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 (SIFyield) 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.
While the response of crops to extreme high temperatures has been well documented at the site scale, the understanding is still limited over broad spatial scales due to lack of studies or tools timely and accurately monitor crop responses to extreme heat. In this work, we use the novel spaceborne data of sun‐induced chlorophyll fluorescence (SIF) to investigate the impacts of heat stress on winter wheat. An earlier and more pronounced response comparing to traditional vegetation indices was found suggesting the SIF have strong potentials to detect heat stress conditions in wheat in a timely manner.
The Orbiting Carbon Observatory-2 (OCO-2), launched in July 2014, is capable of measuring Solar-Induced chlorophyll Fluorescence (SIF), a functional proxy for terrestrial gross primary productivity ...(GPP). Although its primary mission is to measure the column-averaged mixing ratio of CO2 (Xco2) to constrain global carbon source/sink distribution, one of the OCO-2 spectrometers allows for a robust SIF retrieval solely based on solar Fraunhofer lines. Here we present a technical overview of the OCO-2 SIF product, aiming to provide the scientific community guidance on best practices for data analysis, interpretation, and application. This overview consists of the retrieval algorithms, OCO-2 specific bias correction, retrieval uncertainty evaluation, cross-mission comparison with other existing SIF products, and a global-scale examination of the SIF-GPP relationship. With the initial three years of data (September 2014 onward), we compared OCO-2 SIF with retrievals from Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and examined its relationship with FLUXCOM and MODIS GPP datasets. Our results show that OCO-2 SIF, along with GOSAT products, closely resemble the mean spatial and temporal patterns of FLUXCOM GPP from regions to the globe. Compared with GOME-2, however, OCO-2 depicts a more realistic spatial contrast between the tropics and extra-tropics. The linear relationship between OCO-2 SIF and existing modeled GPP products diverges somewhat across biomes at the global scale, consistent with previous GOSAT or GOME-2 based findings when modeled GPP products were used, but in contrast to a consistent cross-biome SIF-GPP relationship obtained at flux tower sites with OCO-2 products. This contrast suggests a critical need to reconcile differences in diverse SIF and GPP products and the relationships among them. Overall, the OCO-2 SIF products are robust and valuable for monitoring the global terrestrial carbon cycle and for constraining the carbon source/sink strengths of the Earth system. Finally, insights are offered for future satellite missions optimized for SIF retrievals.
•We present a technical overview of the OCO-2 SIF product and evaluate its fidelity.•The retrieval precision of OCO-2 is considerably improved over existing products.•The SIF-GPP relationship diverges across biomes if modeled GPP products are used.
Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In ...this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, Vcmax at a reference temperature of 25 °C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, Vcmax, in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007–2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to Vcmax at the seasonal scale (R2 = 0.70 for C4 and R2 = 0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking Vcmax with SIF that was used to estimate spatially and temporally varying crop Vcmax for the US Corn Belt region. The resulting Vcmax maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite-based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal Vcmax from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional Vcmax using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.
•Far-red SIF shows strong link to Vcmax at the seasonal scale than VIs.•Spatially-explicit maps of Vcmax from SIF were developed for crops.•The resulting Vcmax maps improve the regional GPP modeling.
Mid‐to‐high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still ...challenging. In particular, the applicability of existing satellite‐based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun‐induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME‐2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high‐latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6 weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start‐of‐season of approximately 1 month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2–3 °C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated in situ observations of CO2 fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space‐based SIF measurements to light‐use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.
Quantifying ecosystem carbon fluxes and stocks is essential for better understanding the global carbon cycle and improving projections of the carbon-climate feedbacks. Remote sensing has played a ...vital role in this endeavor during the last five decades by quantifying carbon fluxes and stocks. The availability of satellite observations of the land surface since the 1970s, particularly the early 1980s, has made it feasible to quantify ecosystem carbon fluxes and stocks at regional to global scales. Here we provide a review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present. First, we present an overview of the terrestrial carbon cycle and remote sensing of carbon fluxes and stocks. Remote sensing data acquired in a broad wavelength range (visible, infrared, and microwave) of the electromagnetic spectrum have been used to estimate carbon fluxes and/or stocks. Second, we provide a historical overview of the key milestones in remote sensing of the terrestrial carbon cycle. Third, we review the platforms/sensors, methods, findings, and challenges in remote sensing of carbon fluxes. The remote sensing data and techniques used to quantify carbon fluxes include vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence (SIF), land surface temperature, and atmospheric inversions. Fourth, we review the platforms/sensors, methods, findings, and challenges in passive optical, microwave, and lidar remote sensing of biomass carbon stocks as well as remote sensing of soil organic carbon. Fifth, we review the progresses in remote sensing of disturbance impacts on the carbon cycle. Sixth, we also discuss the uncertainty and validation of the resulting carbon flux and stock estimates. Finally, we offer a forward-looking perspective and insights for future research and directions in remote sensing of the terrestrial carbon cycle. Remote sensing is anticipated to play an increasingly important role in carbon cycling studies in the future. This comprehensive and insightful review on 50 years of remote sensing of the terrestrial carbon cycle is timely and valuable and can benefit scientists in various research communities (e.g., carbon cycle, remote sensing, climate change, ecology) and inform ecosystem and carbon management, carbon-climate projections, and climate policymaking.
