelled Image Highlights • Reviewed history of global photosynthesis since 1950s • Reviewed uncertainties in remote sensing of global photosynthesis • Reviewed emerging opportunities with recent and new satellite missions [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R 2  = 0.76) and APAR and GPP (R 2  = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R 2  = 0.83 and R 2  = 0.88, respectively. Relationships between LUE p (=GPP/APAR) and LUE f (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R 2  = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUE p . These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales. [ABSTRACT FROM AUTHOR]

Incident shortwave radiation (SW), photosynthetically active radiation (PAR), and diffuse PAR (PAR dif ) at the land surface drive a multitude of processes related to biosphere-atmosphere interactions and play a critical role in the Earth climate system. Previous global solar radiation products were spatially coarse (> 50-km resolution) or temporally short (a few years), which hindered scaling-up ground based observations of the land surface processes into regional, continental, and global scales across multiple time scales. Here, we report Breathing Earth System Simulator (BESS) SW, PAR, and PAR dif products over the global land surface at a 5 km resolution with 4 day intervals between 2000 and 2016. We combined an atmospheric radiative transfer model with an artificial neural network (ANN) to compute SW, PAR, and PAR dif . A series of MODerate Resolution Imaging Spectroradiometer (MODIS) atmosphere and land products were used as inputs to run the ANN. We test the performance of the products using data from 158 (SW), 77 (PAR), and 22 (PAR dif ) stations collected in the Baseline Surface Radiation Network (BSRN) and flux tower networks, which covered a range of climatic zones from polar to tropical zones. BESS had strong linear relationships with in-situ SW data (R 2 = 0.95, relative bias = − 2.3%), PAR (R 2 = 0.94, relative bias = 1.7%), and PAR dif (R 2 = 0.84, relative bias = 0.2%). BESS captured the interannual variability of SW at both the site (a majority of long-term BSRN sites) and continental levels. Over the study period, global annual SW, PAR, and PAR dif values did not show any dimming or brightening trends, although these trends appeared at regional levels, e.g. dimming in India. Mean annual SW over the global land surface was 184.8 W m − 2 (875 ZJ yr − 1 , zetta = 10 21 ); 46% of SW was partitioned to PAR, which was further split into direct (59%) and diffuse (41%) components. The developed products will be useful in solar energy harvesting research and will improve water, carbon, and energy flux estimates of terrestrial ecosystems from local to the global scales. [ABSTRACT FROM AUTHOR]

The diurnal sampling capability of geostationary satellites provides unprecedented opportunities for monitoring canopy photosynthesis at multiple temporal scales. At the diurnal scale, only geostationary satellites can currently provide sub-daily data at regular intervals, also it can help to minimize data gaps due to clouds at the seasonal scale. However, the potential of geostationary satellites for monitoring photosynthesis has not been explored in depth. In this study, we tracked diurnal to seasonal variations in gross primary production (GPP) using the product of near-infrared reflectance of vegetation and photosynthetically active radiation (PAR) (NIRvP) over deciduous forests, mixed forests and a rice paddy during the growing season. For this purpose, we generated three levels of reflectance and PAR from Geostationary Korea Multi-Purpose Satellite-2A (GK-2A). We examined how NIRvP derived from GK-2A tracked in-situ GPP data collected from five flux tower sites in South Korea. Bi-directional Reflectance Distribution Function (BRDF) normalized NIRvP agreed well with in-situ GPP over the course of the growing season at hourly (R2 = 0.68–0.77) and daily timesteps (R2 = 0.71–0.83). Atmospheric correction and BRDF normalization improved the performance of NIRvP in tracking GPP at both the diurnal and seasonal time scales. Also, GK-2A showed a much higher percentage of available high-quality BRDF data over the whole growing season for all study sites than the Moderate Resolution Imaging Spectroradiometer (MODIS) (GK-2A: 85%; MODIS: 39%), especially during the cloudy monsoon period. Our findings demonstrated that the unique observation characteristics of geostationary satellites can contribute to large-scale monitoring of diurnal to seasonal GPP dynamics. [Display omitted] • Demonstrated that GK-2A NIRvP well captured diurnal to seasonal variations in GPP. • Quantified greater high-quality BRDF data availability of GK-2A (85%) than MODIS (39%). • Showed importance of atmospheric correction and BRDF normalization for tracking GPP. [ABSTRACT FROM AUTHOR]

