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Summary: To what degree plant ecosystems thermoregulate their canopy temperature (Tc) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability.With global datasets of Tc, air temperature (Ta), and other environmental and biotic variables from FLUXNET and satellites, we tested the 'limited homeothermy' hypothesis (indicated by Tc & Ta regression slope < 1 or Tc < Ta around midday) across global extratropics, including temporal and spatial dimensions.Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 or Tc > Ta around midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, their Tc–Ta difference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site‐mean ΔT exhibits larger variations than the slope indicator, suggesting ΔT is a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial‐wide ΔT variation (0–6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%).These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the 'limited homeothermy' hypothesis, but their thermoregulation still occurs, implying that slope < 1 or Tc < Ta are not necessary conditions for plant thermoregulation. See also the Commentary on this article by Drake, 238: 921–923. [ABSTRACT FROM AUTHOR]

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]

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]

With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions become increasingly critical for water management and sustainability. However, estimating ET from satellites for precise water resource management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote-sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presents a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water–carbon–energy coupled biophysical model, BESS (Breathing Earth System Simulator), with a generic and fully automated fusion algorithm, STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30 m multispectral surface reflectance by fusing Landsat and MODIS satellite data to derive a fine-resolution leaf area index and visible/near-infrared albedo, all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET. We applied BESS-STAIR from 2000 through 2017 in six areas across the US Corn Belt and validated BESS-STAIR ET estimations using flux-tower measurements over 12 sites (85 site years). Results showed that BESS-STAIR daily ET achieved an overall R2=0.75 , with root mean square error RMSE =0.93 mm d -1 and relative error RE =27.9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations captured the spatial patterns, seasonal cycles, and interannual dynamics well in different sub-regions. The high performance of the BESS-STAIR framework primarily resulted from (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30 m data from the STAIR fusion algorithm, and (3) BESS's applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resource management and precision agriculture applications for the US Corn Belt and even worldwide given the global coverage of its input data. [ABSTRACT FROM AUTHOR]

Probabilistic modelling of gaps for light–canopy interactions has long served as a theoretical basis to estimate vegetation structural parameters—leaf area index (LAI) and leaf angle distribution (LAD)—from optical measurements such as hemispherical photos. Direct inversion of such probabilistic models provides a reliable statistical algorithm for parameter estimation, but this inferential paradigm has been seldom explored. Even worse, many classical LAI algorithms implicitly assume "wrong" statistical models inconsistent with the underlying probabilistic gap models—a subtle issue not articulated before but known to cause practical issues.Here, we clarified how to improve LAI and LAD estimation by directly inverting binary gap/non‐gap data of hemispherical photos via binary nonlinear regression (BNR). We implemented the new BNR method and some classical algorithms in an R package "hemiphoto2LAI", comprising a total of 135 models for LAI estimation.Compared to classical algorithms, BNR features many theoretical advantages and allows estimating LAI and LAD simultaneously. BNR can address questions difficult to answer by classical algorithms (e.g. how better is one LAD than another?). We demonstrated the utility of the BNR paradigm based on both synthetic and real data.Overall, BNR is statistically more justifiable but its potential has been under‐appreciated. We encourage the community to embrace this new paradigm for reliable analyses of hemispherical photos or other gap data for canopy research. [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]

Understanding the long-term performance of global satellite leaf area index ( LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products ( GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products ( GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre- MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS> LAI3g> TCDR> GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS> LAI3g> TCDR> GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products. [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]

Clumping index quantifies the level of foliage aggregation, relative to a random distribution, and is a key structural parameter of plant canopies and is widely used in ecological and meteorological models. In this study, the inter- and intra-annual variations in clumping index values, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product, are investigated at six forest sites, including conifer forests, a mixed deciduous forest and an oak-savanna system. We find that the clumping index displays large seasonal variation, particularly for the deciduous sites, with the magnitude in clumping index values at each site comparable on an intra-annual basis, and the seasonality of clumping index well captured after noise removal. For broadleaved and mixed forest sites, minimum clumping index values are usually found during the season when leaf area index is at its maximum. The magnitude of MODIS clumping index is validated by ground data collected from 17 sites. Validation shows that the MODIS clumping index can explain 75% of variance in measured values (bias = 0.03 and rmse = 0.08), although with a narrower amplitude in variation. This study suggests that the MODIS BRDF product has the potential to produce good seasonal trajectories of clumping index values, but with an improved estimation of background reflectance. [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]

