Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0-0.037), as compared to an intermediate offset of 0.047-0.062 from NIRv, and the largest offset of 0.089-0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.

Reliable spatiotemporal crop data are vital for sustainable agricultural management. However, efficient algorithms that can be automatically applied to large regions are scarce, especially for cash crops, since it is hard to distinguish their uniqueness merely from temporal profiles of traditional vegetation indices. The efficiency of knowledge-based temporal features and red-edge pigment indices in characterizing crop growth has been reported in the literature, but the potential of combined applications in identifying crops has not been validated yet. This study fills this gap by developing a knowledge-based automated Peanut mapping Algorithm with a combined consideration of crop Phenology and Pigment content variations (PAPP). Peanut crop has earlier and longer flowering stages compared to other crops such as paddy rice and maize. Peanut fields are distinguished with less variations in anthocyanin and chlorophyll as well as higher carotenoid concentrations. Herein, three phenology and pigment-based indicators were proposed for peanut mapping by exploring the concentration and variations of the chlorophyll, anthocyanin and carotenoid indices, respectively. This PAPP algorithm was validated over large regions (around 250 thousand km(2) cropland) covering three provinces of Northeast China using Sentinel-2 time-series images. The results reported that there was 8,371 km(2) peanut area in Northeast China in 2018, concentrated in the western Jilin and Liaoning provinces. Validation from the 1,102 field survey sites revealed overall accuracies of 94%, with a kappa index of 0.87 and F-1 score of 0.91. The PAPP algorithm was not sensitive to thresholding, and a high classification accuracy could be obtained once the threshold of one indicator was roughly defined. The thresholds could be determined based on the proportions of staple crops (i.e. paddy rice and maize) using the historical agricultural statistical data since peanut fields either show the least or largest values in these three proposed indicators. The PAPP algorithm demonstrates the capabilities of automatic peanut mapping over large regions with no requirements of further training and modifications. This study makes contributions to a sustainable agricultural management society given the potential significant role of legume crops in co-delivering food security and adapting to climate change.

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

Carbonyl sulfide (COS) has emerged as a multi-scale tracer for terrestrial photosynthesis. To infer ecosystem-scale photosynthesis from COS fluxes often requires knowledge of leaf relative uptake (LRU), the concentration-normalized ratio between leaf COS uptake and photosynthesis. However, current mechanistic understanding of LRU variability remains inadequate for deriving robust COS-based estimates of photosynthesis. We derive a set of closed-form equations to describe LRU responses to light, humidity and CO2 based on the Ball-Berry stomatal conductance model and the biochemical model of photosynthesis. This framework reproduces observed LRU responses: decreasing LRU with increasing light or decreasing humidity; it also predicts that LRU increases with ambient CO2. By fitting the LRU equations to flux measurements on a C-3 reed (Typha latifolia), we obtain physiological parameters that control LRU variability, including an estimate of the Ball-Berry slope of 7.1 without using transpiration measurements. Sensitivity tests reveal that LRU is more sensitive to photosynthetic capacity than to the Ball-Berry slope, indicating stomatal response to photosynthesis. This study presents a simple framework for interpreting observed LRU variability and upscaling LRU. The stoma-regulated LRU response to CO2 suggests that COS may offer a unique window into long-term stomatal acclimation to elevated CO2.

The uptake of carbonyl sulfide (COS) by terrestrial plants is linked to photosynthetic uptake of CO2 as these gases partly share the same uptake pathway. Applying COS as a photosynthesis tracer in models requires an accurate representation of biosphere COS fluxes, but these models have not been extensively evaluated against field observations of COS fluxes. In this paper, the COS flux as simulated by the Simple Biosphere Model, version 4 (SiB4), is updated with the latest mechanistic insights and evaluated with site obser- vations from different biomes: one evergreen needleleaf forest, two deciduous broadleaf forests, three grasslands, and two crop fields spread over Europe and North America. We improved SiB4 in several ways to improve its representation of COS. To account for the effect of atmospheric COS mole fractions on COS biosphere uptake, we replaced the fixed atmospheric COS mole fraction boundary condition originally used in SiB4 with spatially and temporally varying COS mole fraction fields. Seasonal amplitudes of COS mole fractions are similar to 50-200 ppt at the investigated sites with a minimum mole fraction in the late growing season. Incorporating seasonal variability into the model reduces COS uptake rates in the late growing season, allowing better agreement with observations. We also replaced the empirical soil COS uptake model in SiB4 with a mechanistic model that represents both uptake and production of COS in soils, which improves the match with observations over agricultural fields and fertilized grassland soils. The improved version of SiB4 was capable of simulating the diurnal and seasonal variation in COS fluxes in the boreal, temperate, and Mediterranean region. Nonetheless, the daytime vegetation COS flux is underestimated on average by 8 +/- 27 %, albeit with large variability across sites. On a global scale, our model modifications decreased the modeled COS terrestrial biosphere sink from 922 Gg S yr(-1) in the original SiB4 to 753 Gg S yr(-1) in the updated version. The largest decrease in fluxes was driven by lower atmospheric COS mole fractions over regions with high productivity, which highlights the importance of accounting for variations in atmospheric COS mole fractions. The change to a different soil model, on the other hand, had a relatively small effect on the global biosphere COS sink. The secondary role of the modeled soil component in the global COS budget supports the use of COS as a global photosynthesis tracer. A more accurate representation of COS uptake in SiB4 should allow for improved application of atmospheric COS as a tracer of local- to global-scale terrestrial photosynthesis.

