Reply on RC2

<p>Sun-induced chlorophyll fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, current studies have generally been limited by the spatial resolution of datasets with a sufficiently long archive. For example, while available since 2007, the commonly used GOME-2 SIF data has a spatial resolution in the order of 0.5° (~50km), too coarse to effectively separate the competing effects of different types of vegetation from the overall ecosystem dynamics, or to draw general conclusions relevant to the land cover of a region. While finer SIF retrievals are becoming available, such as from the TROPOMI instrument on-board of the Sentinal-5P platform, several years will be needed before their archives reach a sufficient temporal depth.</p><p> </p><p>Using GOME-2 SIF retrievals, downscaled to a resolution of 0.05° (~5km) via a proven methodology [1], comparisons are made with the data-driven FLUXCOM GPP dataset and divergences and convergences explored to see where high-resolution SIF can enhance our understanding of GPP. This includes an exploration of the spatial and temporal relationships between estimates of GPP and SIF at a global scale. The high resolution of the SIF data allows the relationships to be broken down by plant functional type (PFT) for separate climate zones, thus enabling a confrontation between FLUXCOM GPP and SIF at fine granularity and eventually a future integration of SIF in the estimation of data-driven GPP products.</p><p> </p><p>Whilst a linear relationship is generally observed between SIF and GPP in all vegetation categories, areas of non-linearity suggest where SIF could potentially provide more information about ecosystem dynamics that are not represented in the GPP dataset. For example some vegetation types experience saturation in the seasonal GPP measurements (likely driven by the saturation of the fraction of absorbed PAR), that are not emerging from the SIF signal. In addition, in highly productive ecosystems like tropical rainforests, a wide range of spatio-temporal variation in SIF is observed, while only a considerably smaller variability is reproduced in the modelled GPP. Further studies are conducted on how SIF and GPP behave differently in anomalies of air temperature and soil moisture. Overall, the study suggests there is room to improve global land-climate models by incorporating information from SIF.</p><p> </p><p> </p><p>[1] Duveiller, G., Filipponi, F., Walther, S., Köhler, P., Frankenberg, C., Guanter, L., and Cescatti, A.: A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity, Earth Syst. Sci. Data Discuss., , in review, 2019.</p>

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising optical indicator of Gross Primary Production (GPP) in terrestrial ecosystems. However, the interpretation of SIF as a proxy of GPP is challenged when plants experience abiotic stress, particularly during extreme climatic events whose frequency is projected to increase in the future. Recently, the feasibility of canopy-scale active chlorophyll fluorescence measurements (LED-induced chlorophyll fluorescence), which directly measure the apparent fluorescence yield (FyieldLIF), has provided new perspectives on detecting the physiological responses of plants to abiotic stress. This study was conducted during summer 2022 European heat waves in a mixed temperate deciduous broadleaf forest, located in the Fontainebleau-Barbeau station (Integrated Carbon Observation System FR-Fon site), about 50 km South-East of Paris, France. Continuous measurements of carbon dioxide (CO2) and energy exchanges, SIF, FyieldLIF, and ancillary environmental variables were acquired. We investigated how atmospheric dryness, measured as Vapor Pressure Deficit (VPD), affected canopy chlorophyll fluorescence (both SIF and FyieldLIF) and GPP, as well as their relationships. Our results indicated that high VPD has a negative impact on GPP and FyieldLIF at both half-hourly and daily scales. In contrast, SIF exhibits a positive response to high VPD at the half-hourly scale, but this relationship reverses, showing a negative response at the daily scale. At the half-hourly scale, our results revealed a decrease of the correlation between SIF and GPP (R² decreased from 0.49 to 0.17) as atmospheric dryness increased. In contrast, the correlation between FyieldLIF and GPP strengthened significantly under the same conditions (R² increased from 0.07 to 0.43). However, at the daily scale, the correlations between SIF and GPP and between FyieldLIF and GPP showed an overall increase, suggesting a time-scale-dependent response of these relationships to atmospheric dryness. This study also highlighted the advantages of FyieldLIF over SIF in detecting plant responses to high atmospheric dryness. This underlines the potential of canopy-level active chlorophyll fluorescence measurements for understanding and quantifying the nature of the relationship between canopy chlorophyll fluorescence and photosynthesis in ecosystems under extreme climatic conditions.

Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy

Abstract. Global terrestrial ecosystems control the atmospheric CO2 concentration through gross primary production (GPP) and ecosystem respiration processes. Chlorophyll fluorescence is one of the energy release pathways of excess incident light in the photosynthetic process. Over the last 10 years, extensive studies have revealed that canopy-scale Sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf-level photosynthesis to global GPP, can be observed from satellites. SIF is used to infer photosynthetic capacity of plant canopy; however, it is not clear how the leaf-level SIF emission contributes to the top-of-canopy directional SIF. Plant canopy radiative transfer models are useful tools to understand the mechanism of anisotropic light interactions such as scattering and absorption in plant canopies. One-dimensional (1-D) plane-parallel layer models (e.g., the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model) have been widely used and are useful to understand the general mechanisms behind the temporal and seasonal variations in SIF. However, a 1-D model does not explain the complexity of the actual canopy structures. Three-dimensional models (3-D) have a potential to delineate the realistic directional canopy SIFs. Forest Light Environmental Simulator for SIF (FLiES-SIF) version 1.0 is a 3-D Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind the SIF emission from complex forest canopies. The FLiES-SIF model is coupled with leaf-level fluorescence and a physiology module so that users are able to simulate how the changes in environmental and leaf traits as well as canopy structure affect the observed SIF at the top of the canopy. The FLiES-SIF model was designed as three-dimensional model, yet the entire modules are computationally efficient: FLiES-SIF can be easily run by moderate-level personal computers with lower memory demands and public software. In this model description paper, we focused on the model formulation and simulation schemes, and showed some sensitivity analysis against several major variables such as view angle and leaf area index (LAI). The simulation results show that SIF increases with LAI then saturated at LAI>2–4 depending on the spectral wavelength. The sensitivity analysis also shows that simulated SIF radiation may decrease with LAI at a higher LAI domain (LAI>5). These phenomena are seen in certain Sun and view angle conditions. This type of nonlinear and nonmonotonic SIF behavior towards LAI is also related to spatial forest structure patterns. FLiES-SIF version 1.0 can be used to quantify the canopy SIF in various view angles including the contribution of multiple scattering which is the important component in the near-infrared domain. The potential use of the model is to standardize the satellite SIF by correcting the bidirectional effect. This step will contribute to the improvement of the GPP estimation accuracy through SIF.

Accurate characterization of gross primary productivity (GPP) is critically important in assessing mangrove carbon budgets, but the current knowledge of the temporal variations of GPP in evergreen mangroves is very limited. Remote sensing of sun‐induced chlorophyll fluorescence (SIF) has emerged as a promising approach to approximating GPP across ecosystems, but its capability for tracking GPP in evergreen mangroves has not been assessed. The SIF‐GPP link at a subtropical mangrove and its environmental controls are explored using 1‐year time‐series measurements from tower‐based hyperspectral and eddy covariance systems. Both the relationship between SIF and GPP as well as that between SIFy (SIF yield: the ratio of SIF over absorbed photosynthetically active radiation [APAR]) and LUE (light use efficiency: the ratio of GPP over APAR) at diurnal and seasonal time scales are analyzed. The temporal variations of SIF and GPP shared overall similar changing patterns, but their functional relationship tended to be time scale‐dependent. Midday depressions in SIF were observed when environmental stresses occurred around noon (including excess light and high VPD), and the strength of the SIF‐GPP link was affected by changing environmental conditions. The SIFy‐LUE relationship was temporally more dynamic, tending to match during midday hours but diverge from each other during morning and afternoon hours. These findings confirm SIF can serve as a potential remotely sensed indicator of mangrove canopy photosynthesis. This paper provides the first, high temporal‐resolution, continuous SIF measurements in mangroves, and highlights the importance of the impacts of environmental conditions on the SIF‐GPP relationship.

<p>Accurate estimation of terrestrial gross primary productivity is essential for the development of credible future carbon cycle and climate simulations. Current remote sensing techniques allow retrieval of sun-induced chlorophyll fluorescence (SIF) as a valid proxy for GPP, but low resolution, sparse coverage, or resolution mismatches between the different satellite sensors hinder our ability to effectively link SIF to many environmental variables at fine scales. In order to better characterize heterogeneous landscapes, several attempts to downscale SIF products to higher resolutions have been made. We investigate the ability of the downscaled GOME-2 product developed by Duveiller et al. (2019), to capture the differences in spatiotemporal dynamics over the Greater Alpine Space. We analyse SIF in connection to land cover and elevation, and calculate land phenology metrics based on seasonal SIF time series. Ground-based GPP validation suggests biome-specific SIF-GPP relationships, but the comparison was hindered by the resolution mismatch of the data. Moreover, missing data are present at high elevations, diminishing the suitability of current SIF products to analyse cloud-prone mountainous areas. Important insights into spatial patterns and seasonal trends could be inferred at forest and other large-area land cover types, typical of mid elevations in the Alps, but many anthropogenic habitats at low elevations, as well as high elevation grasslands and other small-scale heterogeneous environments could not be thoroughly investigated and are likely to be underrepresented or prone to biases. Similar downscaling procedures might be applied at finer scales to e.g. TROPOMI products, or alternative advanced remote sensing SIF techniques and instruments might be needed in order to enable detailed and systematic evaluations of the Alpine region or similar highly heterogenous landscapes, before a process-oriented monitoring and unbiased implementation into climate models may be performed.</p>

Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America

To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (p < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development.

Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance

<strong class="journal-contentHeaderColor">Abstract.</strong> Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO<span class="inline-formula">₂</span> fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (<span class="inline-formula"><i>r</i>&gt;0.84</span>), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of <span class="inline-formula"><i>r</i>=0.49</span>) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop<span class="inline-formula">+</span>SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop<span class="inline-formula">+</span>SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO<span class="inline-formula">₂</span> fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO<span class="inline-formula">₂</span> fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO<span class="inline-formula">₂</span> fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop<span class="inline-formula">+</span>SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIFnadir) on GPP, compared to total SIF emitted by all leaves (SIFtotal) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIFtotal is better correlated to GPP than raw SIF observations (SIFobs). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIFobs, SIFnadir and SIFtotal) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIFobs, SIFnadir and SIFtotal in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. The results indicate that diurnal SIFnadir has stronger correlations to GPP than SIFobs for field measurements, with the increase of mean coefficient of determination (R2) by 0.05–0.07 for far-red band and 0.11–0.20 for red band. SIFnadir shows comparable performance with SIFtotal for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIFnadir and SIFobs, the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIFnadir and SIFtotal in estimating GPP. TROPOMI-based analysis further confirms that SIFnadir overall has higher correlations to AmeriFlux GPP than SIFobs for different plant functional types and shows similar performance with SIFtotal. Compared to SIFtotal, the estimation of SIFnadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.

Solar-induced chlorophyll fluorescence (SIF) has been recognized as a proxy of gross primary production (GPP) across various terrestrial biomes. However, the effects of low temperature on SIF and GPP among different plant function types (PFTs) have not yet been well-explored. To gain a better understanding of the relationship between SIF and GPP, we investigated the variation in the GPP/SIF ratio in response to low-temperature conditions using satellite and tower-based datasets. Based on the TROPOMI SIF product and FLUXCOM GPP data, we found that the SIF and GPP exhibited consistent seasonal and spatial patterns, while the GPP/SIF ratio differed for different PFTs. The GPP/SIF ratio for forest types was generally higher than 10 gC·d−1·mw−1·nm·sr, whereas the GPP/SIF ratio for grass and crop types was generally lower than 10 gC·d−1·mw−1·nm·sr. In addition, there were noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples that experienced low-temperature stress (below 10 °C, defined as group A) and those that grew under relatively warm conditions (above 10 °C throughout the year, defined as group B). The GPP/SIF ratio for group A generally exhibited a “hump-shaped” seasonal pattern, and that for group B showed a slightly “bowl-shaped” seasonal pattern, which means it is important to consider the effects of temperature on the SIF-GPP relationship. Through linear regression and correlation analysis, we demonstrate that there was a positive correlation between the GPP/SIF ratio and temperature for group A, with a wide temperature range including low-temperature conditions, indicating that, in this case, temperature affected the SIF–GPP relationship; however, for group B—with a temperature higher than 10 °C throughout the year—the GPP/SIF ratio was not consistently affected by temperature. The response of GPP/SIF to low temperature stress was confirmed by tower-based observations at a C3 cropland (C3CRO) site and a boreal evergreen needleleaf forest (BoENF) site. Although the relationship between the GPP/SIF ratio and temperature differed among PFTs, the GPP/SIF ratio decreased under low-temperature conditions for PFTs. Therefore, the GPP/SIF ratio was not constant and was largely influenced by low temperature for different PFTs, thus highlighting the importance of incorporating temperature into SIF-based GPP estimation.

Interpretable tropical cyclone intensity estimation using Dvorak-inspired machine learning techniques

The development of comprehension of 50 sentences expressing comparative, passive, temporal, spatial, and familial relationships was evaluated in 210 grade school children. Significant increases in correct responses occurred during the first five grades but not between Grades 5 and 8. The periods during which significant increases occurred differed for sentence categories. In the early grades comparative relationships were easiest followed by passive, temporal, spatial, and familial relationships. The findings suggest that spatial relationships are established earlier in a developmental sequence than temporal relationships. In Grades 1 and 2 WISC Full Scale IQ and comprehension of logico-grammatical sentences correlated significantly.