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.

Urbanization has widely known to directly consume swaths of cropland worldwide. Knowledge on what kinds of urbanization processes spared cropland is important for land use planning. This study offered insights on the impact of city level (city hierarchy: from the 1st to the 6th Tier cities) and urbanization modes (mega-city, city, town and village modes) on cropland losses through a first-ever continuous national survey on 345 prefectural level cities or above in mainland China from 2003 to 2016. We found that higher tier cities were associated with more direct and severe losses. Specifically, over 80 % of the recent urbanization formed on cropland in the 1st Tier cities, and the newly 1st Tier cities suffered the most rigorous losses. At national level, mega-city mode urbanization resulted in direct cropland losses (80 %) and the village mode was associated with prominent high-quality ratio (45 %). Town mode spared cropland more than village mode. However, ranking with urbanization mode was less obvious and even changed in the lower-Tier cities. At national scale, around 1.45 % of the total cropland area (approximately 2297 km(2) per year), including 1.06 % of high quality cropland area (approximately 852 km(2) per year), has been permanently lost. The most rapid cropland loss was in 2009 (3464 km(2)), and that of high quality cropland occurring in 2007 (1775 km(2)). Over 95 % cropland losses located in the east of the Hu line. Findings in this study called for target adaptive planning with full considerations of city hierarchy and urbanization mode. Particularly, land use policies to effective support land development in small towns can potentially relief pressure on cropland.

Earth system models predict that increases in atmospheric and soil dryness will reduce photosynthesis in the Amazon rainforest, with large implications for the global carbon cycle. Using in situ observations, solar-induced fluorescence, and nonlinear machine learning techniques, we show that, in reality, this is not necessarily the case: In many of the wettest parts of this region, photosynthesis and biomass tend to increase with increased atmospheric dryness, despite the associated reductions in canopy conductance to CO2. These results can be largely explained by changes in canopy properties, specifically, new leaves flushed during the dry season have higher photosynthetic capacity than the leaves they replace, compensating for the negative stomatal response to increased dryness. As atmospheric dryness will increase with climate change, our study highlights the importance of reframing how we represent the response of ecosystem photosynthesis to atmospheric dryness in very wet regions, to accurately quantify the land carbon sink.

Cropland abandonment has long been recognized as a marginalization process in rural regions but recently also as a precursor to urbanization in peri-urban regions. This study aimed to evaluate the relative importance of urbanization and poor productivity in cropland abandonment. A thorough investigation was conducted on 345 cities/towns at the prefecture level or higher in China using 500-m annual maps for 2003-2016. Cropland abandonment was spatiotemporally coupled with the urbanization. At a national scale, approximately 0.55% of the total cropland area (approximately 862 km(2) per year) has been abandoned. Cropland abandonment increased significantly until 2009, declined in the early 2010s, and then leveled off in recent years. In particular, the new first-tier cities suffered the greatest abandonment per capita. However, cropland abandonment was increasingly shifted to lower-tier cities, particularly the high-quality cropland abandonment in the medium-tier cities. Low-quality cropland in rural regions of the medium- and lower-tier cities experienced incessant abandonment. Results showed that the extent of cropland abandonment increased with city sizes described either by population/GDP or by built-up area. High-quality cropland abandonment was more closely related to the built-up area than to population and GDP for total abandonment. Urbanization-induced abandonment in peri-urban areas was dominant for most cities, with exceptions only for a few small cities. Contrary to expectation, cropland abandonment for most cities/towns in rural regions was not more driven by poor productivity, compared to peri-urban regions. Our study highlights that urbanization mainly accounted for the regional differences in cropland abandonment in China during the past two decades.

Sun-induced fluorescence (SIF) has been found to be strongly correlated with gross primary production (GPP) in a quasi-linear pattern at the scales beyond leaves. However, the causes of the GPP:SIF relationship deviating from a linear pattern remain unclear. In the current study conducted at two maize sites in Nebraska in 2017 summer growing season, we investigated the relationship between GPP and SIF at 760 nm (F-760) at two temporal scales and quantified the contributions of incoming photosynthetically active radiation (PAR(in)), fraction of absorbed PAR (fPAR), light use efficiency (LUE), and F-760 yield (F-760,F-y, defined as F-760/(PAR(in)xfPAR)) to GPP and F-760 variabilities to further understand the linearity and deviations in the GPP:F-760 relationship. We found the following: (1) For individual growth stages when canopy structure and chlorophyll content were stable, GPP and F-760 were strongly controlled by PAR(in), while LUE and F-760,F-y had much lower contributions to the GPP:F-760 relationship; during this period, LUE and F-760,F-y had either a slightly negative or no clear relationship, which explained some deviations in the GPP:SIF relationship. (2) At the seasonal scale, the contribution of LUE to GPP variability as well as the contribution of F-760,F-y to F-760 variability increased and was comparable to the contribution of PAR(in); the LUE:F-760,F-y relationship showed a strong linear relationship, which strengthened the linear GPP:F-760 relationship. Both maize sites showed similar patterns. A framework was applied to estimate LUE at individual stages and as a result, significantly improved the GPP estimation, thus enhancing the SIF potential for inferring photosynthesis.

We test the relationship between canopy photosynthesis and reflected near-infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near-infrared and red radiation from vegetation, with light-emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near-infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near-infrared radiation were highly correlated with measurements of canopy photosynthesis on half-hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near-infrared radiation were greatest for the fertilized and irrigated C(4)corn crop, intermediate for the C(3)tules on nutrient-rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient-poor, mineral soils. Reflected near-infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near-infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications.

One century ago (1920), Otto Warburg (1883-1970) discovered that in liquid cultures of unicellular green algae (Chlorella sp.) molecular oxygen (O-2) exerts an inhibitory effect on photosynthesis. Decades later, O-2 dependent suppression of photosynthetic carbon dioxide (CO2) assimilation (the "green" Warbur geffect) was confirmed on the leaves of seed plants. Here, we summarize the history of this discovery and elucidate the consequences of the photorespiratory pathway in land plants with reference to unpublished CO2 exchange data measured on the leaves of sunflower (Helianthus annuus) plants. In addition, we discuss the inefficiency of the key enzyme Rubisco and analyze data concerning the productivity of C3 vs. C4 crop species (sunflower vs. maize, Zea mays). Warburg's discovery inaugurated a research agenda in the biochemistry of photosynthetic CO2 assimilation that continues to the present. In addition, we briefly discuss Warburg's model of metabolic processes in cancer, net primary production (global photosynthesis) with respect to climate change, trees and other land plants as CO2 removers, and potential climate mitigators in the Anthropocene.