Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence

Water stress significantly affects the diurnal variation of solar-induced chlorophyll fluorescence (SIF): A case study for winter wheat

Remote sensing of Solar-induced chlorophyll fluorescence (SIF) has been widely used in estimating Gross Primary Productivity (GPP) and detecting stress in terrestrial ecosystems. Water stress adversely impacts the growth, development, and productivity of a plant. Recently, the characterizing and understanding of the diurnal cycling of plant functioning and ecosystem processes has been explored using SIF. However, the diurnal response of SIF to different levels of water stress remains unclear. This study conducted field experiments on winter wheat by subjecting it to different levels of water stress including well-watered (CK) and, mild, moderate, and severe water stress (D1, D2, D3), and collected the spectral data using an automated SIF measurement system. The results observed the strong SIF-PAR (photosynthetically active radiation) correlations and that these relationships gradually decoupled with increasing water stress, which further decreased the accuracy of temporal upscaling of far-red SIF from an instantaneous to daily scale. To quantify the characteristics of diurnal far-red SIF, five indices including peak time, peak value, curve opening coefficient (leading coefficient of the parabola), and left/right slopes of the peak were proposed. The results demonstrated that diurnal far-red SIF was characterized by an earlier peak time, decreasing peak value, wider curve opening, and flattening right slope from the CK plot to the D3 plot. There were certain mechanisms linking the different indices, for example, between peak size and opening coefficient. Furthermore, the response of far-red SIF to water stress was most pronounced at noon. SIF/PAR exhibited a more significant response to varying water stress compared to far-red SIF, which mitigated the negative influence of PAR variations on diurnal SIF. These findings contribute to the monitoring of plant water dynamics at fine temporal scales.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms

Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001-2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R2 = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating gross carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.

A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data

Satellite-derived solar-induced chlorophyll fluorescence (SIF) has been proven to be a valuable tool for monitoring vegetation’s photosynthetic activity at regional or global scales. However, the coarse spatiotemporal resolution or discrete space coverage of most satellite SIF datasets hinders their full potential for studying carbon cycle and ecological processes at finer scales. Although the recent TROPOspheric Monitoring Instrument (TROPOMI) partially addresses this issue, the SIF still has drawbacks in spatial insufficiency and spatiotemporal discontinuities when gridded at high spatiotemporal resolutions (e.g., 0.05°, 1-day or 2-day) due to its nonuniform sampling sizes, swath gaps, and clouds contaminations. Here, we generated a new global SIF product with Seamless spatiotemporal coverage at Daily and 0.05° resolutions (SDSIF) during 2018–2020, using the random forest (RF) approach together with TROPOMI SIF, MODIS reflectance and meteorological datasets. We investigated how the model accuracy was affected by selection of explanatory variables and model constraints. Eventually, models were trained and applied for specific continents and months given the similar response of SIF to environmental variables within closer space and time. This strategy achieved better accuracy (R2 = 0.928, RMSE = 0.0597 mW/m2/nm/sr) than one universal model (R2 = 0.913, RMSE = 0.0653 mW/m2/nm/sr) for testing samples. The SDSIF product can well preserve the temporal and spatial characteristics in original TROPOMI SIF with high temporal correlations (mean R2 around 0.750) and low spatial residuals (less than ±0.081 mW/m2/nm/sr) between them two at most regions (80% of global pixels). Compared with the original SIF at five flux sites, SDSIF filled the temporal gaps and was better consistent with tower-based SIF at the daily scale (the mean R2 increased from 0.467 to 0.744. Consequently, it provided more reliable 4-day SIF averages than the original ones from sparse daily observations (e.g., the R2 at Daman site was raised from 0.614 to 0.837), which resulted in a better correlation with 4-day tower-based GPP. Additionally, the global coverage ratio and local spatial details had also been improved by the reconstructed seamless SIF. Our product has advantages in spatiotemporal continuities and details over the original TROPOMI SIF, which will benefit the application of satellite SIF for understanding carbon cycle and ecological processes at finer spatial and temporal scales.

Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products

Gross primary productivity (GPP) is a vital variable of the global carbon cycle, but the quantification of global GPP is subject to significant uncertainty due to the lack of direct observations at a global scale. Here, we evaluated and compared 45 GPP products in terms of their applicability to different vegetation types at various spatiotemporal scales. The results show that 44 GPP products and obsGPP (Model Tree Ensemble GPP derived from observations and named obsGPP) have similar global patterns with correlation coefficients greater than 0.8 except for NGT, where GOSIF, RS, and BESS are prominent. GPP products have the greatest variation in Suriname, with a mean 75th and 25th percentile difference value of 0.4748 (normalized), and we recommend RS, SDGVM and LPJ-wsl as they provide GPP estimates close to the average GPP. In terms of seasonal estimations, considerable disagreement occurs among the GPP products in winter, with a range from 118.76 to 314.95 gC/m²/season, among which JULES has the closest GPP value to the average GPP estimation. For studies concerning vegetation types preference is given to the LUE average GPP. The 45 GPP products are more consistent on grasslands but, have obvious differences for savannas. All GPP products have their own specific spatiotemporal scales, such as global or national scales or different seasons and different vegetation types (forest, grasslands, etc.). This study provides guidelines for selecting GPP products.

Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.

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.

A unified vegetation index for quantifying the terrestrial biosphere

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO₂ and mitigating global climate change.

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents' estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

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°.

The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

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.

Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation

The response of terrestrial carbon uptake to increasing atmospheric [CO₂ ], that is the CO₂ fertilization effect (CFE), remains a key area of uncertainty in carbon cycle science. Here we provide a perspective on how satellite observations could be better used to understand and constrain CFE. We then highlight data assimilation (DA) as an effective way to reconcile different satellite datasets and systematically constrain carbon uptake trends in Earth System Models. As a proof-of-concept, we show that joint DA of multiple independent satellite datasets reduced model ensemble error by better constraining unobservable processes and variables, including those directly impacted by CFE. DA of multiple satellite datasets offers a powerful technique that could improve understanding of CFE and enable more accurate forecasts of terrestrial carbon uptake.

Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2

Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05° spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R2 = 0.74, Root Mean Square Error = 1.92 g C m−2 d−1). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1°) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 ± 8.8 Pg C yr−1) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.

Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m2, and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m2. There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future.

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.

Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges

Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.

Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content

The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO₂ emissions through photosynthesis. The rate of photosynthesis is determined jointly by environmental variables and the intrinsic photosynthetic capacity of plants (i.e. maximum carboxylation rate; Vcmax25 ). A lack of an effective means to derive spatially and temporally explicit Vcmax25 has long hampered efforts towards estimating global photosynthesis accurately. Recent work suggests that leaf chlorophyll content (Chl_leaf ) is strongly related to Vcmax25 , since Chl_leaf and Vcmax25 are both correlated with photosynthetic nitrogen content. We used medium resolution satellite images to derive spatially and temporally explicit Chl_leaf , which we then used to parameterize Vcmax25 within a terrestrial biosphere model. Modelled photosynthesis estimates were evaluated against measured photosynthesis at 124 eddy covariance sites. The inclusion of Chl_leaf in a terrestrial biosphere model improved the spatial and temporal variability of photosynthesis estimates, reducing biases at eddy covariance sites by 8% on average, with the largest improvements occurring for croplands (21% bias reduction) and deciduous forests (15% bias reduction). At the global scale, the inclusion of Chl_leaf reduced terrestrial photosynthesis estimates by 9 PgC/year and improved the correlations with a reconstructed solar-induced fluorescence product and a gridded photosynthesis product upscaled from tower measurements. We found positive impacts of Chl_leaf on modelled photosynthesis for deciduous forests, croplands, grasslands, savannas and wetlands, but mixed impacts for shrublands and evergreen broadleaf forests and negative impacts for evergreen needleleaf forests and mixed forests. Our results highlight the potential of Chl_leaf to reduce the uncertainty of global photosynthesis but identify challenges for incorporating Chl_leaf in future terrestrial biosphere models.

A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data

Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global ‘OCO-2’ SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05°, 8-day) over the period 2000–2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R2 = 0.79, root mean squared error (RMSE) = 0.07 W m−2 μm−1 sr−1). The model was then used to estimate SIF for each 0.05° × 0.05° grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1°), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R2 = 0.73, p < 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.

Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy

Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA <50° and under strictly clear sky conditions. Correlations found, even under these screened scenarios, resulted around ~r2 = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (ΔF/Fm′), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed.

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Upscaling Solar-Induced Chlorophyll Fluorescence from an Instantaneous to Daily Scale Gives an Improved Estimation of the Gross Primary Productivity

Solar-induced chlorophyll fluorescence (SIF) is closely linked to the photosynthesis of plants and has the potential to estimate gross primary production (GPP) at different temporal and spatial scales. However, remotely sensed SIF at a ground or space level is usually instantaneous, which cannot represent the daily total SIF. The temporal mismatch between instantaneous SIF (SIFinst) and daily GPP (GPPdaily) impacts their correlation across space and time. Previous studies have upscaled SIFinst to the daily scale based on the diurnal cycle in the cosine of the solar zenith angle ( cos ( SZA ) ) to correct the effects of latitude and length of the day on the variations in the SIF-GPP correlation. However, the important effects of diurnal weather changes due to cloud and atmospheric scattering were not considered. In this study, we present a SIF upscaling method using photosynthetically active radiation (PAR) as a driving variable. First, a conversion factor (i.e., the ratio of the instantaneous PAR (PARinst) to daily PAR (PARdaily)) was used to upscale in-situ SIF measurements from the instantaneous to daily scale. Then, the performance of the SIF upscaling method was evaluated under changing weather conditions and different latitudes using continuous tower-based measurements at two sites. The results prove that our PAR-based method can reduce not only latitude-dependent but also the weather-dependent variations in the SIF-GPP model. Specifically, the PAR-based method gave a more accurate prediction of diurnal and daily SIF (SIFdaily) than the cos ( SZA ) -based method, with decreased relative root mean square error (RRMSE) values from 42.2% to 25.6% at half-hour intervals and from 25.4% to 13.3% at daily intervals. Moreover, the PAR-based upscaled SIFdaily had a stronger correlation with the daily absorbed PAR (APAR) than both the SIFinst and cos ( SZA ) -based upscaled SIFdaily, especially for cloudy days with a coefficient of determination (R2) that increased from approximately 0.5 to 0.8. Finally, the PAR-based SIFdaily was linked to GPPdaily and compared to the SIFinst or cos ( SZA ) -based SIFdaily. The results indicate that the SIF-GPP correlation can obviously be improved, with an increased R2 from approximately 0.65 to 0.75. Our study confirms the importance of upscaling SIF from the instantaneous to daily scale when linking SIF with GPP and emphasizes the need to take diurnal weather changes into account for SIF temporal upscaling.

Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.