•We review 50 years of history and advances in remote sensing of C fluxes and stocks•We present an overview of terrestrial C cycle, remote sensing, and key milestones•We review remote sensing platforms/sensors, data, methods, findings, and challenges•We also discuss the uncertainty and validation of the C flux and stock estimates•A forward-looking perspective and insights for future research are provided
Large‐scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar‐induced ...fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007–2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment‐2 satellite, benchmarked with county‐level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF‐based approach accounting for photosynthetic pathways (i.e. C₃ and C₄ crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon‐use‐efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.
Remote sensing of sun-induced chlorophyll fluorescence (SIF) is a novel optical tool for the assessment of terrestrial photosynthesis or gross primary production (GPP). Several recent studies have ...demonstrated the strong link between GPP and space-borne retrievals of SIF at broad scales. However, critical gaps remain between short-term small-scale mechanistic understanding and seasonal global observations. Here, we present a model-based analysis of the relationship between SIF and GPP across scales for diverse vegetation types and a range of meteorological conditions, with the ultimate focus on reproducing the environmental conditions during remote sensing measurements. The coupled fluorescence-photosynthesis model SCOPE is used to simulate GPP and SIF at the both leaf and canopy levels for 13 flux sites. Analyses were conducted to investigate the effects of temporal scaling, canopy structure, overpass time, and spectral domain on the relationship between SIF and GPP. The simulated SIF is highly non-linear with GPP at the leaf level and instantaneous time scale and tends to linearize when scaling to the canopy level and daily to seasonal. These relationships are consistent across a wide range of vegetation types. The relationship between SIF and GPP is primarily driven by absorbed photosynthetically active radiation (APAR), especially at the seasonal scale, although the photosynthetic efficiency also contributes to strengthen the link between them. The linearization of their relationship from leaf to canopy and averaging over time is because the overall conditions of the canopy fall within the range of the linear responses of GPP and SIF to light and the photosynthetic capacity. Our results further show that the top-of-canopy relationships between simulated SIF and GPP have similar linearity regardless of whether we used the morning or midday satellite overpass times. Field measurements confirmed these findings. In addition, the simulated red SIF at 685nm has a similar relationship with GPP as that of far-red SIF at 740nm at the canopy level. These findings provide model-based evidence to interpret remotely sensed SIF data and their relationship with GPP.
•SIF tend to linearize with GPP from leaf to canopy and short-term to seasonal scale.•SIF-GPP relationships are similar between morning and midday satellite overpass.•The simulated SIF685 has a similar relationship with GPP as SIF740 at canopy level.
Solar‐induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), which has substantially improved spatial and temporal resolutions, will improve the global ...estimations of gross primary production (GPP) than previous satellite SIF data. However, the canopy‐leaving SIF observed by sensors (SIFobs) represents only a portion of the total canopy SIF emission (SIFtotal). This portion is sensitive to the canopy structure and observation direction, resulting in uncertainties in GPP estimations using SIFobs. Here we used the spectral invariant theory to derive global soil‐resistant SIFtotal (SIFtotal‐SR) from TROPOMI SIFobs and evaluated the SIFtotal‐SR performance in estimating GPP. We found that the clear differences in SIFobs between needleleaf forest and crops diminished for SIFtotal‐SR. SIFtotal‐SR increased the coefficient of determination (R2) by 0.09 and 0.11 against the flux tower instantaneous and daily GPP, respectively. This derived SIFtotal‐SR can be used to develop more robust GPP models and better constrain carbon cycle models.