[Display omitted] • Edge effects on SOC stocks were measured in urban and rural forest patches. • SOC stocks in the rural forest edges were 80% lower than in the forest interiors. • Biotic factors explained SOC stocks better in the rural forest than urban forests. • Anthropogenic drivers led to heterogeneous edge effects on urban SOC stocks. Landscape fragmentation has created large areas of forest edge. Understanding how soil organic carbon (SOC) stocks within forest edges respond to fragmentation is essential to assess carbon budgets; however, the causes and magnitude of edge effects on SOC stocks have been poorly characterized. Here, we quantify the edge effects on SOC stocks within and across urban to rural forest patches from three fragmented urban forests to a large patch of rural forest. The SOC stocks within 20 m of the rural forest edge (1.86 kgC m−2) is on average 80% lower than the interiors of rural forest (10.47 kgC m−2). We found that biotic factors, including annual litterfall mass (R 2 > 0.94), peak leaf area index (R 2 > 0.92), and fine-root mass density (R 2 > 0.77), explained the spatial variation in SOC stocks within the rural forest. In urban forests, human activities at forest edges led to contrasting edge effects on SOC stocks, for instance, the SOC stocks at the east edges (4.74 kgC m−2) were 56% greater than at the west edges (3.03 kgC m−2) explained by the adjacent land uses (e.g., paved roads vs. non-paved soils) and in-situ litterfall management. We also found significant differences in summer soil temperature (Δ T S > 2.8 °C) and soil moisture (ΔVWC > 0.05 m3 m−3) between the east and west forest edges. Our results reveal that the factors responsible for the edge effects on SOC stocks in rural forests are biotic factors, while heterogeneous human activities at the local scale lead to complex edge effects on urban forest SOC stocks. [ABSTRACT FROM AUTHOR]

Abstract: Correct estimates of gap fraction are essential for quantifying canopy architectural variables, such as leaf area and clumping indices, which modify land–atmosphere interactions. However, gap fraction measurements from optical sensors are contaminated by radiation that is scattered by plant elements and ground surfaces. In this study, we propose a simple one-dimensional, invertible, bidirectional transmission model to remove scattering effects from gap fraction measurements. To evaluate how well the proposed model computes scattered radiance under a variety of ecosystem conditions, we compared simulated scattered radiance by the proposed model to a more sophisticated three-dimensional model in four ecosystem types (oak-grass savanna, birch, pine, and spruce stands). The simple model showed good agreement with the three-dimensional model in the scattering factor (scattered radiation from leaves normalized by sky radiation), except for highly reflective stems such as birch. The simple model showed that the scattering factor is highest when the leaf area index (LAI) is low (1–2m2 m−2) in a non-clumped canopy, potential errors in estimating the LAI increase with an increase in LAI, and bright land surfaces (e.g., snow and bright soil) and bright stems (e.g., birch) can contribute significantly to scattering effects. By applying the simple model with LAI-2200 data collected in an oak-grass savanna woodland, we found that the scattering factor causes significant underestimation of the LAI (up to 26% for sunny conditions, 7.7% for diffuse sky conditions) and significant overestimation of the apparent clumping index (up to 14% for sunny conditions, 4.3% for diffuse sky conditions). The LAI is underestimated because of the effect of scattered radiation on gap fraction estimates, which cause overestimation of the clumping index. Even under highly diffuse sky conditions, errors in LAI estimates due to scattering effects are not always negligible (up to 7.7% underestimation). The proposed inversion scheme provides an opportunity to quantify gap fractions, LAI, and apparent clumping index even under sunny conditions. [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]

Abstract: We developed and tested the use of light emitting diodes (LEDs) to monitor vegetation reflectance in narrow spectral bands as a tool suitable for quantifying and monitoring ecosystem structure, function and metabolism. LEDs are appealing because they are inexpensive, small and reliable light sources that, when used in reverse mode, can measure spectrally selective radiation. We selected LEDs in red and near-infrared wavebands as they are used to calculate the normalized difference vegetation index (NDVI). The lab experiments revealed that the LEDs showed linear relation with a hyper-spectral spectroradiometer (R 2 >0.94 and 0.99 for red and NIR, respectively) and marginal sensitivity to temperature. To test the efficacy of this novel sensor, we measured spectral reflectance with LEDs over an annual grassland in California over 3.5 years. The LED-sensor captured daily to interannual variation of the spectral reflectance at the two bands with reliable and stable performance. The spectral reflectance in the two bands and NDVI proved to be useful to identify the leaf-on and leaf-off dates (mean bias errors of 5.3 and 4.2 days, respectively) and to estimate canopy photosynthesis (R 2 =0.91). We suggest that this novel instrument can monitor other structural and functional (e.g. leaf area index, leaf nitrogen) variables by employing LEDs that have other specific wavelengths bands. Considering that off-the-shelf LEDs cover a wide range of wavebands from the ultraviolet to near-infrared regions, we believe that the research community could explore a range of similar instruments across a range of bands for a variety of ecological applications. [ABSTRACT FROM AUTHOR]