Several global gross primary production (GPP) and evapotranspiration (ET) remote sensing products exist, mainly provided by machine-learning (e.g. MPI-BGC) and semi-empirical (e.g. MODIS) approaches. Process-based approaches have the advantage of representing the atmosphere-vegetation-soil system and associated fluxes as an organic integration, but their sophistication results in a lack of high spatiotemporal resolution continuous products. Targeting this gap, we reported a new set of global 8-day composite 1-km resolution GPP and ET products from 2000 to 2015, using a simplified process-based model, the Breathing Earth System Simulator (BESS). BESS couples atmosphere and canopy radiative transfer, photosynthesis and evapotranspiration, and uses MODIS atmosphere and land data and other satellite data sources as inputs. We evaluated BESS products against FLUXNET observations at site scale (total of 113 sites, 742 site years), and against MPI-BGC products at global scale. At site scale, BESS 8-day products agreed with FLUXNET observations with R 2 = 0.67 and RMSE = 2.58 gC m − 2 d − 1 for GPP, and R 2 = 0.62 and RMSE = 0.78 mm d − 1 for ET, respectively, and they captured a majority of seasonal variability, about half of spatial variability, and a minority of interannual variability in FLUXNET observations. At global scale, BESS mean annual sum GPP and ET maps agreed with MPI-BGC products with R 2 = 0.93 and RMSE = 229 gC m − 2 y − 1 for GPP, and R 2 = 0.90 and RMSE = 118 mm y − 1 for ET, respectively. Over the period of 2001–2011, BESS quantified the mean global GPP and ET as 122 ± 25 PgC y − 1 and 65 × 10 3 ± 11 × 10 3 km 3 y − 1 , respectively, with a significant ascending GPP trend by 0.27 PgC y − 2 ( p < 0.05), similar to MPI-BGC products as well. Overall, BESS GPP and ET estimates were comparable with FLUXNET observations and MPI-BGC products. The process-based BESS can serve as a set of independent GPP and ET products from official MODIS GPP and ET products. [ABSTRACT FROM AUTHOR]

Observations of sun-induced chlorophyll fluorescence (SIF) by remote sensing have improved our understanding of the structural and physiological dynamics of vegetation. Substantial efforts have been made to measure SIF with ground-based sensing systems, but field observation data for various plant functional types are still sparse. This is partly due to the limited availability of commercial SIF measurement systems, the relatively high cost of hyperspectral spectroradiometers, and the difficulties of sensor calibration and maintenance in the field. We developed a filter-based smart near-surface remote sensing system for SIF (4S-SIF) to overcome the technical challenges of monitoring SIF in the field, which also decreased the sensor cost, thus enabling more comprehensive spatial sampling. To retrieve SIF, we combined ultra-narrow bandpass filters (full width half maximum <1.3 nm) and photodiode detectors to observe electromagnetic radiation at specific wavelengths (757, 761, and 770 nm). We confirmed that the spectral and radiometric performance of the bandpass filters was satisfactory to retrieve SIF by comparing them to a high-spectral-resolution spectroradiometer that served as a reference. In particular, we confirmed that the digital numbers (DNs) from 4S-SIF exhibited linear relationships with the DN from the reference spectroradiometer in each band (R2 > 0.99). In addition, we developed equations to correct for temperature-induced changes in filter transmittance, such that SIF can be reliably extracted in outdoor environments without the need to actively stabilize the temperature. Furthermore, we confirmed that the SIF signal from 4S-SIF had a strong linear relationship with the reference spectroradiometer-based SIF. Importantly, this relationship held even when the physiological mechanisms of the plant were altered by a herbicide treatment that induced substantial changes in the SIF signal (R2 = 0.85, relative RMSE = 0.22), which indicated that 4S-SIF could be used to retrieve SIF. We believe that 4S-SIF will be a useful tool for collecting in-situ SIF data across multiple spatial and temporal scales. [Display omitted] • We developed a filter-based sensor system to continuously monitor far-red SIF. • We tested the system under laboratory and field conditions. • The system reliably captured far-red SIF as observed with a reference spectrometer. • A herbicide treatment experiment confirmed that the system indeed captured SIF. [ABSTRACT FROM AUTHOR]

