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 high-resolution spectral sensors in tandem with 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.

Efficient and timely quantification of landscape changes of urbanization, devegetation, and inundation in China is important for decision making in sustainable development. This study proposed a novel and automatic approach to track landscape changes in China based on 500 m 8-day composite MODIS datasets from 2001 to 2017. An overall accuracy of 94.09% was achieved on the basis of 5,178 references sites. Results showed that newly impervious surface areas (ISA), bare lands, and inundated water bodies accounted for 0.51%, 0.23%, and 0.25% of the total land area in China, respectively. Rapid urbanization transformation was observed with the average annual land urbanization rate of 9.53%, almost three-times higher than the global rate of urbanization. The speediest period of urbanization was exhibited in 2009 and 4,938.89 km(2) of newly formed ISA were generated in this single year. The most obvious period of devegetation, which converted vegetation to bare lands, was observed from 2006 to 2009, with 2000-2100 km(2) yr(-1). Urbanization and devegetation dominantly occurred in humid/semihumid regions (>85%), particularly in the Yangtze River Delta. The remarkable devegetation was strongly associated with newly ISA; however, significant reduction of devegetation was observed in arid/semiarid regions in 2010s. Besides one hotspot surrounding newly ISA, the other hotspot of inundation in remote highlands was highlighted and distinct reduction in inundation was evidenced after 2010. A free download link for these datasets is attached ().

Differentiating the contributions of photosynthesis and respiration to the global carbon cycle is critical for improving predictive climate models. Carbonic anhydrase (CA) activity in leaves is responsible for the largest biosphere-atmosphere trace gas fluxes of carbonyl sulfide (COS) and the oxygen-18 isotopologue of carbon dioxide ((COO)-O-18) that both reflect gross photosynthetic rates. However, CA activity also occurs in soils and will be a source of uncertainty in the use of COS and (COO)-O-18 as carbon cycle tracers until process-based constraints are improved. In this study, we measured COS and (COO)-O-18 exchange rates and estimated the corresponding CA activity in soils from a range of biomes and land use types. Soil CA activity was not uniform for COS and CO2, and patterns of divergence were related to microbial community composition and CA gene expression patterns. In some cases, the same microbial taxa and CA classes catalyzed both COS and CO2 reactions in soil, but in other cases the specificity towards the two substrates differed markedly. CA activity for COS was related to fungal taxa and beta-D-CA expression, whereas CA activity for CO2 was related to algal and bacterial taxa and alpha-CA expression. This study integrates gas exchange measurements, enzyme activity models, and characterization of soil taxonomic and genetic diversity to build connections between CA activity and the soil microbiome. Importantly, our results identify kinetic parameters to represent soil CA activity during application of COS and (COO)-O-18 as carbon cycle tracers.

Solar-induced chlorophyll fluorescence (SIF) has emerged as a leading approach for remote sensing of gross primary productivity (GPP). While SIF has an intrinsic, underlying relationship with canopy light capture and light use efficiency, these physiological relationships are obscured by the fact that satellites observe a small and variable fraction of total emitted canopy SIF. Upon emission, most SIF photons are reabsorbed or scattered within the canopy, preventing their observation remotely. The complexities of the radiative transfer process, which vary across time and space, limit our ability to reliably infer physiological processes from SIF observations. Here, we propose an approach for estimating the fraction of total emitted near-infrared SIF (760 nm) photons that escape the canopy by combining the near-infrared reflectance of vegetation (NIRV) and the fraction of absorbed photosynthetically active radiation (fPAR), two widely available remote sensing products. Our approach relies on the fact that NIRV is resilient against soil background contamination, allowing us to reliably calculate the bidirectional reflectance factor of vegetation, which in turn conveys information about the escape ratio of SIF photons. Our NIRV-based approach explains variations in the escape ratio with an R-2 of 0.91 and an RMSE of 1.48% across a series of simulations where canopy structure, soil brightness, and sun-sensor-canopy geometry are varied. The approach is applicable to conditions of low leaf area index and fractional vegetation cover. We show that correcting for the escape ratio of SIF using NIRV provides robust estimates of total emitted SIF, providing for the possibility of studying physiological variations of fluorescence yield at the global scale.

Terrestrial photosynthesis is the largest and one of the most uncertain fluxes in the global carbon cycle. We find that near-infrared reflectance of vegetation (NIRV), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R-2 = 0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales. Scaling the relationship between gross primary production (GPP) and NIRV from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147 Pg C/year (95% credible interval 131-163 Pg C/year), which falls between bottom-up GPP estimates and the top-down global constraint on GPP from oxygen isotopes. NIRV-derived estimates of GPP are systematically higher than existing bottom-up estimates, especially throughout the midlatitudes. Progress in improving estimated GPP from NIRV can come from improved cloud screening in satellite data and increased resolution of vegetation characteristics, especially details about plant photosynthetic pathway.

