Accurate estimation of gross primary production (GPP) is essential for understanding the carbon cycle in terrestrial ecosystems. Although numerous models have been developed to estimate GPP, many require a large number of input variables and parameters. For example, light-use efficiency (LUE) models typically rely on multiple inputs, such as photosynthetically active radiation (PAR), the fraction of absorbed PAR, air temperature, vapor pressure deficit, and biome-specific parameters. This dependence on multiple data sources can introduce additional uncertainties into GPP estimates. Here, we developed and evaluated an efficient chlorophyll-based canopy photosynthesis model (CPM) for estimating GPP using the Sentinel-3 OLCI-derived canopy chlorophyll content (CCC) and the canopy chlorophyll absorption coefficient in the red-edge band (alpha(RE)). In the CPM framework, GPP is estimated from the product of potential incident PAR (PAR(pot)) and remotely sensed indicators of photosynthetic capacity, such as CCC or alpha(RE). By using PAR(pot) instead of actual PAR, CPM reduces its dependence on ancillary environmental inputs. We evaluated the CCC-based and alpha(RE)-based CPM models using data from 301 eddy covariance flux sites distributed globally. Based on leave-one-site-out cross-validation, the global CPM models accurately estimated GPP using CCC & times; PAR(pot) (R-2 = 0.76, RMSE = 1.79 gC.m(-2).d(-1), nRMSE = 6.73%) and alpha(RE)& times;PAR(pot) (R-2 = 0.73, RMSE = 1.91 gC.m(-2).d(-1), nRMSE = 7.15%). The CPM models outperformed the widely used near-infrared reflectance of vegetation (NIRv)-based method and the MODIS GPP product. We further evaluated the ability of CCC & times; PAR(pot) to capture global photosynthetic patterns using TROPOMI sun-induced chlorophyll fluorescence (SIF) as a benchmark and found a strong correlation between CCC & times; PAR(pot) and SIF (R-2 > 0.80). These findings demonstrate that the CPM framework relying solely on CCC and alpha(RE) provides accurate and consistent estimates of GPP, offering a practical approach for improving assessments of the global carbon cycle and ecosystem responses to climate change.

Mature non-stressed plants often contain a lot more chlorophyll than they need to efficiently capture light energy in the PAR range. In this situation, some pigment molecules apparently become physiologically redundant because they remain shaded and cannot participate efficiently in light harvesting. As a result of the build-up of chlorophyll, strong absorption of these pigments extends well beyond 700 nm, the conventional border of PAR, into far red (FR) region of the spectrum (to 750 nm and beyond) contributing significantly to the budget of the absorbed light energy. It is also well known that FR light, when supplemented to conventional PAR spectrum, harmonizes energy flow in the photosynthetic apparatus, reduces risk of photodamage boosting plant productivity. We argue that a possible functional role of the "redundant chlorophyll" accumulated in plants is ensuring the capture of FR photons. The latter is among important acclimations to fluctuating light fluxes as well as to permanently low-light environments ensuring efficient operation of complex plant canopies. We discuss the opportunity to harness the "FR boost" of productivity by leveraging inherent optical properties of green plants without sophisticated approaches such as engineering of long-wave chlorophylls into the plant photosynthetic apparatus.

The amount of absorbed light is one of the main factors governing plant photosynthesis, and ultimately, the gross primary production (GPP) of vegetation. Since canopy chlorophyll (Chl) content defines the amount of light that can be absorbed (thus the amount of energy available for photosynthesis), it is representative of the status of the photosynthetic apparatus and directly relates with vegetation productivity. The non-invasive assessment of these traits is the foundation of proximal and remote sensing and of high-throughput phenotyping of plants. The goal of this study is to explore: (i) the response of GPP to the absorption coefficient of Chl derived from canopy reflectance (i.e., assessed in situ) across the PAR and red-edge spectral regions in two plant species with contrasting biochemistry, structural properties, and photosynthetic pathway; (ii) the efficiency of contrasting plants in absorbing radiation and converting it into photosynthetic carbon uptake. The spectral composition of light absorbed by vegetation and the contribution of each spectral range to GPP were quantified. The highest responses of GPP to the Chl absorption coefficient occurred in the red-edge and green spectral regions. More notably, in contrasting plant species the GPP responses in the visible and red-edge spectral regions were almost identical and close to the quantum yield of CO2 fixation. This potentially opens a novel avenue for the remote assessment of the quantum yield of photosynthesis. The uncertainty of the relationship between GPP and Chl absorption coefficient and its impact on the estimation of photosynthetic rates was also quantified.

