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[Display omitted] The precise estimation of the number of trees, their individual tree locations, along with species information, is crucial for enhancing ecosystem services in urban areas. Previous studies largely used satellite or airborne images for mapping trees, but they were insufficient to generate a large number of species distributions. Ground-level data also led to inaccurate positional information of individual trees, although they provided reliable species detection results. In this study, we propose a novel framework that fully explores the complementary strengths of air- and ground-level sensing platforms by leveraging various deep neural networks to generate a detailed tree maps at a city-wide scale. Our strategy includes individual tree mapping and tree species detection using three-color channel images acquired from multiple sensing platforms. Through publicly available airborne imagery, we estimate the presence of over 1.2 million trees in Suwon city of South Korea spanning 121.04 km2 (R2 of 0.95 and relative bias of −1.9 %), achieving more accurate individual tree positions (positional uncertainty around 2.0 m) than conventional methods. Our comprehensive experiments also demonstrate the effectiveness of utilizing tree bark photos and street-level imagery taken by citizens and vehicles to identify urban tree species, with accuracy rates of over 80 % for citizen-sensed tree species maps and 66 % for vehicle-sensed tree species map. Along with the proliferation of web-based airborne images, the widespread use of smartphones, and the advancements in vehicle-mounted sensors, this study can facilitate efficient and accurate management of urban trees across scales. For reproducibility of the study, we share the source code and datasets at https://github.com/landkwon94/ryoungseob-master. [ABSTRACT FROM AUTHOR]

The ecological mechanism underlying nocturnal stomatal conductance (gsn) in C3 and C4 plants remains elusive. In this study, we proposed a 'coordinated leaf trait' hypothesis to explain gsn in rice plants. We conducted an open‐field experiment by applying drought, nutrient stress and the combined drought–nutrient stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient stress. With the aforementioned multiple abiotic stressors considered as random effects, gsn exhibited a strong positive correlation with dark respiration (Rn). Notably, gsn primed early morning (5:00–7:00) photosynthesis through faster stomatal response time. This photosynthesis priming effect diminished after mid‐morning (9:00). Leaves were cooled by gsn‐derived transpiration. However, our results clearly suggest that evaporative cooling did not reduce dark respiration cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that dark respiration can explain considerable variation of gsn. Summary Statement: The nocturnal stomatal conductance in rice plants was not strongly affected by drought or macronutrient stress. Instead, it exhibited a strong positive correlation with dark respiration and played a role in priming early morning photosynthesis. [ABSTRACT FROM AUTHOR]

[Display omitted] Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earth's surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In order to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) approach combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.90, for the dual RSS-GAN maps vs in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from − 5.8 % to 0.3 % and − 12.4 % to − 3.7 % for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which reflects fine-scale information from Planet Fusion. Finally, the dual RSS-GAN maps showed both spatial enhancement and reducing the underestimation of vegetation index in historic Landsat dataset from 1984. This study presents a new approach for resolving sub-pixel spatial information in Landsat images. [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]

• Multi-objective optimization of rice production via Pareto fronts computed with NSGA-III. • Coupled with process model DNDC to calculate yield, water use, CH 4 and N 2 O emissions. • Increased rice yield is achievable with reduced irrigation and greenhouse gas emissions. Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective, such as increasing crop yield, reducing irrigation water consumption, or minimizing greenhouse gas (GHG) emissions, have proven to be insufficient. Simultaneously optimizing multiple competing rice farming objectives remains less explored. The present study aimed to increase current rice yield, reduce irrigation water consumption, and tackle the dilemma to reduce GHG (CH 4 and N 2 O) emissions at once. Using a heuristic and holistic method, we optimized farm management, focusing on irrigation regimes, sowing window, fertilization rate, tillage depth, and their interactions. We calibrated and validated the process-based DeNitrification–DeComposition (DNDC) model with 5 years of eddy covariance observations in a rice paddy site. The DNDC model was integrated with a Pareto-based non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. Results show that current farm management practices have failed to achieve the potential yield and imposed high environmental costs in the form of water use (604–810 mm/yr) and GHG emissions (CH 4 : 186–220 kg C/ha/yr; N 2 O: 0.3–1.6 kg C/ha/yr). By contrast, our new approach suggested that the optimized management could maintain or even increase current crop yield to its potential (∼10 t/ha), while reducing irrigation demand and GHG emissions by >50 %. Our results suggest that earlier sowing, combined with improved irrigation practices, may play a crucial role in maximizing crop yield and potentially providing environmental benefits. We found that the optimal proportion of non-flooded days was around 54 % of the growing season length, and the optimal stage for non-flooding days was the vegetative stage. Our approach could further be applied to evaluate the environmental sustainability of farming systems under various climate and local conditions, and to guide policymaking and farming practices. [Display omitted] [ABSTRACT FROM AUTHOR]

Ground reference data are an essential prerequisite for supervised crop mapping. The lack of a low-cost and efficient ground referencing method results in pervasively limited reference data and hinders crop classification. In this study, we apply a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google Street View (GSV) images in two distinct farming regions: Illinois and the Central Valley in California. We demonstrate the feasibility and reliability of our new ground referencing technique by performing pixel-based crop mapping at the state level using the cloud-based Google Earth Engine platform. The mapping results are evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From ~ 130,000 GSV images, the CNN model identified ~ 9,400 target crop images. These images are well classified into crop types, including alfalfa, almond, corn, cotton, grape, rice, soybean, and pistachio. The overall GSV image classification accuracy is 92% for the Central Valley and 97% for Illinois. Subsequently, we shifted the image geographical coordinates 2–3 times in a certain direction to produce 31,829 crop reference points: 17,358 in Illinois, and 14,471 in the Central Valley. Evaluation of the mapping results with CDL products revealed satisfactory coherence. GSV-derived mapping results capture the general pattern of crop type distributions for 2011–2019. The overall agreement between CDL products and our mapping results is indicated by R2 values of 0.44–0.99 for the Central Valley and 0.81–0.98 for Illinois. To show the applicational value of the proposed method in other countries, we further mapped rice paddy (2014–2018) in South Korea which yielded fairly well outcomes (R2 = 0.91). These results indicate that GSV images used with a deep learning model offer an efficient and cost-effective alternative method for ground referencing, in many regions of the world. [ABSTRACT FROM AUTHOR]

The maximum rate of carboxylation (Vcmax) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating Vcmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar‐induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant Vcmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in Vcmax has not been fully investigated. Here, we adapt an optimality‐based model to simulate daily Vcmax,25C (Vcmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal Vcmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant Vcmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m−2 s−1, which suggests that seasonal changes in Vcmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality‐based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next‐generation trait‐based terrestrial biosphere models would improve predictions of global photosynthesis. [ABSTRACT FROM AUTHOR]

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

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

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]

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

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]

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

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]

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

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

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

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]

Field measurements of vertical canopy structure have been challenging for decades and still important for understanding forest ecosystems. We measured the vertical canopy structure and its seasonal changes in a temperate deciduous forest canopy in Gwangneung, Korea. Time-series measurements of leaf area index (LAI) were collected in 2013 from a five story (4-m vertical intervals) tower. We evaluated crown depth and species composition by height from a vegetation survey. The vertical distribution of leaf and woody area density was described from measurements taken during the leaf-on and leaf-off seasons, and averaged 0.18 and 0.04 m m, respectively. Three strata were characterized: (1) the dense upper crowns with large trees (>16-m) of Quercus serrata, in which 29.3 % of the plant materials were distributed; (2) abundant foliage dominated by Carpinus laxiflora at about 16-m (40.8 %); and (3) a diverse and well-developed understory vegetation at about 4-m (15.5 %), consisting of C. laxiflora, Carnipus cordata, and Styrax japonica communities. Per-layer phenology of each species was successfully illustrated by the drastic increase in LAI during the leaf-out season [days of the year (DOY) 110-140], the full-leaved stage LAI of 3.4 ± 0.9 m m (mean ±1 standard deviation), and a decrease during the leaf-fall season (DOY 280-320). The seasonal variation in gap fractions reflected different light conditions varying with canopy height. This type of vertical profile archive is valuable not only for comparing the structure of various forests but also for monitoring changes in this ecosystem in the future. [ABSTRACT FROM AUTHOR]

A large number of East Asian cities, like cities in other parts of the world, are being affected by extreme heatwaves. Yet, little is known about the general urban design characteristics of sites with significant heat vulnerability within the localized context. In this study, empirical data sets were constructed describing the biomedical, social, environmental, and place-based parameters associated with the location of heat-related emergency calls in Suwon, South Korea, between 2010 and 2014. The results showed that the distribution of heat vulnerability appeared to be highly clustered within a few places, although the area did not have a high proportion of socioeconomically vulnerable populations. Here, three urban design characteristics were described in association with the location of emergency calls: (1) high-rise building complexes built on a superblock gridiron layout with large open spaces in between, (2) low-rise, high-density residential districts with a fine-scaled street layout, and (3) hilly terrain parkland sites adjacent to a densely urbanized district. These general characteristics of the sites and their potential links to heat vulnerability were explained in terms easily understandable by urban design and planning professionals so that climatic knowledge can be more effectively integrated with urban form-making practices. [ABSTRACT FROM PUBLISHER]

Summary Understanding basin-scale evapotranspiration (ET) is an important issue for the management of regional water resources, especially with the recent trend of intensified precipitation (P). This study assessed the spatial and temporal variations of regional ET in response to P extremes, for various types of land-cover across the Youngsan River basin in Korea. The spatial distribution of monthly P and ET from 2001 to 2009 were estimated using rainfall records from 40 weather stations located across the basin and a satellite-derived, process-based ET model Breathing Earth System Simulator (BESS) (Ryu et al., 2011), respectively. The study periods were focused on the recent years with abnormally large, small and normal P, which were identified from anomalies in the z-sores of long-term (1973–2011) rainfall records. The variation of regional ET was assessed in terms of: (1) the controlling factors, using the statistics of related meteorological and geographical data, (2) a water-energy balance, using Budyko’s framework, and (3) the water balance of four selected watersheds in the region, using the partitioning of annual P into ET and riverflow discharge (Q). The total annual ET of this region decreased in abnormally large-P year and increased in small-P year, because the ET in July to August (which accounts for more than 36% of annual ET) was limited by the available energy rather than available water due to the summer monsoon. In terms of land cover types, forests showed larger interannual variability in ET than paddy fields or cropland, with the differences in ET between large and small-P years being 108 and 82 mm yr −1 , respectively. The sensitivity of annual ET to P extremes was significantly related to the leaf area index (LAI), rather than soil properties, topography, or specific land-cover type ( p < 0.05, generalized linear model). However, the interannual variations of ET were not large (15–18%) compared to those of annual P (51–62%) and Q (108–232%) during 2002–2009. Thus, vegetation played a consistent role in releasing water back to the regional atmosphere through ET, regardless of P extremes. [ABSTRACT FROM AUTHOR]

• Generated CubeSats derived NIRvP maps at daily, 3 m resolution. • Matching NIRvP maps to flux tower footprint improved the NIRvP-GPP relationship. • The effects of matching the footprints were largest for wetland sites. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In this study, we used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). We then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, we found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution. [Display omitted] [ABSTRACT FROM AUTHOR]

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

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

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

Abstract: 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: 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]

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]