Abstract: Effective leaf area index is routinely quantified with optical instruments that measure gap fraction through the probability of beam penetration of sunlight through the vegetation. However, there have been few efforts to obtain theoretically consistent effective leaf area indices from those measurements. To apply the Beer–Lambert law, multiple gap fraction measurements may be averaged in two ways: (1) by taking the mean of the logarithms of the individual gap fraction values or (2) by taking the logarithm of the mean gap fraction. Based on a theoretical model and gap fraction measurements from 41 sites, we report that effective leaf area index must be quantified using the second approach. The first approach implemented in the LAI-2000 instrument considers clumping effects at scales larger than shoots. Thus, the combination of the first approach with an independent clumping index overestimates leaf area index up to 30% at the investigated sites. Clumping effects accounted for by the LAI-2000 instrument, called the “apparent” clumping index, were dependent on canopy cover, crown shape, and canopy height. A forest gap fraction model showed that short canopy height, vertically prolonged crown shape and higher canopy cover are associated with the lowest apparent clumping indices. We show that the apparent clumping index is a useful quantity to constrain the true clumping index and to investigate spatial and temporal variation of clumping effects. Such information would be useful to evaluate a coarse global clumping index map and improve land surface models. [Copyright &y& Elsevier]

Abstract: Savannas are spatially heterogeneous, open ecosystems, thus efforts to quantify canopy structure with methods developed for homogeneous, closed canopies are prone to failure. We examine the applicability of two direct (litterfall, allometry) and five indirect (LAI-2000, TRAC, digital hemispheric photography, digital cover photography, traversing radiometer system) methods to determine leaf area index across a 9ha domain in an oak-savanna ecosystem in California, USA. Interpretation of the leaf area index measurements is supported by two gap-fraction models. We recommend that leaf inclination angle distribution should be characterized first. For this purpose, we propose a simple, reliable and reproducible method using a digital camera. We show that the combination of digital cover photography and LAI-2000 could provide spatially representative leaf area index, gap fraction and element clumping index. Based on these two indirect methods, we quantify spatially representative element clumping index and leaf area index at ecosystem scale as 0.49±0.10 (mean±95% confidence interval) and 0.77±0.27, respectively. In contrast to previous studies in northern ecosystems, measurement and modeling results suggest that element clumping index decreases with view zenith angle, most likely due to apparent changes of tree distribution pattern with the view zenith angle. Our results highlight the importance of ecosystem-scale clumping effects for the adequate quantification of tree leaf area index in savannas. Finally, we suggest a protocol to quantify leaf area index and its associated canopy structure variables in open canopy ecosystems. [Copyright &y& Elsevier]

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]

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]

Although the relevance of ongoing global greening for climate change has been recognized, its impact on the surface energy partitioning is yet to be quantified. Changes in vegetation structure and density are expected to influence the redistribution of heat and moisture across the climate system ultimately affecting water and energy cycles. Here, we analyse satellite leaf area index (LAI) and surface energy fluxes at the global scale for the period 1982¬–2016, and explore how variations in LAI have affected the energy partitioning. We show that latent fluxes (LE) increase and sensible fluxes (H) decrease with an increase in LAI, with sensitivities of 4.46±0.04 and -4.10±0.05 Wm-2 per unit change of LAI, respectively. These sensitivities show a remarkable increase of about 50% after 2000, possibly due to a more active stomatal control and an increased transpiration/evaporation ratio, triggered by decreasing soil moisture and/or increasing atmospheric evaporative demand. The long-term positive trend in LAI led to a decline in Bowen ratio (B=H/LE) prominently in arid zones (-0.031±0.002 decade-1) attributable to the increased evaporative surface and the consequent enhancement of transpiration. Overall, annual surface energy partitioning appears more strongly influenced by LAI (itself driven by climate and biotic factors) than directly by climate drivers. Land surface models in general tend to overestimate the vegetation control over tree biomes compared to observation-based records, due to the poor representation of LAI variability. Improving the model representation of vegetation dynamics and related biophysical processes is therefore crucial in view of the projected increases in LAI and aridity. [ABSTRACT FROM AUTHOR]