Environmental stresses on plants are among the major limitations to crop productivity. Plants are naturally equipped with a set of acclimatory mechanisms enabling them to withstand stresses without irreversible damage. Here, we overhauled the approach to non-invasive gauging of the response of maize to combined drought and high-light stresses. This was done to leverage the advantages of a recently developed framework based on using the reflected signal absorption coefficient modality. We document the deployment of two responses, one based on non-photochemical quenching (NPQ) of the absorbed light energy and the second on the adjustment of leaf photosynthetically active radiation (PAR) interception. The NPQ-based response, implemented by the xanthophyll cycle and inferred from changes in the reflection of light in the blue-green region of the spectrum, engaged at the beginning of the stress but quickly reached a plateau. We demonstrate that altering leaf absorption in PAR is a fundamental plant response mechanism to diverse stresses. This response is quantified by the PAR absorption coefficient retrieved from the non-destructively measured reflectance. A profound decrease in the PAR absorption coefficient in the whole PAR region was shown to be a reliable measure of the degree of stress regardless of its cause. The effects of superficial shading and leaf position were readily detectable by the leaf PAR absorption coefficient. We consider this report a proof of concept for quantifying plant stress and monitoring the acclimation state via the absorption coefficient in the PAR region through non-destructive, remotely sensed techniques.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Absorption of radiation in the photosynthetically active radiation (PAR) region is significantly influenced by plant biochemistry, structural properties, and photosynthetic pathway. To understand and quantify the effects of these traits on absorbed PAR it is necessary to develop practical and reliable tools that are sensitive to these traits. Using a semi-analytical modeling framework for deriving the absorption coefficient of plant canopies from reflectance spectra, we quantify the effects of functional, structural and biochemical traits of vegetation on the relationship between the absorption coefficient in the PAR region (αpar) with canopy characteristics such as the fraction of PAR absorbed by photosynthetically active vegetation (fAPARgreen) and chlorophyll (Chl) content. The reflectance dataset used in the study included simulated data obtained from a canopy reflectance model (PROSAIL) and empirical data on three diverse crop species with different leaf structures, canopy architectures and photosynthetic pathways (rice, maize and soybean) acquired at proximal (i.e., using field spectroradiometers) and remote (i.e., Landsat TM and ETM+) distances. Results show the usefulness of αpar derived from reflectance data for assessing not only the photosynthetic status of vegetation, but also the effects of different functional, structural and biochemical traits on plant performance. Furthermore, these assessments can be made using data acquired by satellite sensor systems such as the Landsat series, which are available since the 1980s, thus facilitating the analysis of the photosynthetic status of terrestrial ecosystems throughout the world with a high temporal depth.

An important aspect of precision horticulture methodology is comprised by online monitoring of apple crop load, gauging fruit expansion, and estimating the extent and the rate of ripening. Remote monitoring based on multiand hyperspectral imaging is a powerful tool for solving this problem. Still, its potential is often limited by insufficient understanding of the relationships between the observed changes in fruit optical properties and the evolution of the fruit biochemistry and other properties in the course of ripening. Non-invasive estimations of apple ripening are based predominantly on the chlorophyll degradation kinetics derived from the spectral reflectance data calibrated against biochemical and rheological assays. Nevertheless, the fruit skin chlorophyll displays irregularities in response to fluctuation in environmental (sunlight, temperature) and other stimuli obscuring the changes caused by ripening itself. Another major pigment group, carotenoids display the opposite pattern of changes: these pigments are often retained or even accumulated during apple ripening. Consequently, the ratio of the contents of carotenoids and chlorophylls displays more robust trends in apple fruit ripening reflecting the variation in on-tree and off-tree ripening rate as affected by environmental stimuli as compared to assessment of degradation of the chlorophylls alone. We propose to use the previously developed in our group plant senescence reflectance index (PSRI), which is closely related with carotenoid-to-chlorophyll contents ratio in fruit and hence with their ripening. The PSRI values plotted vs. chlorophylls (also derived from fruit reflectance) provide for a robust recording of apple ripening both on tree and in storage yielding data sets comparable across orchards and growing seasons. We demonstrate the applicability of this approach to reveal the effect of apple fruit picking date on their ripening rate in storage. We also test the ‘PSRI vs. chlorophylls’ approach for visualization of apple ripening on hyperspectral images and discuss its limitations and possible workarounds.

Changes of chlorophyll (Chl) and carotenoid (Car) contents and their ratio (Car/Chl) represent a sensitive indicator of vegetation photosynthetic activity, developmental changes, and stress responses. The goal of this study was to design methods for estimating Car/Chl in plants across species, seasonal changes and ontogenetic phases requiring no species-specific parameterization. Four tree species (maple, chestnut, beech, and elm), wild vine shrub, and two crops species (maize and soybean) featuring contrasting leaf structure and photosynthetic pathways, a wide variation of pigment content and composition were studied. Two models based on leaf pigment absorption coefficients retrieved from reflectance spectra were proposed and tested. The first model uses the ratio of absorption coefficients at 500 and 700 nm and the second one-the difference between absorption coefficients at 500 and 660 nm. Both models accurately described Car/Chl changes in the range from 0.15 to 0.6 with determination coefficients R-2 of 0.87 for the first model and 0.82 for the second; algorithms for Car/Chl estimation did not require parameterization for each species accurately assessing Car/Chl with normalized root mean square error below 11 % and 14 %, respectively. The findings of a close relationship between leaf absorption coefficients, retrieved from reflectance, and Car/Chl present the first step towards accurate generic quantification of pigment composition and hence the progression of developmental stages, impact of stresses, and potential photosynthetic activity.