Plain Language Summary
Photosynthesis is an essential process by which plants convert carbon dioxide (CO2) and water into sugars and provides the primary source of energy for life on Earth. Variations in photosynthesis also affect the CO2 concentration in the atmosphere, the water‐heat balance, and the climate. It is therefore important to estimate the photosynthesis rate, which is generally quantified as the gross primary production (GPP) at the large spatial and temporal scales. Solar‐induced chlorophyll fluorescence (SIF) retrieved from the recently launched TROPOspheric Monitoring Instrument, which has substantially improved spatial and temporal resolutions, has greater potential than previous satellite SIF data to be used as a proxy of photosynthesis. However, SIF observed by sensors (SIFobs) represents only a portion of the total canopy SIF emission from all leaves within a canopy (SIFtotal). This portion depends on the canopy structure and observation direction, hindering the application of SIFobs for GPP estimation. In this study, a correction method is proposed to convert directional SIFobs retrievals into SIFtotal by considering the soil effect in the conversion. The derived soil‐resistant SIFtotal could help to better estimate the terrestrial GPP compared with using SIFobs.
Key Points
Total canopy solar‐induced chlorophyll fluorescence (SIF) emission (SIFtotal) is derived from TROPOMI SIF with reflectance data
SIFtotal better correlates to gross primary production (GPP) than observed SIF (SIFobs)
The soil background effect on SIFtotal is minimized by estimating the probability of observing a sunlit background under vegetation
Two new remote sensing vegetation parameters derived from spaceborne spectrometers and simulated with a three dimensional radiative transfer model have been evaluated in terms of their prospects and ...drawbacks for the monitoring of dense vegetation canopies: (i) sun-induced chlorophyll fluorescence (SIF), a unique signal emitted by photosynthetically active vegetation and (ii) the canopy scattering coefficient (CSC), a vegetation parameter derived along with the directional area scattering factor (DASF) and expected to be particularly sensitive to leaf optical properties. Here, we present the first global data set of DASF/CSC and examine the potential of CSC and SIF for providing complementary information on the controversially discussed vegetation seasonality in Amazon forests. A comparison between near-infrared SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument and the Orbiting Carbon Observatory-2 (OCO-2) (overpass time in the morning and noon, respectively) reveals the response of SIF to instantaneous photosynthetically active radiation (PAR). Large-scale seasonal swings of GOME-2 SIF amount up to 21% (regarding the annual maximum) and peak in October and around February, while OCO-2 SIF peaks in February. However, both time series agree very well if SIF is normalized by overpass time and wavelength. We further examine anistropic reflectance characteristics with the finding that the hot spot effect significantly impacts observed GOME-2 SIF values. On the contrary, our sensitivity analysis suggests that CSC is highly independent of sun-sensor geometry as well as atmospheric effects. The slight annual variability of CSC (3%) shows no clear seasonal cycle, while a relatively high spatial standard deviation points to a high degree of spatial heterogeneity in our study domain within the central Amazon Basin.
•Very good agreement between normalized SIF from OCO-2 and GOME-2.•SIF shows a similar but less pronounced hot spot effect compared to NIR reflectance.•Canopy scattering coefficient (CSC) seems to reverse the usual saturation effect.•CSC is highly independent of directional and atmospheric effects.
Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal that can potentially indicate vegetation photosynthetic activity, can be retrieved from ground-based, airborne and satellite ...measurements. However, due to the scattering and re-absorption effects inside the leaves and canopy, SIF measured at the canopy level is only a small part of the total SIF emission at the photosystem level. Therefore, a downscaling mechanism of SIF from the canopy level to the photosystem level is important for better understanding the relationship between SIF and the vegetation gross primary production (GPP). In this study, firstly, we analyzed the canopy scattering effects using a simple parameterization model based on the spectral invariant theory. The probability for SIF photons to escape from the canopy was found to be related to the anisotropic spectral reflectance, canopy interception of the upward solar radiation, and leaf absorption. An empirical approach based on a Random Forest (RF) regression algorithm was applied to downscale SIF constrained by the red, red-edge and far-red anisotropic reflectance. The RF was trained using simulations conducted with the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The performance of the SIF downscaling method was evaluated with SCOPE and Discrete Anisotropic Radiative Transfer (DART) model simulations, ground measurements and airborne data. Results show that estimated SIF at the photosystem level matches well with simulated reference data, and the relationship between SIF and photosynthetically active radiation absorbed by chlorophyll is improved by SIF downscaling. This finding in combination with other evaluation criteria suggests the downscaling of canopy SIF as an efficient strategy to normalize species dependent effects of canopy structure and varying solar-view geometries. Based on our results for the SIF-APAR relationship, we expect that such normalization approaches can be helpful to improve estimates of photosynthesis using remote sensing measurements of SIF.
•A practical method for SIF downscaling from canopy level to photosystem level.•Random forest is efficient for the estimation of SIF escape probability.•Only reflectance at red, red-edge and far-red bands are needed for SIF downscaling.•The method is effective for ground-based and airborne SIF measurements.•The relationship between SIF and APAR can be improved by SIF downscaling.
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