Abstract: Emerging near-surface remote sensing techniques have advanced our ability to monitor forest canopy phenology. Thus far, however, little effort has been made to monitor the phenologies of the various canopies of multi-layer forests separately, despite their importance in regulating forest biogeochemical cycles. Here we report phenological changes in multi-layer canopies of deciduous broadleaf and evergreen needleleaf forests in the Republic of Korea during the spring of 2013. We installed light-emitting diode (LED) sensors at four different canopy heights at two sites to measure the normalized difference vegetation index (NDVI) using red and near-infrared (NIR) spectral reflectance and to estimate leaf area index (LAI) using the blue band gap fraction. LED-sensors identified leaf-out dates of over- and understory canopies at both sites; leaves unfolded 8–11days earlier in the understory canopy than the overstory canopy. At the deciduous forest site, LED-NDVI failed to capture the leaf-out date in the overstory canopy, because all four LED-sensors started to see green-up from the understory canopy while the overstory canopy was leafless. LED-LAI identified different leaf-out dates for the over- and understory canopy, because the gap fraction was measured explicitly for each canopy layer. In the evergreen forest site, LED-NDVI signals between the top of the tower and beneath the overstory canopy were decoupled because of the dense evergreen overstory canopy. Both LED-NDVI and LED-LAI identified new needle expansion in the overstory canopy and understory canopy development. MODIS NDVI agreed well with LED-NDVI data (R2 =0.96, RMSE=0.04) at the deciduous forest site, and we discovered that understory canopy development determined the onset of greenness based on MODIS NDVI data. LED-LAI data agreed well with independent estimates from the other instruments, indicating that LED-sensors could be used to monitor multi-layer canopy LAI. Continuous, in-situ observation of multi-layer canopy phenology will aid in the interpretation of satellite remote sensing phenology products and improve land surface models that adopt a multi-layer canopy model. [Copyright &y& Elsevier]

Abstract: Clumping index, the measure of foliage grouping relative to a random distribution of leaves in space, is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. In this study, we retrieve the clumping index using the original 275m resolution data of the Multi-angle Imaging SpectroRadiometer (MISR) instrument over a set of sites representing diverse biomes and different canopy structures. Also for the first time, the MISR derived clumping index values are directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. Our results illustrate that MISR data with 275m allow clumping index estimates at much more pertinent scales (both spatial and temporal) than previous maps from Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) for modeling local carbon and energy fluxes. [Copyright &y& Elsevier]

Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIR V P) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIR V P captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIR V P showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIR V P data for vegetation monitoring and also indicate the potential of using SIF and NIR V P in combination to extract physiological information from SIF. [Display omitted] • Comprehensive evaluation of SIF - NIRvP relationships across space and time. • NIRvP shows a strong and linear correlation to SIF across spatio-temporal scales. • NIRvP shows stronger relationships to GPP than SIF across scales. • NIRvP shows a stronger relationship to SIF than other structural SIF proxies. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the dynamics of leaf area index (LAI) at ecosystem scale is critical for evaluating and modeling the response of vegetation to environmental variability and change. We present an approach for quantifying daily ecosystem LAI that integrates automatic acquisition of a small number of reference photos, viewed towards the zenith, and a larger number of photos covering a spatially extensive area that were manually acquired during periodic field trips. We present an image archive spanning three years for an oak-savanna ecosystem in California to identify the timing of phenological and disturbance events, and to quantify the seasonal to interannual variability of tree LAI at ecosystem scale. The digital camera-derived LAI, corrected for clumping effects, agreed well (r2 =0.94, root mean square error=0.05) with independent estimates of LAI from litterfall traps and the LAI-2000 Plant Canopy Analyzer. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product, which combined grass and tree LAI, we obtained realistic seasonal patterns of ecosystem LAI that corresponded with tree LAI from the digital cameras for the period when grass was dead. The digital camera method is an easy and inexpensive way to monitor LAI at ecosystem scale. This method can be used for testing and improving phenology models, evaluating remote-sensing-derived LAI products, and quantifying forest structures in rapidly changing environments. [Copyright &y& Elsevier]

Solar-induced chlorophyll fluorescence (SIF) provides us with new opportunities to understand the physiological and structural dynamics of vegetation from leaf to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. Here, we report the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, we investigated the relationships of light use efficiency (LUE p), chlorophyll fluorescence yield (Φ F), and the fraction of emitted SIF photons escaping from the canopy (f esc) separately. We found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. Also, there were differential responses of LUE p and Φ F to air temperature. While LUE p reached saturation at high air temperatures, Φ F did not saturate. We found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Φ F (R2 = 0.84) implying that Φ F could be more closely related to pigment pool changes rather than LUE p. In addition, we found that the f esc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUE p to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forest. [Display omitted] • Assessed relationships between GPP and SIF via LUE P , Φ F , f esc in an ENF site. • Found a strongly non-linear relationship between GPP and SIF. • While LUE p reached saturation at high air temperatures, Φ F did not saturate. • Φ F -CCI relationship was strongly linear. • f esc enhanced SIF-GPP correlation. [ABSTRACT FROM AUTHOR]

Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (f esc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R2 = 0.4–0.6) of f esc to the seasonal dynamics of the photosynthetic light use efficiency (LUE P), while the estimated physiological SIF yield was almost entirely uncorrelated to LUE P both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and f esc , explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for f esc , improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIR V) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates. • A mechanistic decomposition of canopy SIF for three in situ crop datasets. • The canopy structure and radiation factor outperforms SIF for GPP estimation. • Canopy escape fraction of SIF correlates with photosynthetic light use efficiency. • Correcting SIF for canopy scattering improves the correlation to APAR but not GPP. • Estimates of physiological SIF yield show no clear seasonal patterns. [ABSTRACT FROM AUTHOR]

Statistical gross primary productivity (GPP) estimation from remote sensing observations has mostly been attempted on the basis of multispectral observations. To make full use of the information contained in vegetation spectra, however, hyperspectral observations should be used in combination with appropriate multivariate methods. Nevertheless, only very few previous studies attempted to estimate GPP directly from hyperspectral observations and did so on the basis of reflectance, with only a limited number of temporally discontinuous observations. In this study, we used long-term, continuous, half-hourly hyperspectral observations covering the visible and near-infrared spectral range to estimate GPP directly from upwelling irradiance using partial least square (PLS) regression in a rice paddy. To gain a better understanding of processes underlying the PLS estimation, we used extensive complementary field observations to run process-based simulations using the SCOPE model. We then applied PLS regression to the simulated hyperspectral data in the same way as for the observations and disentangled contributions related to relevant physiological processes, namely sun-induced chlorophyll fluorescence (SIF) and xanthophyll cycle-related spectral changes (XC). We found that upwelling hyperspectral irradiance in the visible and near-infrared spectral range predicted GPP better than reflectance. Furthermore, PLS-based GPP estimates outperformed both far-red SIF and widely used vegetation index-based methods. However, the most relevant information for the observation-based PLS-models was not clearly related to XC or SIF as the near-infrared spectral range showed comparable performance. Also, the simple average of upwelling irradiance over the 850–900 nm range outperformed the other non-multivariate approaches, including far-red SIF. These results held for the evaluation in terms of the seasonal variation of GPP, while there was apparently a small contribution of SIF and XC for the diurnal variation. The simulation-based analysis showed that SIF and XC contributed useful information to both GPP and photosynthetic light use efficiency (LUE) estimates at both seasonal and diurnal time scales. The strongest unique contribution from either SIF or XC, however, was to the diurnal variation of GPP and XC showed considerably better performance than SIF. We did not find improvements when combining the spectral regions of XC (500–570 nm) and SIF (650–800 nm) to estimate GPP. While SIF showed improvements when combined with the remaining spectral information excluding XC, this was not the case for XC. Our approach combines the strengths of process-based modeling with multivariate statistical analysis to improve our understanding of the usable information content in vegetation spectra and is highly relevant for further developing suitable methods for GPP estimation at large scales. • Hyperspectral GPP estimation outperforms SIF and other multispectral methods. • Upwelling radiation outperforms reflectance for multivariate GPP estimation. • Simulated xanthophyll cycle-related spectral changes very promising for GPP. • No improvements when combining SIF and xanthophyll cycle-related spectral changes. • Clear discrepancies between main mechanisms for observed and simulated results. [ABSTRACT FROM AUTHOR]

Drought events have a major impact on vegetation structure and function. Recently, solar-induced chlorophyll fluorescence (SIF) has been widely used to understand the photosynthesis rates of vegetation under drought stress conditions. However, it is still unclear whether the reduction in SIF shown under drought stress conditions is regulated by physiological or structural factors. To understand the underlying reduction mechanism of SIF under drought stress, we conducted an experiment under a drought condition using one-year-old Prunus yedoensis seedlings. We compared the experiment trees with control trees that were not exposed to drought stress. We collected spectral and gas exchange data to monitor physiological changes and scanned the trees with a terrestrial laser scanner to monitor the structural changes. The discrete anisotropic radiative transfer (DART) and Fluspect models were used to simulate canopy-level SIF. We found that drought stress caused leaf-level steady-state fluorescence yield to increase, while maximum photosynthetic rate, stomatal conductance, and the quantum yield of Photosystem II decreased significantly. Regarding the canopy structural changes, the leaf inclination angle distribution of the experiment trees gradually turned toward erectophile over time (55.2 ± 9.3° to 74.7 ± 6.0°; mean ± standard deviation), whereas that of the control trees remained relatively constant (52.9 ± 8.8°). Furthermore, reduction of crown cover of the experiment trees was 3-fold (77.4 ± 9.8%) compared with the control trees (26.0 ± 16.2%). The simulated nadir-view canopy-level SIF of the experiment trees was reduced 2.8-fold compared to the control trees. These findings, obtained specifically from Prunus yedoensis seedlings, indicate that canopy-level SIF reduced due to the canopy structural changes, although leaf-level fluorescence yield increased. Therefore, canopy structural changes should be considered when attempting to understand SIF reduction in drought conditions. [Display omitted] • Tested SIF reduction under a drought condition using Prunus yedoensis seedlings. • Leaf inclination angles increased in trees subjected to drought stress. • Crown cover reduced 3-fold in experiment trees compared to the control trees. • Drought stress caused leaf-level Fs to increase, while canopy-level SIF decreased. • Canopy structural changes controlled canopy-level SIF reduction under drought. [ABSTRACT FROM AUTHOR]

Satellite-based vegetation monitoring provides important insights regarding spatiotemporal variations in vegetation growth from a regional to continental scale. Most current vegetation monitoring methodologies rely on spectral vegetation indices (VIs) observed by polar-orbiting satellites, which provide one or a few observations per day. This study proposes a new methodology based on diurnal changes in land surface temperatures (LSTs) using Japan's geostationary satellite, Himawari-8/Advanced Himawari Imager (AHI). AHI thermal infrared observation provides LSTs at 10-min frequencies and ∼ 2 km spatial resolution. The DTC parameters that summarize the diurnal cycle waveform were obtained by fitting a diurnal temperature cycle (DTC) model to the time-series LST information for each day. To clarify the applicability of DTC parameters in detecting vegetation drying under humid climates, DTC parameters from in situ LSTs observed at vegetation sites, as well as those from Himawari-8 LSTs, were evaluated for East Asia. Utilizing the record-breaking heat wave that occurred in East Asia in 2018 as a case study, the anomalies of DTC parameters from the Himawari-8 LSTs were compared with the drying signals indicated by VIs, latent heat fluxes (LE), and surface soil moisture (SM). The results of site-based and satellite-based analyses revealed that DTR (diurnal temperature range) correlates with the evaporative fraction (EF) and SM, whereas T max (daily maximum LST) correlates with LE and VIs. Regarding other temperature-related parameters, T 0 (LST around sunrise), T a (temperature rise during daytime), and δT (temperature fall during nighttime) are unstable in quantification by DTC model. Moreover, time-related parameters, such as t m (time reaching T max), are more sensitive to topographic slope and geometric conditions than surface thermal properties at humid sites in East Asia, although they correlate with EF and SM at a semi-arid site in Australia. Additionally, the spatial distribution of the DTR anomaly during the 2018 heat wave corresponds with the drying signals indicated as negative SM anomalies. Regions with large positive anomalies in T max and DTR correspond to area with visible damage to vegetation, as indicated by negative VI anomalies. Hence, combined T max and DTR potentially detects vegetation drying indetectable by VIs, thereby providing earlier and more detailed vegetation monitoring in both humid and semi-arid climates. • Himawari-8 satellite was used to measure diurnal changes in land surface temperature. • Anomalies in diurnal changes correlated with decreases in surface soil moisture. • Himawari-8 diurnal temperature range indicates drying/heat stress in plants. • This monitoring strategy may be used for drying detection even in humid climates. [ABSTRACT FROM AUTHOR]

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V c,max and maximum electron transport rate, J max). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V c,max25 and J max25) traits (n = 135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. We further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V c,max (R 2 = 0.55, nRMSE = 11.79%), J max (R 2 = 0.54, nRMSE = 12.34%), and five additional foliar traits (R 2 = 0.38–0.60, nRMSE = 10.11–13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectra-trait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels. • UAS-based imaging spectroscopy well estimates canopy foliar photosynthetic capacity. • Canopy structure strongly affects the canopy-scale spectra-trait relationships. • UAS imaging spectroscopy maps trait variations at inter- and intra-specific levels. [ABSTRACT FROM AUTHOR]

Recent advances in remotely sensed solar-induced chlorophyll fluorescence (SIF) have provided an exciting and promising opportunity for estimating gross primary production (GPP). Previous studies mainly focused on the linear correlation between SIF and GPP and the slope of the SIF-GPP relationship, both of which lack rigorous consideration of the seasonal trajectories of SIF and GPP. Here, we investigated the timing of seasonal peaks of far-red SIF and GPP in soybean fields by integrating tower data, satellite data, and process-based Soil Canopy Observation of Photosynthesis and Energy (SCOPE, v2.0) model simulations. We found inconsistency between the seasonal peak timing of far-red SIF and GPP in three of four soybean fields based on tower far-red SIF and eddy-covariance measurements. In particular, far-red SIF reached its seasonal maximum 14–17 days earlier than GPP. This far-red SIF-GPP difference in peak timing degraded the correlation between sunny-day far-red SIF and GPP at daily scale (Pearson r = 0.83–0.87 at the site with 14–17 days difference and Pearson r = 0.96 at the site with no difference), and it can be explained by a divergence in the seasonality between absorbed photosynthetic active radiation (APAR) and canopy chlorophyll content (Chl Canopy). We found that the seasonality of far-red SIF - a byproduct of the light reactions of photosynthesis - was primarily controlled by APAR, whereas GPP seasonality was dominated by Chl Canopy. Further, SCOPE model simulations showed that the seasonal patterns of leaf area index (LAI), leaf chlorophyll content (Chl Leaf) and leaf angle distribution (LAD) could affect the different peak timing of SIF and GPP and consequently the seasonal relationship between far-red SIF and GPP. A further increase in LAI after the fraction of light absorption (FPAR) saturates and a later peak of Chl Leaf compared to LAI results in a later peak of GPP compared to far-red SIF. More horizontal leaf angles can further exacerbate this difference. Our results advance mechanistic understanding of the SIF-GPP relationships and combining chlorophyll content information with SIF could potentially improve remote-sensing-based GPP estimation. • We did a rigorous seasonality investigation of soybean canopy SIF and GPP. • SIF reaches seasonal maximum 14–17 days earlier than GPP at three of four sites. • SIF and APAR reach seasonal peak at similar times. • GPP and canopy chlorophyll content (Chl Canopy) reach peak at similar times. • Later peak of LAI and Chl Leaf than FPAR caused the later peak of GPP than SIF. [ABSTRACT FROM AUTHOR]