Abstract: Many studies on land surface radiation balances have relied on geostationary satellites. These satellites have provided data with high temporal resolution (less than 3h); however, the spatial resolution was too coarse (20–250km scale) to investigate local-scale land surface radiation balances. Moderate resolution imaging spectroradiometer (MODIS) – onboard both the Terra and Aqua satellites – yields a tradeoff with regard to this problem by providing higher spatial resolution (∼1km scale) and sensing all over the earth nearly twice a day during daytime; this provides a potential tool for the periodical monitoring of the land surface energy balance. The reliability of MODIS-derived estimates is, however, affected by the presence of multiple error sources, such as those related to heterogeneous land cover and complex topography. In this study, we have used atmospheric (5 and 10km scale) and land (1km scale) products obtained from both the Terra and Aqua MODIS devices as inputs in order to estimate the radiation components (1km scale) under clear daytime conditions over a heterogeneous farmland area and a rugged deciduous forest in the Korea Flux Network (KoFlux). The reliability of these estimates and the associated errors were evaluated by comparing against field measurements taken for 41 and 26 clear days with regard to the farmland and forest sites, respectively. Solar radiation was successfully retrieved with a root mean square error (RMSE) of ∼20Wm−2 for both the Terra and Aqua devices over the flat farmland site, whereas the rugged forest site exhibited corresponding values of 40 and 65Wm−2 RMSE values with consistent positive biases (presumably caused by topographic effects). The RMSE values of the downward longwave radiation were ∼20Wm−2 for both the Terra and Aqua devices for both these sites. The sensitivities of the upward components of the shortwave and longwave radiations varied with the RMSE values to the scale of the spatial heterogeneity of both the sites. Consequently, the RMSE values of the net radiation ranged from 33 to 61Wm−2 for both the devices at both the sites. Our results suggest that the scales of the patch mosaics within the landscapes need to be quantified for proper retrieval of the MODIS-derived radiation products. More extensive validation efforts are required to identify and account for major error sources across diverse land surface conditions. [Copyright &y& Elsevier]

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

Himawari-8, a new-generation geostationary satellite, can retrieve sub-hourly land surface temperatures (LSTs) with moderate spatial resolution, providing a new scale for monitoring the thermal environment in Asia and Oceania. This study evaluated uncertainties of LSTs retrieved by three operational algorithms from Advanced Himawari Imager (AHI) data. We compared two nonlinear split-window algorithms (SOB and WAN algorithms) and one nonlinear three-band algorithm (YAM algorithm). First, the error characteristics of the retrieved LSTs caused by the input parameter errors were simulated under various land-atmospheric conditions using an atmospheric radiative transfer model. Thereafter, retrieved LSTs from actual AHI data were evaluated using in-situ observations from AsiaFlux and OzFlux networks and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LSTs. The simulated results showed that the YAM algorithm maintained the highest accuracy, whereas the WAN algorithm had the highest robustness to input errors. The YAM algorithm had the smallest total error including input errors over a wide range of retrieval conditions. Validation of the three algorithms via in-situ LSTs from 12 sites revealed nighttime mean RMSEs for all sites of ∼1.7 °C, and daytime mean RMSEs for semi-arid and humid sites of approximately 3.0 °C and 2.0 °C, respectively. These are comparable to the accuracies reported for LST products with higher spatial resolutions, such as the Moderate Resolution Imaging Spectroradiometer and Landsat. Within the Himawari-8 disk, the estimation error of the YAM algorithm was ∼1.0 °C lower than those of the SOB and WAN algorithms in regions with extremely high viewing angle, temperature, and humidity (e.g., northern China, Australia, and Southeast Asia). Furthermore, AHI LSTs showed closer agreement with ECOSTRESS compared to in-situ LSTs, suggesting the usefulness of ECOSTRESS for assessing the diurnal LSTs derived from geostationary satellites. The resulting LST products and the knowledge of their error characteristics have the potential to improve the collective understanding of terrestrial energy and water cycles based on improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