Climate change can impose large offsets between the seasonal cycle of photosynthesis and that in solar radiation and temperature which drive it. Ecophysiological adjustments to such offsets in forests growing under hot and dry conditions are critical for maintaining carbon uptake and survival. Here, we investigate the adjustments that underlie the unusually short and intense early spring productive season, under suboptimal radiation and temperature conditions in a semi-arid pine forest. We used eddy covariance flux, meteorological, and close-range sensing measurements, together with leaf chlorophyll content over four years in a semi-arid pine forest to identify the canopy-scale ecophysiological adjustments to the short active season, and long seasonal drought. The results reveal a range of processes that intricately converge to support the early spring peak (March) in photosynthetic activity, including peaks in light use efficiency, leaf chlorophyll content, increase in the absorption of solar radiation, and high leaf scattering properties (indicating optimizing leaf orientation). These canopy-scale adjustments exploit the tradeoffs between the yet increasing temperature and solar radiation, but the concurrently rapidly diminishing soil moisture. In contrast, during the long dry stressful period with rapidly declining photosynthesis under high and potentially damaging solar radiation, physiological photoprotection was conferred by strongly relaxing the early spring adjustments. The results provide evidence for canopy-scale ecophysiological adjustments, detectable by spectral measurements, that support the survival and productivity of a pine forest under the hot and dry conditions, which may apply to large areas in the Mediterranean and other regions in the next few decades due to the current warming and drying trends.

Semi-arid forests represent some of the most sensitive ecosystems to climate change. Identifying adjustments to extreme conditions can indicate their resilience, and that of forests undergoing increasing aridity trends. We used eddy covariance and close-range sensing measurements over four years in a semi-arid pine forest to identify canopy-scale adjustments to the short active season and long seasonal drought. Peaks in light use efficiency (LUE), leaf chlorophyll content (LCC), and increasing absorbed photosynthetic active radiation (APAR; based on canopy absorption coefficient in the green range), all converged to support an early peak (March) in gross primary productivity (GPP), exploiting the narrow optimum between PARin, temperature and the rapidly decreasing soil moisture in spring. In contrast, during the long dry period (>200 days), while PARin increased, LCC and LUE decreased, offering physiological photoprotection as GPP sharply declined under the stressful conditions. The strong negative correlation between ρNIR and PARin indicated canopy biophysical adjustments that enhance light absorption under low radiation and eliminate photodamage under excessive radiation. The results provide clear indications of canopy-scale adjustments underlying the high productivity of the forest and its resistance to the harsh conditions, which may soon apply to forests in currently milder climatic regions.

The absorption of Photosynthetically Active Radiation (PAR) by different foliar pigments defines the amount of energy available for photosynthesis and also the need for photoprotection. Both characteristics reveal essential information about productivity, development, and stress acclimation of plants. Here we present an approach for the estimation of the efficiency by three foliar pigment groups (chlorophylls, carotenoids, and anthocyanins) at capturing light, via the absorption coefficient derived from leaf reflectance spectra. The absorption coefficient (and hence light capture efficiency) of the pigment is quantitatively related to the ratio of light absorbed by each pigment group over the total amount of light absorbed by the leaf. The proposed approach allows discerning the contribution of pigment groups to the overall light absorption, despite the strong interference by other pigments with overlapping absorption spectra. For photosynthetic pigments, like chlorophylls, this is indicative of the energy captured for photosynthesis and hence of potential plant productivity. For photoprotective pigments, like anthocyanins or secondary carotenoids, it gives information about the spectral ranges where their optical screening works best and their screening capacity. In addition, the approach allows the selection of optimal spectral bands where different pigments operate. Such information improves our understanding of the phenological, physiological and photosynthetic dynamics of plants over space and through time, useful for developing better monitoring and management strategies.