Research Highlights: To better understand within-community variation in wood density, our study demonstrated that a more nuanced approach is required beyond the climate-wood density correlations used in global analyses. Background and Objectives: Global meta-analyses have shown higher wood density is associated with higher temperatures and lower rainfall, while site-specific studies have explained variation in wood density with structural constraints and allometry. On a regional scale, uncertainty exists as to what extent climate and structural demands explain patterns in wood density. We explored the role of species climate niche, geofloristic history, habitat specialization, and allometry on wood density variation within a California forest/chaparral community. Materials and Methods: We collected data on species wood density, climate niche, geofloristic history, and riparian habitat specialization for 20 species of trees and shrubs in a California forest. Results: We found a negative relationship between wood density and basal diameter to height ratio for riparian species and no relationship for non-riparian species. In contrast to previous studies, we found that climate signals had weak relationships with wood density, except for a positive relationship between wood density and the dryness of a species' wet range edge (species with drier wet range margins have higher wood density). Wood density, however, did not correlate with the aridity of species' dry range margins. Geofloristic history had no direct effect on wood density or climate niche for modern California plant communities. Conclusions: Within a California plant community, allometry influences wood density for riparian specialists, but non-riparian plants are 'overbuilt' such that wood density is not related to canopy structure. Meanwhile, the relationship of wood density to species' aridity niches challenges our classic assumptions about the adaptive significance of high wood density as a drought tolerance trait.

Solar Induced chlorophyll Fluorescence (SIF) shows promise as an approach for estimating gross primary production (GPP) remotely. However, sun-target-sensor geometry and within-canopy absorption of SIF can alter the relationship between measured SIF and GPP, because sensors can only retrieve some unknown fraction of the total emitted SIF. Radiative transfer models that allow for variation in canopy structure and sensor angles are therefore needed to properly interpret SIF measurements. Spectral invariants allow decoupling of the wavelength-independent canopy structure and the wavelength-dependent leaf and soil spectrum in the radiative transfer process. Here we develop a simple analytical Fluorescence Radiative Transfer model based on Escape and Recollision probability (FluorRTER) to investigate the impact of canopy structure and sun-target-sensor geometry on SIF emissions. SIF simulations using the FluorRTER model agreed well the one-dimensional Soil-Canopy Observation of Photochemistry and Energy balance (SCOPE) model and the three-dimensional Fluorescence model with Weighted Photon Spread (FluorWPS) model. The fractional vegetation cover (FVC) and clumping effect have a large influence the SIF emission of 3D discontinuous canopies. For a moderate solar zenith angle (30 degrees) and a clumped canopy (FVC = 0.6), the difference between the directional observed SIF of a 3D discontinuous canopy and a 1D homogeneous canopy was as large as 43.2% and 38.4% for Photosystem I + II fluorescence at 685 nm and at 740 nm, respectively. By bridging the gap between observed SIF and total emitted SIF over 3D heterogeneous vegetation canopies, the FluorRTER model can assist with the angular normalization of SIF measurements and enable the more robust interpretation of how variations in SIF from directional and hemispherical in-situ, airborne and satellite observations relate to leaf and whole-canopy physiological processes.

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIRv, Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv, Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv, Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIRv, Rad and absorbed photosynthetically active radiation by green leaves (APAR(green)), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. The NIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv, Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

Solar-induced chlorophyll fluorescence (SIF) measured from space has been increasingly used to quantify plant photosynthesis at regional and global scales. Apparent canopy SIF yield (SIFyield apparent), determined by fluorescence yield (Phi(F)) and escaping ratio (f(esc)), together with absorbed photosynthetically active radiation (APAR), is crucial in driving spatio-temporal variability of SIF. While strong linkages between SIFyield apparent and plant physiological responses and canopy structure have been suggested, spatio-temporal variability of SIFyield apparent at regional scale remains largely unclear, which limits our understanding of the spatio-temporal variability of SIF and its relationship with photosynthesis. In this study, we utilized recent SIF data with high spatial resolution from two satellite instruments, OCO-2 and TROPOMI, together with multiple other datasets. We estimated SIFyield apparent across space, time, and different vegetation types in the U.S. Midwest during crop growing season (May to September) from 2015 to 2018. We found that SIFyield apparent of croplands was larger than non-croplands during peak season (July-August). However, SIFyield apparent between corn (C4 crop) and soybean (C3 crop) did not show a significant difference. SIFyield apparent of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns. The spatial variability of precipitation during the growing season could explain the overall spatial pattern of SIFyield apparent. Further analysis by decomposing SIFyield apparent into Phi(F) and f(esc) using near-infrared reflectance of vegetation (NIRV) suggests that fesc may be the major driver of the observed variability of SIFyield apparent.

Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (f(esc)) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R-2 = 0.4-0.6) of fesc to the seasonal dynamics of the photosynthetic light use efficiency (LUEP), while the estimated physiological SIF yield was almost entirely uncorrelated to LUEP both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and f(esc), explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for f(esc), improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIRV) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates.