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We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications. Key Points: Reflected near‐infrared radiation is a strong proxy for canopy photosynthesisThis proxy works for a wide range of ecosystem structure and functionThe method has promise to be used for a variety of applications relating to carbon and water use [ABSTRACT FROM AUTHOR]

California is expected to experience great spatial/temporal variations evaporation. These variations arise from strong north‐south, east‐west gradients in rainfall and vegetation, strong interannual variability in rainfall (±30%) and strong seasonal variability in the supply and demand for moisture. We used the Breathing Earth System Simulator to evaluate the rates and sums of evaporation across California, over the 2001–2017 period. Breathing Earth System Simulator is a bottom‐up, biophysical model that couples subroutines that calculate the surface energy balance, photosynthesis, and stomatal conductance. The model is forced with high‐resolution remote sensing data (1 km).The questions we address are as follows: How much water is evaporated across the natural and managed ecosystems of California? How much does evaporation vary during the booms and busts in annual rainfall? and Is evaporation increasing with time due to a warming climate? Mean annual evaporation, averaged over the 2001–2017 period, was relatively steady (393 ± 21 mm/year) given the high interannual variation in precipitation (519 ± 140 mm/year). No significant trend in evaporation at the statewide level was detected over this time period, despite a background of a warming climate. Irrigated agricultural crops and orchards, at 1‐km scale, use less water than inferred estimates for individual fields. This leaves the potential for sharing water, a scarce resource, more equitably among competing stakeholders, for example, farms, fish, people, and ecosystems. Plain Language Summary: Many stakeholders are contending for the limited water budget that is available to California, the world's fifth largest economy. Yet the amount of water used by natural and managed ecosystems across the state is not well known. We produced a new, process‐oriented estimate of statewide water use by natural and managed ecosystems using a biophysical model forced with satellite remote sensing. Despite the booms and busts in rainfall over the 2001 to 2017 period, we find that statewide water use is conservative, compared to the annual variability in rainfall. Nor do we detect that statewide evaporation is increasing as the climate has warmed over this period. We find that crops use less water than conventional wisdom because a subset of fields across a 1‐km pixel are fallow and are at peak leaf area and maximum evaporation potential for a relatively short period. Forests, on the other hand, use more water than conventional wisdom because they have a long growing season and absorb more energy than crops. Our intent is to provide water managers with new information on water use to better share water among the various stakeholders, for example, agricultural, cities, fish, ground water reservoirs, and water quality. Key Points: We produced new granular information (1‐km resolution) on evaporation across California using the biophysical model, Breathing Earth System SimulatorAnnual variations in evaporation were highly damped (393 mm/year +/− 5%) relative to the booms and busts in rainfall between 2001 and 2017 (+/− 28%)Statewide evaporation is not increasing with global warming, over 17 years of inspection, because feedbacks dampen the response to a warming climate [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]

Graphical abstract Highlights • Developed a system that monitors VIs, fPAR, and LAI automatically. • Displayed linear responses to light intensities in the lab and rice field. • Agreed to a reference spectrometer and LAI-2200. • Tracked green LAI by integrating LED and micro-camera. Abstract Continuous monitoring of vegetation indices (VIs) the fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) through satellite remote sensing has advanced our understanding of biosphere–atmosphere interactions. Substantial efforts have been put into monitoring individual variables in the field, but options to concurrently monitor VIs, fPAR, and LAI in-situ have been lacking. In this paper, we present the Smart Surface Sensing System (4S), which automatically collects, transfers and processes VIs, fPAR and LAI data streams. The 4S consists of a microcomputer, controller and camera, a multi-spectral spectrometer built in with a light-emitting diode (LED) and an internet connection. Lab testing and field observations in a rice paddy site that experiences wet summer monsoon seasons confirmed the linear response of 4S to light intensities in the blue, green, red and near-infrared spectral channels, with wide ranging temperatures and humidity having only a minor impact on 4S throughout the growing season. Applied over an entire rice growing season (day of year [DOY] 120 - 248), VIs and fPAR from 4S were linearly related to corresponding VIs from a reference spectrometer (R2 = 0.98; NDVI, R2 = 0.96; EVI) and the LAI-2200 instrument (R2 = 0.76), respectively. Integration of gap fraction-based LAI from LED sensors and a green index from the micro-camera allowed tracking of the seasonality of green LAI. The continuous and diverse nature of 4S observations highlights its potential for evaluating satellite remote sensing products. We believe that 4S will be useful for the expansion of ecological sensing networks across multiple spatial and temporal scales. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015-2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of -0.7±1.2 PgC in 2015, but a slight positive anomaly of 0.05±0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of -1.1±0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of -0.22±0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33±0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most.We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies. This article is part of a discussion meeting issue 'The impact of the 2015/ 2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'. [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]

