Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Hydrogen Sulfide Alleviates Cadmium Stress by Enhancing Photosynthetic Efficiency and Regulating Sugar Metabolism in Wheat Seedlings

Hydrogen sulfide (H₂S) plays prominent multifunctional roles in the mediation of various physiological processes and stress responses to plants. In this study, hydroponic experiments were carried out to explore the effects of NaHS pretreatment on the growth of wheat (Triticum aestivum L.) under 50 μM cadmium (Cd). Compared with Cd treatment alone, 50 μM NaHS pretreatment increased the plant height, soluble sugar content of shoots and roots, and dry weight of shoots and roots under Cd stress, while the Cd concentration of shoots and roots was significantly reduced by 18.1% and 25.9%, respectively. Meanwhile, NaHS pretreatment protected the photosynthetic apparatus by increasing the net photosynthetic rate and PSII electron transportation rate of wheat leaves under Cd stress. NaHS pretreatment significantly increased the soluble sugar content to maintain the osmotic pressure balance of the leaf cells. The gene expression results associated with photosynthetic carbon assimilation and sucrose synthesis in wheat leaves suggested that the NaHS pretreatment significantly up-regulated the expression of TaRBCL, TaRBCS, and TaPRK, while it down-regulated the expression of TaFBA, TaSuSy, TaSAInv, and TaA/NInv. In summary, NaHS pretreatment improved the resistance of wheat seedlings under Cd stress by increasing the rate of photosynthesis and regulating the expression of genes related to sugar metabolism.

Satellite greenness and solar-induced chlorophyll fluorescence reveal reverse desertification in Gurbantunggut Desert

The desertification reversal is a process of revegetation and natural restoration in fragile dryland areas due to human activities and climate change mediation. Understanding the impact of desertification reversion on terrestrial ecosystems, including vegetation greenness and photosynthetic capacity, is crucial for land policy-making and carbon-cycle model improvement. However, the phenomenon of desertification reversal is rarely mentioned in previous studies, which dramatically limits the understanding of vegetation dynamics in the arid area. Therefore, it is of great necessity to investigate the status of desertification reversal on the ecosystem in arid areas. In this study, we first reported the phenomenon of desertification reversion over the southern edge of the Gurbantunggut Desert through the Moderate-resolution Imaging Spectroradiometer classification map year by year. We discussed the consequences, ways, and causes of desertification reversion. Our results showed that the desertification reversal significantly increased vegetation greenness and photosynthetic capacity, which largely offset the negative impact of desertification on the ecosystem productivity; cropland expansion and grassland's natural restoration were the two main ways of desertification reversal; the improvement of soil-water condition was an essential environmental factor leading to the phenomenon of reverse desertification. This finding highlights the importance of desertification reversal in the carbon cycle of dryland ecosystems and prove that desertification reversal is an integral part of global and dryland vegetation greening.

Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light

Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.

Large spatial variation and stagnation of cropland gross primary production increases the challenges of sustainable grain production and food security in China

Sustainable crop grain production and food security is a grand societal challenge. Substantial investments in China's agriculture have been made in the past decades, but our knowledge on cropland gross primary production in China remains limited. Here we analyzed gross primary production (GPP), solar-induced chlorophyll fluorescence (SIF), terrestrial water storage, crop grain production, and agricultural investment and policy during 2000-2018. We found that based on croplands in 2000, approximately 52 × 10⁶ ha (~37%) had continuous increasing trends in GPP during 2000-2018, which were mainly located in northern China. GPP for 63% of croplands was stagnant, declined, or had no significant change. At the national scale, annual cropland GPP increased during 2000-2008 but became stagnant in 2009-2018, which was inconsistent with the interannual trend in the crop grain production data for 2009-2018. The spatial mismatch between crop production and water availability became worse. The major grain exporting provinces, mostly located in water-stressed regions, experienced increased water resource constraints, which posed a challenge for sustainable grain production. The stagnant cropland GPP and increasing water resource constraints highlight the urgent need for sustainable management for crop production and food security in China.

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.

Fires Drive Long-Term Environmental Degradation in the Amazon Basin

The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km2 burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km2 burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin.

Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

Evaluating Multi-Angle Photochemical Reflectance Index and Solar-Induced Fluorescence for the Estimation of Gross Primary Production in Maize

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance > 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.

Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity

Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA0)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA0), and air temperature. The machine learning model performed well at predicting SIF, giving R2 values of 0.73, an RMSE of 0.30 mW m−2 nm−1 sr−1 and a bias of 0.22 mW m−2 nm−1 sr−1 for 2018. If cos (SZA0) was not included, the accuracy of the RF model decreased: the R2 value was then 0.65, the RMSE 0.34 mW m−2 nm−1 sr−1 and an bias of 0.26 mW m−2 nm−1 sr−1, further verifying the importance of cos (SZA0). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R2 value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°.

Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.

Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V_cmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V_cmax is time-consuming and laborious; therefore, the spatiotemporal distribution of V_cmax is still poorly understood due to limited measurements of V_cmax. In this study, we used a data assimilation approach to map the spatial variation of V_cmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V_cmax map, the mean V_cmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived V_cmax dataset by Kattge et al. (2009). The gradient of V_cmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V_cmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V_cmax values, is highlighted in this V_cmax map. Legume plants are characterized with high V_cmax values. This V_cmax map also clearly illustrates the emerging soybean revolution in South America where V_cmax is the highest among the world. The gradient of V_cmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V_cmax, for global climate change studies.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

Solar-induced fluorescence (SIF) has been empirically linked to gross primary productivity (GPP) in multiple ecosystems and is thus a promising tool to address the current uncertainties in carbon fluxes at ecosystem to continental scales. However, studies utilizing satellite-measured SIF in South America have concentrated on the Amazonian tropical forest, while SIF in other regions and vegetation classes remain uninvestigated. We examined three years of Orbiting Carbon Observatory-2 (OCO-2) SIF data for vegetation classes within and across the six Brazilian biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal) to answer the following: (1) how does satellite-measured SIF differ? (2) What is the relationship (strength and direction) of satellite-measured SIF with canopy temperature (Tcan), air temperature (Tair), and vapor pressure deficit (VPD)? (3) How does the phenology of satellite-measured SIF (duration and amplitude of seasonal integrated SIF) compare? Our analysis shows that OCO-2 captures a significantly higher mean SIF with lower variability in the Amazon and lower mean SIF with higher variability in the Caatinga compared to other biomes. OCO-2 also distinguishes the mean SIF of vegetation types within biomes, showing that evergreen broadleaf (EBF) mean SIF is significantly higher than other vegetation classes (deciduous broadleaf (DBF), grassland (GRA), savannas (SAV), and woody savannas (WSAV)) in all biomes. We show that the strengths and directions of correlations of OCO-2 mean SIF to Tcan, Tair, and VPD largely cluster by biome: negative in the Caatinga and Cerrado, positive in the Pampa, and no correlations were found in the Pantanal, while results were mixed for the Amazon and Atlantic Forest. We found mean SIF most strongly correlated with VPD in most vegetation classes in most biomes, followed by Tcan. Seasonality from time series analysis reveals that OCO-2 SIF measurements capture important differences in the seasonal timing of SIF for different classes, details masked when only examining mean SIF differences. We found that OCO-2 captured the highest base integrated SIF and lowest seasonal pulse integrated SIF in the Amazon for all vegetation classes, indicating continuous photosynthetic activity in the Amazon exceeds other biomes, but with small seasonal increases. Surprisingly, Pantanal EBF SIF had the highest total integrated SIF of all classes in all biomes due to a large seasonal pulse. Additionally, the length of seasons only accounts for about 30% of variability in total integrated SIF; thus, integrated SIF is likely captures differences in photosynthetic activity separate from structural differences. Our results show that satellite measurements of SIF can distinguish important functioning and phenological differences in vegetation classes and thus has the potential to improve our understanding of productivity and seasonality in the tropics.

Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands

Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( ε / S I F yield ), the one-leaf light-use efficiency (SL-LUE) model ( ε max ), and the two-leaf LUE (TL-LUE) model ( ε msu and ε msh ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the ε / S I F yield and ε max were similarly robust and considerably more stable than ε msu and ε msh for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( C V ) of ε / S I F yield was relatively small (15.0%) and was obviously lower than ε max ( C V = 27.0%), ε msu ( C V = 43.2%), and ε msh ( C V = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types.

Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence

Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007–2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.

Relationship between fluorescence yield and photochemical yield under water stress and intermediate light conditions

The dynamics between fluorescence (Fs) yield and photochemical (P) yield in a changing environment are essential for understanding the relationship between photosynthesis and fluorescence. The ratio of Fs yield and P yield tends to be constant under high light intensity, but the relationship between these two yields, and its response to environmental conditions, need to be explored further under intermediate and low light. In this study, we performed leaf-scale measurements of fluorescence parameters by pulse-amplitude modulation (PAM) technology in summer maize (Zea mays L.) plants grown under intermediate light conditions in a climate chamber. Plants were treated as moderately water stressed and non-water stressed. Results showed that a decrease in P yield was accompanied by increases in Fs yield and non-photochemical quenching (NPQ) yield in response to moderate water stress under intermediate and low light conditions. Fs yield was negatively correlated with P yield under intermediate and low light conditions when there was sufficient soil water in the root zone. Under water stress, the correlation between Fs yield and P yield was negative in low light, but became positive under higher light levels. Fs yield was negatively related to P yield when NPQ yield was low; however, they were synergistically and positively associated when excessive light dissipation was dominated by NPQ.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014

Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPP_VPM) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPP_VPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPP_VPM is also significantly positive correlated with GOME-2 SIF (R² > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPP_VPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPP_VPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPP_VPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models.

Spatiotemporal variation characteristics of green space ecosystem service value at urban fringes: A case study on Ganjingzi District in Dalian, China

In this study, a green space classification system for urban fringes was established based on multisource land use data from Ganjingzi District, China (2000-2015). The purpose of this study was to explore the spatiotemporal variation of green space landscapes and ecosystem service values (ESV). During 2006-2015, as urbanization advanced rapidly, the green space area decreased significantly (359.57 to 213.46 km²), the ESV decreased from 397.42 to 124.93 million yuan, and the dynamic degrees of ESV variation were always <0. The green space large plaque index and class area both declined and the number of plaques and plaque density increased, indicating green space landscape fragmentation. The dynamic degrees of ESV variation in western and northern regions (with relatively intensive green space distributions) were higher than in the east. The ESV for closed forestland and sparse woodland had the highest functional values of ecological regulation and support, whereas dry land and irrigated cropland provided the highest functional values of production supply. The findings of this study are expected to provide support for better construction practices in Dalian and for the improvement of the ecological environment.

Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains

Extremely high temperatures represent one of the most severe abiotic stresses limiting crop productivity. However, understanding crop responses to heat stress is still limited considering the increases in both the frequency and severity of heat wave events under climate change. This limited understanding is partly due to the lack of studies or tools for the timely and accurate monitoring of crop responses to extreme heat over broad spatial scales. In this work, we use novel spaceborne data of sun-induced chlorophyll fluorescence (SIF), which is a new proxy for photosynthetic activity, along with traditional vegetation indices (Normalized Difference Vegetation Index NDVI and Enhanced Vegetation Index EVI) to investigate the impacts of heat stress on winter wheat in northwestern India, one of the world's major wheat production areas. In 2010, an abrupt rise in temperature that began in March adversely affected the productivity of wheat and caused yield losses of 6% compared to previous year. The yield predicted by satellite observations of SIF decreased by approximately 13.9%, compared to the 1.2% and 0.4% changes in NDVI and EVI, respectively. During early stage of this heat wave event in early March 2010, the SIF observations showed a significant reduction and earlier response, while NDVI and EVI showed no changes and could not capture the heat stress until late March. The spatial patterns of SIF anomalies closely tracked the temporal evolution of the heat stress over the study area. Furthermore, our results show that SIF can provide large-scale, physiology-related wheat stress response as indicated by the larger reduction in fluorescence yield (SIF_yield ) than fraction of photosynthetically active radiation during the grain-filling phase, which may have eventually led to the reduction in wheat yield in 2010. This study implies that satellite observations of SIF have great potential to detect heat stress conditions in wheat in a timely manner and assess their impacts on wheat yields at large scales.

Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat

Precise detection of leaf nitrogen concentration (LNC) is helpful for nutrient diagnosis and fertilization guidance in farm crops. Numerous researchers have estimated LNC with techniques based on reflectance spectra or active chlorophyll fluorescence, which have limitations of low accuracy or small scale in the field. Given the correlation between chlorophyll and nitrogen contents, the response of sun-induced chlorophyll fluorescence (SIF) to chlorophyll (Chl) content reported in a few papers suggests the feasibility of quantifying LNC using SIF. Few studies have investigated the difference and power of the upward and downward SIF components on monitoring LNC in winter wheat. We conducted two field experiments to evaluate the capacity of SIF to monitor the LNC of winter wheat during the entire growth season and compare the differences of the upward and downward SIF for LNC detection. A FluoWat leaf clip coupled with a ASD spectrometer was used to measure the upward and downward SIF under sunlight. It was found that three (↓FY687, ↑FY687/↑FY739, and ↓FY687/↓FY739) out of the six SIF yield (FY) indices examined were significantly correlated to the LNC (R2 = 0.6, 0.51, 0.75, respectively). The downward SIF yield indices exhibited better performance than the upward FY indices in monitoring the LNC with the ↓FY687/↓FY739 being the best FY index. Moreover, the LNC models based on the three SIF yield indices are insensitive to the chlorophyll content and the leaf mass per area (LMA). These findings suggest the downward SIF should not be neglected for monitoring crop LNC at the leaf scale, although it is more difficult to measure with current instruments. The downward SIF could play an increasingly important role in understanding of the SIF emission for LNC detection at different scales. These results could provide a solid foundation for elucidating the mechanism of SIF for LNC estimation at the canopy scale.

Translating High-Throughput Phenotyping into Genetic Gain

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

Photosynthetic Characteristics and Chloroplast Ultrastructure of Summer Maize Response to Different Nitrogen Supplies

Maize (Zea mays L.) is the important crop over the world. Nitrogen (N) as necessary element affects photosynthetic characteristics and grain yield of summer maize. In this study, N0 (0 kg N ha^-1), N1 (129 kg N ha^-1), N2 (185 kg N ha^-1), and N3 (300 kg N ha^-1) was conducted using hybrid 'ZhengDan958' at Dawenkou research field (36°11'N, 117°06'E, 178 m altitude) in the North China Plain to explore the effects of N rate on photosynthetic characteristics and chloroplast ultrastructure. Gas exchange parameters, chlorophyll fluorescence parameters, leaf area index (LAI), chlorophyll SPAD value, chloroplast ultrastructure, dry matter weight and grain yield were measured. At physiological maturity stage, dry matter weight and grain yield of N2 increased by 33-52% (P ≤ 0.05) and 6-32% (P ≤ 0.05), respectively, compared with other treatments. During the growing from silking (R1) to milk (R3) stage, LAI of N0 and N1 were 35-38% (P ≤ 0.05) and 9-23% (P ≤ 0.05) less than that of N2, respectively. Chlorophyll SPAD value of N0 and N1 were 13-22% (P ≤ 0.05) and 5-11% (P ≤ 0.05) lower than that of N2. There was no significant difference in LAI and chlorophyll SPAD value between N2 and N3 during the period from R1 to R3 (P > 0.05). The net photosynthetic rate (P_n), maximal quantum efficiency of PSII (F_v/F_m) and quantum efficiency of PSII (Φ_PSII) were higher with the increase of N rate up to N2 (P ≤ 0.05), and those of N3 were significantly less than N2 (P ≤ 0.05). In compared with N2, the chloroplast configuration of N0 and N1 became elliptical, almost circular or irregular. The membrane of chloroplast and thylakoid resolved with growing stage, and the number of chloroplast per cell and lamellae per grana decreased under N0 and N1 treatment (P ≤ 0.05). Under N0 and N1 treatments, summer maize had more negative photosynthetic characteristics. The more number of osmium granule and vesicle and the larger gap between lamellae were shown in N3. Therefore, N2 treatment, 185 kg N ha^-1, is the appropriate application rate for grain yield, photosynthesis and chloroplast ultrastructure.

Temporal consistency between gross primary production and solar-induced chlorophyll fluorescence in the ten most populous megacity areas over years

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPP_vpm) estimates for the world’s ten most populous megacities during 2000–2014. The seasonal dynamics of GPP_vpm during 2007–2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5° × 0.5° resolution. Annual GPP_vpm during 2000–2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.