Vegetation dynamics can be tracked using remotely sensed vegetation indices, but these metrics can result in conflicting conclusions. This Technical Review details the history, application and potential pitfalls associated with vegetation indices and makes recommendations for their best use.

There remains limited information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (phi F), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships.

Greening, an increase in photosynthetically active plant biomass, has been widely reported as period-related and region-specific. We hypothesized that vegetation trends were highly density-dependent with intensified browning in dense canopies and increased greening in sparse canopies. We exploited this insight by estimating vegetation trends in peak growth from dense to sparse canopies graded from 1 to 20 using the non-parametric Mann-Kendall trend test based on the 500 m 8-day composite MODIS Near Infrared Reflectance of terrestrial vegetation (NIRv) time series datasets in the past two decades (2001-2019) at the global scale. We found that global greening increased by 1.42% per grade with strong fit before grade 15 (R-2 = 0.95): net browning (11% browning vs 9% greening) exhibited in high-density canopies (NIRv > 0.39) in contrast to 32% greening in low-density canopies (NIRv asymptotic to 0.15). While the density-dependent greening was evidenced across different biomes and ecosystems, the steepest gradient (changes per grade) in cropland highlighted the increasingly intensified agricultural activities globally. Greening gradients declined in the dryland, but enhanced in the High-latitude ecosystems driven by warming, especially in the shrubland. Density-dependent vegetation trends were accounted for by the disproportionately impacts from climate changes and the un-equal contributions of Land Cover Changes (LCC) among dense and sparse canopies. Vegetation trends and greening gradients could be extensively facilitated by Wetting or Decreasing solar Radiation (WDR), especially in sparse grass -land and shrubland. Browning was dominant in dense canopies, which was further aggravated by Drying and Increasing solar Radiation (DIR), especially woody vegetation. This study implied the widespread degradation or mortality of highly productive vegetation hidden among global greening dominant in open ecosystems, which might be further exacerbated by the predicted increasing drought under global warming.

Solar-induced chlorophyll fluorescence (SIF) shows great potential to assess plants physiological state and response to environmental changes. Recently the near-infrared reflectance of vegetation (NIRv) provides a promising way to quantify the confounding effect of canopy structure in SIF, while the difference between SIF and NIRv under varying environmental conditions has not been well explored. Here we developed a simple approach to extract the fluorescence yield (Phi(F)) by the combined use of SIF and the near-infrared radiance of vegetation (NIRvR). The proposed NIRvR approach was evaluated in multiple ways, including with the seasonal leaf-level steady-state fluorescence yield. Results indicate that NIRvR-derived Phi(F) well captured the seasonal variation of the fluorescence yield changes, and achieved similar results with the existing approach. Both SIF and NIRvR were derived from the airborne imaging fluorescence spectrometer HyPlant for three case studies to evaluate the impacts of light adaptation, heat stress and water limitation on Phi(F). For the light adaptation case study, Phi(F) over the low-light adapted sugar beet field was about 1.3 times larger compared to an unaffected reference area while the difference in NIRvR was minimal, which clearly shows the short-term photosynthetic light induction effect and the ability of SIF to detect plant physiological responses. For the heat stress experiment, OF decreased during a natural heatwave in 2015 in the fields of rapeseed from 0.0150 to 0.0130, barley from 0.0152 to 0.0144, and wheat from 0.0146 to 0.0142 which showed signs of senescence, while slightly increased from 0.0125 to 0.0130 in the corn field which was still in growing. At the water-limited sugar beet field, Phi(F) first increased towards solar noon and then slightly decreased during the afternoon over the water-limited areas from 0.017 to 0.021 and 0.020, with high temperature and high light at noon. The advantages to use SIF/NIRvR as a proxy of Phi(F) to detect stress-induced limitations in photosynthesis include that the impacts of canopy structure and sun-sensor geometry on the Phi(F) estimation are explicitly cancelled, and photosynthetically active radiation (PAR) is not required as input. Finally, our approach is directly applicable to satellite-derived estimates of SIF, enabling the study of variations in Phi(F) to detect the effects of abiotic changes and stresses at large scale.

We have obtained Washington CCD photometry with the CTIO 4m and 1.5m for approximately 50 intermediate-to-old age star clusters in the Clouds. The data extend to near or below the main sequence and provide excellent photometry for the giants, from which precise (internal errors < 0.l dex) mean cluster abundances can be determined. We present data for several of the clusters and discuss the results. Intermediate resolution spectra have also been obtained for some 16 clusters with the CTIO 4m ARGUS multiple-object fibre-fed spectrograph. Finally, we have also obtained high dispersion (R approximately 20,000) echelle spectra for several of the brighter giants in a small sample of Large Magellanic Cloud (LMC) clusters. Detailed elemental abundances derived from these spectra will be presented. These data will help refine our knowledge of the age-metallicity relation in the Clouds.