Conventional process-based crop simulation models and agro-land surface models require numerous forcing variables and input parameters. The regional application of these crop simulation models is complicated by factors concerning input data requirements and parameter uncertainty. In addition, the empirical remotely sensed regional scale crop yield estimation method does not enable growth process modeling. In this study, we developed a process-based rice yield estimation model by integrating an assimilate allocation module into the satellite remote sensing-derived and biophysical process-based Breathing Earth System Simulator (BESS). Normalized accumulated gross primary productivity ( G P P n o r m - a c c u ) was used as a scaler for growth development, and the relationships between G P P n o r m - a c c u and dry matter partitioning coefficients were determined from the eddy covariance and biometric measurements at the Cheorwon Rice paddy KoFlux site. Over 95% of the variation in the dry matter allocation coefficients of rice grain could be explained by G P P n o r m - a c c u . The dynamics of dry matter distribution among different rice components were simulated, and the annual grain yields were estimated. BESS-Rice simulated GPP and dry matter partitioning dynamics, and rice yields were evaluated against in-situ measurements at three paddy rice sites registered in KoFlux. The results showed that BESS-Rice performed well in terms of rice productivity estimation, with average root mean square error (RMSE) value of 2.2 g C m −2  d −1 (29.5%) and bias of –0.5 g C m −2  d −1 (–7.1%) for daily GPP, and an average RMSE value of 534.8 kg ha −1 (7.7%) and bias of 242.1 kg ha −1 (3.5%) for the annual yield, respectively. BESS-Rice is much simpler than conventional crop models and this helps to reduce the uncertainty related to the forcing variables and input parameters and can result in improved regional yield estimation. The process-based mechanism of BESS-Rice also enables an agronomic diagnosis to be made and the potential impacts of climate change on rice productivity to be investigated. [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]

Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products. [ABSTRACT FROM AUTHOR]

Parks account for a large proportion of green spaces in urban regions, but most previous studies have focused on the values of recreational services in urban parks-carbon uptake by plants in urban parks has been studied less extensively. Urban parks typically form complex landscapes in space and time by integrating multiple species with open canopies. Thus, to better understand canopy photosynthesis in urban park, measuring spatial and temporal variations in photosynthetic parameters and canopy structural variables is essential. Here, we report seasonal and spatial variations in two key photosynthetic parameters ( V cmax and J max which are the maximum rates of carboxylation and electron transport, respectively) and leaf area index (LAI) in Seoul Forest Park. During the peak growing season, we found an eightfold difference (20 to 149 μmol m −2 s −1 ) and fourfold difference (38 to 141 μmol m −2 s −1 ) in V cmax and J max , respectively, across 10 species. Over the seasons, two woody species ( Zelkova serrata (Thunb.) Makino and Prunus yedoensis Matsum ), respectively showed three- to fivefold differences in V cmax and two- to fivefold differences in J max . We evaluated whether leaf nitrogen contents could predict V cmax and J max , and found significant correlations among the three variables during the peak growing season across 10 species, but no significant correlations among them over the seasons in the two woody species. LAI computed using in- situ observations and satellite remote-sensing imagery showed a high spatial heterogeneity during the growing season. These results highlight the considerable spatial and temporal variability in photosynthetic parameters and LAI, which implies that individual tree-based three-dimensional canopy modeling will be essential for accurate estimation of canopy photosynthesis in urban parks. [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: 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]

Interannual variability in biosphere-atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the 'acid test' for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study. [ABSTRACT FROM AUTHOR]

Abstract: The regular monitoring of evapotranspiration from satellites has been limited because of discontinuous temporal coverage, resulting in snapshots at a particular point in space and time. We developed a temporal upscaling scheme using satellite-derived instantaneous estimates of evapotranspiration to produce a daily-sum evapotranspiration averaged over an 8-day interval. We tested this scheme against measured evapotranspiration data from 34 eddy covariance flux towers covering seven plant functional types from boreal to tropical climatic zones. We found that the ratio of a half-hourly-sum of potential solar radiation (extraterrestrial solar irradiance on a plane parallel to the Earth''s surface) between 10:00hh and 14:00hh to a daily-sum of potential solar radiation provides a robust scaling factor to convert a half-hourly measured evapotranspiration to an estimate of a daily-sum; the estimated and measured daily sum evapotranspiration showed strong linear relation (r 2 =0.92) and small bias (−2.7%). By comparison, assuming a constant evaporative fraction (the ratio of evapotranspiration to available energy) during the daytime, although commonly used for temporal upscaling, caused 13% underestimation of evapotranspiration on an annual scale. The proposed temporal upscaling scheme requires only latitude, longitude and time as input. Thus it will be useful for developing continuous evapotranspiration estimates in space and time, which will improve continuous monitoring of hydrological cycle from local to global scales. [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]

Interannual variability in biosphere-atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study., Organismic and Evolutionary Biology

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]