• Image fusion NDVI products were evaluated against in-situ spectral data. • Considerable differences between fusion methods were observed • Biases in Landsat and MODIS were propagated to the fusion products. • Spatial interpolation process led to biases in heterogeneous land covers. • Image fusion products agreed better in time than in space. Satellite image fusion methods that improve spatial and temporal resolution have significant potential to advance understanding of ecosystem dynamics in space and time. However, systematic evaluations of image fusion methods against in situ spectral data are lacking. Here, we used a suite of in situ spectral data collected at 60 elementary sampling units (10 × 10 m) covering 15 Landsat pixel (30 × 30 m) plots and one Moderate Resolution Imaging Spectroradiometer (MODIS) pixel (250 × 250 m) throughout the entire growing season in a heterogeneous rice paddy landscape to evaluate four state-of-the-art image fusion NDVI products. They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. All fusion products showed strong linear relationships against in situ data when combining all spatial and temporal observations (R2: 0.73 to 0.93) although there were partly negative biases (–1% to –9%). These biases resulted from forcing data to image fusion algorithms, such as Landsat (–4%) and MODIS (–7%). Performance difference between fusion methods were considerably larger for spatial than for temporal variation. Furthermore, Landsat NDVI explained only 17–22% of spatial variation against in situ spectral data, which can be translated into weak performance of image fusion products to predict spatial variability in NDVI. Image fusion products that relied on spatial interpolation showed large biases (–15% to –30%) for a vegetation plot surrounded by mixed land cover plots. Our results highlight key sources of uncertainty and will be instrumental in improving satellite image fusion methods to monitor land surface phenology in space and time. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• Non-growing season ecosystem respiration negated the CO 2 sink during the growing season. • Carbon allocation to rice components varied substantially over the seasons. • The ratio of net to gross primary productivity varied seasonally and interannually. • Mid-season drainage substantially reduced CH 4 emission. • The proportion of ebullitive CH 4 flux was 10 to 17% of the total during the cultivation period. Rice is a major crop that feeds more than half of the global population. Rice paddies play a complex role in the carbon cycle by emitting CH 4 to the atmosphere while either sequestering or releasing CO 2 to the atmosphere. Here, we present 3.5 years of eddy covariance measurements of CO 2 and CH 4 fluxes over an intermittently irrigated, single crop rice paddy in South Korea in tandem with carbon stock measurements for leaves, stems, grains, and roots. The rice paddy acted as a slight sink or neutral in CO 2 (−47 ± 51 g C m −2 y −1; mean ± 95% confidence interval) with 941 ± 130 g C m −2 y −1 and 894 ± 108 g C m −2 y −1 as the gross primary production (GPP) and ecosystem respiration (Reco), respectively. The mean annual net primary productivity (NPP) was 666 ± 31 g C m −2 y −1, which was partitioned to leaves (9%), stems (30%), grains (51%), and roots (10%), for a harvest index of 57%. The paddy emitted 20.6 ± 1.5 g C m −2 y −1 in the form of CH 4 , of which between 82 and 96% was emitted during the cultivation period (~132 days). The CH 4 flux showed a mid-season decline caused by mid-season drainage, which produced aerobic soil conditions that inhibited CH 4 emissions. The proportion of estimated ebullitive flux to total CH 4 flux ranged from 10 to 17% during the cultivation period. GPP, Reco, and CH 4 fluxes varied by 12%, 11%, and 7%, respectively, over the 3-year period. When considering the harvest and the global warming potential of CH 4 , the paddy shifted from a greenhouse gas sink to a strong source (2,741 ± 468 g CO 2 eq m −2 y −1). These findings will improve our understanding of carbon budgets and cycles, and help develop greenhouse gas and water management strategies for rice paddies. [ABSTRACT FROM AUTHOR]