Vegetation indices are important remotely sensed metrics for ecosystem monitoring and land surface process assessment, among which Normalized Difference Vegetation Index (NDVI) has been most widely used. The newly launched Landsat 8 Operational Land Imager (OLI) sensor, together with its predecessor Landsat 7 Enhanced Thematic Mapper Plus (ETM +), provides continuous earth observations with an 8-day interval. The design improvements of the new sensor, including narrower near-infrared waveband, higher signal-to-noise ratio (SNR), and greater radiometric sensitivity highlight the need for investigating the land surface observation properties, especially its consistency with data from its predecessors and other satellite sensors. This study aims to evaluate the characteristics of Landsat 8 OLI-derived NDVI against Landsat 7 ETM + by cross-comparison and by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI)-derived NDVIs as well as in-situ NDVI measurements. Simulations of Top of Atmosphere (TOA) reflectance and surface reflectance of broadleaf trees and water were conducted for Landsat 8 OLI, Landsat 7 ETM +, and MODIS in order to evaluate the impact of band pass difference on NDVI calculation. Four consecutive pairs of Landsat 8 OLI and Landsat 7 ETM + data over China and Korea were examined, and NDVIs derived from TOA reflectance and surface reflectance by three atmospheric correction methods were evaluated. Both simulations and comparisons showed that NDVIs derived from atmospherically-corrected surface reflectance had good consistency, while the simulation showed that the agreement varied with atmospheric characteristics. The four pairs of Landsat 8 OLI and Landsat 7 ETM + NDVI had a mean bias error within ± 0.05, and R 2 from 0.84 to 0.98. Vegetated land cover types were found to have better NDVI agreement than non-vegetated land cover types. Especially, Landsat 8 OLI consistently generated lower NDVI values in water area than Landsat 7 ETM +, which resulted from higher aerosol optical thickness in atmosphere. Landsat 8 OLI-derived NDVI showed better agreement with MODIS and GOCI NDVI than Landsat 7 ETM +, mainly on vegetated surfaces. Both Landsat 8 OLI and Landsat 7 ETM + surface reflectance-derived NDVI agreed well with in-situ light emitting diode (LED) NDVI measurements at a homogeneous deciduous forest site. Landsat 8 OLI was also found to produce higher spatial variability of NDVIs than Landsat 7 ETM + at vegetated and urban areas, but lower variability on water area. The overall good agreement between Landsat 8 OLI NDVI and Landsat 7 ETM +, MODIS and GOCI NDVIs as well as in-situ measurements ensures that it is reliable to integrate the new sensor observations with those from the multiple satellite sensors, given that the same atmospheric correction methods are applied. Furthermore, the greater NDVI contrast between vegetated areas and water areas, and the higher spatial variability of Landsat 8 OLI NDVI indicated that the new sensor has better capability in land surface process monitoring, such as land cover mapping, spatiotemporal dynamics of vegetation growth, and drought assessment. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) shows great potential to assess plants physiological state and response to environmental changes. Recently the near-infrared reflectance of vegetation (NIRv) provides a promising way to quantify the confounding effect of canopy structure in SIF, while the difference between SIF and NIRv under varying environmental conditions has not been well explored. Here we developed a simple approach to extract the fluorescence yield (Φ F) by the combined use of SIF and the near-infrared radiance of vegetation (NIRvR). The proposed NIRvR approach was evaluated in multiple ways, including with the seasonal leaf-level steady-state fluorescence yield. Results indicate that NIRvR-derived Φ F well captured the seasonal variation of the fluorescence yield changes, and achieved similar results with the existing approach. Both SIF and NIRvR were derived from the airborne imaging fluorescence spectrometer HyPlant for three case studies to evaluate the impacts of light adaptation, heat stress and water limitation on Φ F. For the light adaptation case study, Φ F over the low-light adapted sugar beet field was about 1.3 times larger compared to an unaffected reference area while the difference in NIRvR was minimal, which clearly shows the short-term photosynthetic light induction effect and the ability of SIF to detect plant physiological responses. For the heat stress experiment, Φ F decreased during a natural heatwave in 2015 in the fields of rapeseed from 0.0150 to 0.0130, barley from 0.0152 to 0.0144, and wheat from 0.0146 to 0.0142 which showed signs of senescence, while slightly increased from 0.0125 to 0.0130 in the corn field which was still in growing. At the water-limited sugar beet field, Φ F first increased towards solar noon and then slightly decreased during the afternoon over the water-limited areas from 0.017 to 0.021 and 0.020, with high temperature and high light at noon. The advantages to use SIF/NIRvR as a proxy of Φ F to detect stress-induced limitations in photosynthesis include that the impacts of canopy structure and sun-sensor geometry on the Φ F estimation are explicitly cancelled, and photosynthetically active radiation (PAR) is not required as input. Finally, our approach is directly applicable to satellite-derived estimates of SIF, enabling the study of variations in Φ F to detect the effects of abiotic changes and stresses at large scale. • The slope of SIF and NIRvR indicates the canopy-scale fluorescence yield Φ F. • Φ F can detect several effects of abiotic changes, e.g., the heat stress. • This approach with HyPlant dataset can minimize the canopy structure effects. • This method does not require PAR and FPAR products as inputs to calculate Φ F. [ABSTRACT FROM AUTHOR]

