Research papers

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Φ_PSIImax), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R² = 0.86, RMSE=1.99 μmol m⁻² s⁻¹) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. The seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Φ_PSIImax. These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in Φ_PSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

The ecological mechanism underlying nocturnal stomatal conductance (g_sn) in C₃ and C₄ plants remains elusive. In this study, we proposed a ‘coordinated leaf trait’ hypothesis to explain g_sn in rice plants. We conducted an open-field experiment by applying drought, nutrient stress and the combined drought–nutrient stress. We found that g_sn was neither strongly reduced by drought nor consistently increased by nutrient stress. With the aforementioned multiple abiotic stressors considered as random effects, g_sn exhibited a strong positive correlation with dark respiration (R_n). Notably, g_sn 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 g_sn-derived transpiration. However, our results clearly suggest that evaporative cooling did not reduce dark respiration cost. Our results indicate that g_sn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that dark respiration can explain considerable variation of g_sn.

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.

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.

Life on Earth depends on the conversion of solar energy to chemical energy by plants through photosynthesis. A fundamental challenge in optimizing photosynthesis is to adjust leaf angles to efficiently use the intercepted sunlight under the constraints of heat stress, water loss and competition. Despite the importance of leaf angle, until recently, we have lacked data and frameworks to describe and predict leaf angle dynamics and their impacts on leaves to the globe. We review the role of leaf angle in studies of ecophysiology, ecosystem ecology and earth system science, and highlight the essential yet understudied role of leaf angle as an ecological strategy to regulate plant carbon–water–energy nexus and to bridge leaf, canopy and earth system processes. Using two models, we show that leaf angle variations have significant impacts on not only canopy-scale photosynthesis, energy balance and water use efficiency but also light competition within the forest canopy. New techniques to measure leaf angles are emerging, opening opportunities to understand the rarely-measured intraspecific, interspecific, seasonal and interannual variations of leaf angles and their implications to plant biology and earth system science. We conclude by proposing three directions for future research.

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.

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.

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.

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 (R² = 0.76) and APAR and GPP (R² = 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 R² = 0.83 and R² = 0.88, respectively. Relationships between LUE_p (=GPP/APAR) and LUE_f (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R² = 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.