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

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

Rice is a major crop that feeds more than half of global population. Rice paddy playscomplex roles in carbon cycle by emitting CH4 to the atmosphere while CO2 is sequesteredfrom or released to the atmosphere. Here, we present 3.5 years of eddy covariancemeasurements of CH4 and CO2 fluxes over an intermittently-irrigated rice paddy in Korea intandem with carbon stock measurements in leaf, stem, grain, and root. The interannualvariation of CO2 fluxes was little although the timings of transplantation and harvest weredifferent for each year. Rice paddy acted as a slight sink or neutral in CO2 (-47.3 ± 50.9 g Cm−2 y−1; mean ± 95% confidence interval) with 942.2 ± 129.4 g C m−2 y−1 and894.9 ± 106.5 g C m−2 y−1 for gross primary production (GPP) and ecosystemrespiration (Reco), respectively. Carbon stock in rice showed large interannualvariation (760.2 ± 183.7 g C m−2) with 70 ± 19.8 g C m−2, 232.1 ± 83.3 g Cm−2, 378.2 ± 75.5 g C m−2, and 79.8 ± 12.8 g C m−2 for leaf, stem, grain, androot, respectively. The rice paddy emitted 21.7 ± 2.5 g C m−2 y−1 of CH4. Whenconsidering the global warming potential of CH4 (197.3 g CO2-Ceq m−2 y−1),the paddy shift from carbon sink to source (150 g CO2-Ceq m−2 y−1). The CH4flux showed a bimodal seasonal pattern caused by the mid-season drainage whichturned the soils aerobic conditions so prohibited CH4 emissions. The irrigationpractices, which are different every year, resulted in the significant interannualvariation of CH4 emission (± 7%). These findings will be useful to understand carbonbudgets and cycles, and develop carbon and water managements in rice paddy. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has recently become an important topic in large scale photosynthesis research. This is mainly related to the selectivity of SIF for vegetation on the one hand and the close mechanistic relationships to photosynthesis on the other hand. The latter, however, is already nonlinear at the leaf level and further complicated by the canopy scattering of far-red SIF. This scattering process, which is strongly affected by the structure of the canopy, results in only a fraction of SIF photons emitted at the leaf-level to escape the canopy. The challenge for canopy-level SIF research is therefore to disentangle the contributions related to canopy structure and leaf physiology. While it is widely assumed that the latter has the stronger relationship to photosynthesis, this has not been tested experimentally at the canopy scale so far. We used a recently developed approach to estimate the canopy escape probability of far-red SIF to analyze the contributions of canopy structure and leaf physiology separately. For this, we conducted a combined analysis for three in-situ datasets from a rice paddy, a wheat and a corn field that all had fPAR, PAR, far-red SIF, GPP and canopy reflectance observations covering most of the growing season. Our analysis focused on half-hourly observations both at the level of fluxes involving the absorbed radiation as well as at the level of light use efficiencies, which excludes the radiation component.We found that canopy structure effects dominated the seasonal component of the SIF escape probability and that this was the cause of a positive correlation of the latter to photosynthetic light use efficiency. The estimated total SIF yield of all leaves in the canopy, in contrast, was dominated by diurnal variations and was almost entirely uncorrelated to photosynthetic light use efficiency. Furthermore, our findings confirm the strong relationships between NIRv to both SIF and GPP at short time scales and the site level that were previously only reported at large spatial and long temporal scales. Thus, our findings bridge gaps between spatio-temoral scales and challenge the widely held assumption that the leaf physiology component in SIF provides most of the additional information for photosynthesis estimation that goes beyond APAR. We think that our approach has the potential to unify previous findings into a coherent picture that could enable the community to more easily identify the advantages and limitations of different methods of estimating GPP from remotely sensed observations. [ABSTRACT FROM AUTHOR]

The emerging technique of remotely sensed Sun-Induced chlorophyll Fluorescence (SIF) hasadvanced our ability to estimate plant photosynthetic activity. However, continuousobservations of SIF and Gross Primary Productivity (GPP) at the canopy scale inevergreen needle leaf forests have been lacking so far. Here, we report continuouslyobserved far-red canopy SIF, GPP and Absorbed Photosynthetically Active Radiation(APAR) during the senescence period in an evergreen needle leaf forest. We installed ahigh-spectral-resolution spectrometer combined with a rotating prism system, whichmeasures up- and down welling irradiance with a single fiber, 20 m above the canopy. Weinvestigated the relationships between SIF, APAR and GPP separately for the followingthree periods; 1) before and 2) after understory vegetation leaf fall, 3) after thetemperature drops below zero. In addition, we examined the effects of diffuse light on therelationships. We found that the slope of the half-hourly SIF-APAR relationshipwas distinctly different in each period but this was not the case in the APAR-GPPand SIF-GPP relationships: while the APAR-GPP relationship showed a similarslope difference as APAR-SIF in the third period, the other two periods had a moresimilar slope value; the SIF-GPP relationship, in contrast had similar slope values inthe last two periods but a very different value in the first period. In addition, thehalf-hourly SIF-APAR relationship showed the highest correlation over the threeperiods (R2 = 0.83). On the other hand, regardless of sky condition, we confirmedthat for daily mean values, SIF-GPP relationship (cloudy: R2 = 0.83, clear: R2 =0.76) was stronger than the SIF-APAR relationship (cloudy: R2 = 0.66, clear: R2 =0.69). Overall, our results indicate that relationships between canopy SIF, GPP andAPAR can vary depending on the presence of understory vegetation, temperatureand the sky condition in an evergreen needle leaf forest. These findings will helpimprove canopy-level SIF-based GPP estimation across multiple temporal scales. [ABSTRACT FROM AUTHOR]