Gross primary production (GPP) is a measure for crop productivity, indicating yield and expressing C exchange of agro‐ecosystems. A multitude of satellite sensors at varying spatial and spectral resolution brings a possibility to use remotely sensed data for regional and global GPP estimation. More work is still needed to develop algorithms for GPP estimation applicable to multiple, if not all, vegetation types, phenological phases, and environmental conditions. This study employed neural networks (NN), multiple linear regressions (MLR), and vegetation indices (VI) to develop algorithms for GPP estimation based solely on remotely sensed data in two crops, maize (Zea mays L.) and soybean [Glycine max (L.) Merr.], with contrasting canopy architectures, leaf structures, and photosynthetic pathways. The focus of the study was to devise algorithms not requiring re‐parameterization for different crop species. Data used in the models included in situ hyperspectral reflectance and satellite surface reflectance products. For the tested NN, MLR, and VI algorithms, the bands selected to obtain minimal errors in maize and soybean combined were mainly located in red edge and near infrared (NIR) spectral regions. For both in situ reflectance and satellite surface reflectance, a NN estimated GPP with normalized root mean square errors (NRMSE) below 14 and 18.7%, respectively, and VI using bands in red edge and NIR with NRMSE 15.6 and 20.4%, respectively. The results showed that the models based on the red edge and NIR bands may facilitate accurate assessments of crop GPP at multiple scales, from close range to satellite platforms.Core Ideas The models using remotely sensed data allow accurate estimation of gross primary production in two crops. The optimal bands for gross primary production estimation in two crops were in near infrared and red edge regions. Vegetation indices with red edge and near infrared reflectance were generic for maize and soybean.

The objective of this study is to develop generic algorithm for estimating fraction of radiation absorbed by photosynthetically active vegetation not requiring parameterization for C3 and C4 crops, maize and soybean, with contrasting photosynthetic pathways, canopy architectures and leaf structures. The study based on data acquired during eight years, altogether 16 site years of maize (12 irrigated and 4 rainfed, nine hybrids) and for 8 site years of soybean (4 irrigated and 4 rainfed, three cultivars). The red edge normalized difference vegetation index (NDVI), green NDVI and wide dynamic range vegetation index (WDRVI) were found to be accurate in estimating fraction of photosynthetically active radiation (PAR) absorbed by photosynthetically active green vegetation in maize and soybean with root mean squared error (RMSE) of 0.057, 0.067 and 0.069, respectively.

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

Agriculture faces the challenge of providing food, fibre and energy from limited land resources to satisfy the changing needs of a growing world population. Global megatrends, e.g., climate change, influence environmental production factors; production and consumption thus must be continuously adjusted to maintain the producer–consumer-equilibrium in the global food system. While, in some parts of the world, smallholder farming still is the dominant form of agricultural production, the use of digital information for the highly efficient cultivation of large areas has become part of agricultural practice in developed countries. Thereby, the use of satellite data to support site-specific management is a major trend. Although the most prominent use of satellite technology in farming still is navigation, Earth Observation is increasingly applied. Some operational services have been established, which provide farmers with decision-supporting spatial information. These services have mostly been boosted by the increased availability of multispectral imagery from NASA and ESA, such as the Landsat or Copernicus programs, respectively. Using multispectral data has arrived in the agricultural commodity chain. Compared to multispectral data, spectrally continuous narrow-band sampling, often referred to as hyperspectral sensing, can potentially provide additional information and/or increased sampling accuracy. However, due to the lack of hyperspectral satellite systems with high spatial resolution, these advantages mostly are not yet used in practical farming. This paper summarizes where hyperspectral data provide additional value and information in an agricultural context. It lists the variables of interest and highlights the contribution of hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectral missions.

Sun induced fluorescence at 760 nm (F-760) has shown to provide a valid approach to quantify gross primary production (GPP) at various scales, however the relationship between GPP and F-760 is influenced by the escape probability of fluorescence (Fesc), a variable which is not still fully understood. Combining radiative transfer modelling approaches, by means of the SCOPE model, and a data driven methodology based on variable selection methods we identify the predictors of Fesc, focusing on the effect of functional and structural traits. We show that Fesc is mainly predicted by structural variables such as fraction of grasses and near infrared reflectance. Building on the analysis of the predictor of Fesc, LUEp and LUEf we present a semi-empirical model formulation based only on optical data that significantly improves the GPP prediction.

The goal of this study is to quantify variability of daily light use efficiency (LUE) based on radiation absorbed by photosynthetically active vegetation (LUEgreen) in C3 and C4 crops. Data contained GPP, fAPAR, total and green leaf area index taken over 16 site years of irrigated and rainfed maize and 8 site years of soybean in 2001–2008 including years with quite strong drought events. LUEgreen in irrigated and rainfed sites were statistically indistinguishable showing low sensitivity to water availability. Seasonal changes of LUEgreen remained remarkably small over a wide range of water supply, leaf area index and weather conditions. The magnitude and composition of incident radiation affected the magnitude of the day-to-day LUEgreen change – increases in incident PAR caused statistically significant decreases of LUEgreen. Convergence of LUEgreen to a narrow range in irrigated and rainfed crops brought important implications for understanding mechanisms of plant response to stress and remote estimation of primary production in crops.