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements. • Previous VIs have not accounted for the shape of the soil spectrum at the red edge. • We propose the NIRvH approach for estimating the true NIR reflectance of vegetation. • NIRvH reduces the soil contamination using the SVD method and a logistic function. • Compared to DVI and NIRv, the proposed NIRvH has the best agreement with SIF. • NIRvH is promising for the anisotropic correction of directional observed SIF. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has emerged as a leading approach for remote sensing of gross primary productivity (GPP). While SIF has an intrinsic, underlying relationship with canopy light capture and light use efficiency, these physiological relationships are obscured by the fact that satellites observe a small and variable fraction of total emitted canopy SIF. Upon emission, most SIF photons are reabsorbed or scattered within the canopy, preventing their observation remotely. The complexities of the radiative transfer process, which vary across time and space, limit our ability to reliably infer physiological processes from SIF observations. Here, we propose an approach for estimating the fraction of total emitted near-infrared SIF (760 nm) photons that escape the canopy by combining the near-infrared reflectance of vegetation (NIR V) and the fraction of absorbed photosynthetically active radiation (fPAR), two widely available remote sensing products. Our approach relies on the fact that NIR V is resilient against soil background contamination, allowing us to reliably calculate the bidirectional reflectance factor of vegetation, which in turn conveys information about the escape ratio of SIF photons. Our NIR V -based approach explains variations in the escape ratio with an R2 of 0.91 and an RMSE of 1.48% across a series of simulations where canopy structure, soil brightness, and sun-sensor-canopy geometry are varied. The approach is applicable to conditions of low leaf area index and fractional vegetation cover. We show that correcting for the escape ratio of SIF using NIR V provides robust estimates of total emitted SIF, providing for the possibility of studying physiological variations of fluorescence yield at the global scale. • The escape ratio of near-infrared SIF can be estimated using NIR V and fPAR. • The approach applies broadly, including sparse canopies with bright soil backgrounds. • The approach allows estimation of total emitted SIF from directional SIF data. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p < 0.01, r2 = 0.77 and p < 0.01, respectively, compared to top-of-canopy ground-based measurements acquired synchronously with the overflight) over the forested study area. Second, maps of GPP and absorbed photosynthetically active radiation (APAR) were derived using a customised version of the coupled biophysical model Breathing Earth System Simulator (BESS). The model was driven with airborne-derived maps of key forest traits (i.e., leaf chlorophyll content (LCC) and leaf area index (LAI)) and meteorological data providing a high-resolution snapshot of the variables of interest across the study site. The LCC and LAI were accurately estimated (RMSE = 5.66 μg cm−2 and RMSE = 0.51 m2 m−2, respectively) through an optimised Look-Up-Table-based inversion of the PROSPECT-4-INFORM radiative transfer model, ensuring the accurate representation of the spatial variation of these determinants of the ecosystem's functionality. The spatial relationships between the measured F and modelled BESS outputs were then analysed to interpret the variability of ecosystem functioning at a regional scale. The results showed that far-red F is significantly correlated with the GPP (r2 = 0.46, p < 0.001) and APAR (r2 = 0.43, p < 0.001) in the spatial domain and that this relationship is nonlinear. Conversely, no statistically significant relationships were found between the red F and the GPP or APAR (p > 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution. • High-resolution LCC and LAI maps were obtained from HyPlant through RTM inversion. • High-resolution GPP and APAR maps were obtained driving BESS with HyPlant data. • High-resolution F 687 and F 760 maps were obtained from HyPlant using SFM in a forest. • F 687 showed a non-significant relation with GPP and APAR in the spatial domain. • F 760 showed a positive nonlinear relation with GPP and APAR in the spatial domain. [ABSTRACT FROM AUTHOR]