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

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

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]

• A machine learning method (NN SIF) is developed to partition NEE using SIF. • NN SIF effectively reduces structural errors of standard partitioning methods. • NN SIF better partitions NEE under high temperature and water stress. • NN SIF unravels physiological controls on ecosystem–scale GPP–SIF relationships. Accurately partitioning net ecosystem exchange (NEE) into ecosystem respiration (ER) and gross primary productivity (GPP) is critical for understanding the terrestrial carbon cycle. The standard partitioning methods rely on simplified empirical models, which have inherent structural errors. These structural errors lead to biased GPP and ER estimation, especially during extreme events (e.g., drought) and human disturbances (e.g., crop harvest). Recently, solar-induced chlorophyll fluorescence (SIF) has been shown to be well correlated to GPP, thus offering a path to improve the NEE partitioning by constraining GPP. However, the ecosystem-scale relationship between GPP and SIF remains limited. Here, we show that neural networks informed by SIF observations (NN SIF) can be successfully used to partition NEE, while simultaneously learning the ecosystem-scale GPP-SIF relationship. NN SIF was compared against standard partitioning methods and NN without SIF constraint (NN noSIF), using field data from different ecosystems and synthetic data generated by a coupled fluorescence-photosynthesis model (SCOPE). NN SIF showed superior performance as: (1) it effectively improves the ER estimation, especially at high temperature, (2) it better captures the moisture limitation on ER, (3) it more accurately estimates LUE variations to stress, and (4) it uniquely captures the rapid GPP drop after land management (harvest). Furthermore, NN SIF can retrieve the GPP-SIF relationship at the ecosystem scale, and elucidate how this relationship responds to environmental conditions. Overall, our algorithm provides the first direct and non-empirical estimate of the ecosystem-scale GPP-SIF relationship, without relying on any prior empirical assumptions on the relationships between CO 2 fluxes, climatic drivers, and SIF. The new knowledge learned by NN SIF can help better estimate global-scale GPP using satellite SIF, especially during extreme events and in the presence of land management. [ABSTRACT FROM AUTHOR]

Abstract: Most land surface and ecosystem models assume that a vegetated canopy can be abstracted as a turbid medium when such models compute mass, energy, and carbon exchange. However, those models fail to simulate radiation environments in heterogeneous landscapes. This study aims to couple a spatially explicit three-dimensional (3D) shortwave and longwave radiative transfer model with a soil and canopy energy balance and canopy physiology model (CANOAK-FLiES), to investigate how well the 3D model performs in a heterogeneous landscape compared to a 1D model. The canopy structural parameters were extracted using airborne-based Light Detection and Ranging (LiDAR) data and digital cover photographs. The developed model was compared with a wide variety of field and remote sensing data (e.g., hyper-spectral remote sensing images, overstory and understory radiation, and tower-based energy and CO2 fluxes) in a woody oak savanna in CA, USA. Overall, the simulated spatial and diurnal patterns of the radiation environment were consistent with the measurements of shortwave and longwave wavebands. The 3D approach worked better than the 1D approach from the wet mild spring to the dry hot summer while the relative importance of using the 3D approach depends on climate and canopy physiological conditions as well as the canopy structure. With low leaf area index in the oak woodland, the woody elements absorbed 12%–39% of the PAR and 20%–52% of the NIR radiation. Consequently, 12% of the daytime energy flux was used as woody heat storage at our study site. This novel 3D model has the potential to serve as a useful tool for analyzing the spatio-temporal variability of radiation and energy fluxes in evaluating footprints of radiation sensors and eddy covariance fluxes, and serve as a standard in evaluating the performance of a hierarchy of simpler land surface models to compute mass and energy exchange of heterogeneous landscapes. [Copyright &y& Elsevier]

Summary: Global vegetation and land‐surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco‐evolutionary optimality (EEO) principles can provide novel, parameter‐sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf‐level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change. [ABSTRACT FROM AUTHOR]

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

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]