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.

The enhanced vegetation productivity driven by increased concentrations of carbon dioxide (CO2) [i.e., the CO2 fertilization effect (CFE)] sustains an important negative feedback on climate warming, but the temporal dynamics of CFE remain unclear. Using multiple long-term satellite- and ground-based datasets, we showed that global CFE has declined across most terrestrial regions of the globe from 1982 to 2015, correlating well with changing nutrient concentrations and availability of soil water. Current carbon cycle models also demonstrate a declining CFE trend, albeit one substantially weaker than that from the global observations. This declining trend in the forcing of terrestrial carbon sinks by increasing amounts of atmospheric CO2 implies a weakening negative feedback on the climatic system and increased societal dependence on future strategies to mitigate climate warming.

Greening represents a significant increase in vegetation activity. Accurate quantification of global greening in peak growth is vital for quantifying changes in terrestrial productivity. Normalized Difference Vegetation Index (NDVI) is the remote sensing indicator most commonly applied for this purpose. However, there is limited knowledge about how the applications of other improved or newly available products can impact global greening assessments. This study reports the first systematic investigation of the impact of vegetation indicator selection on global greening in peak growth. It examines the period from 2003 to 2017 using six indicators with a spatial resolution of 500 m: Near Infrared Reflectance of terrestrial vegetation (NIRv) and the coupled diagnostic biophysical modeled Gross Primary Production (PML-GPP), together with NDVI, Enhanced Vegetation Index with two bands (EVI2), Leaf Area Index (LAI) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP (MOD-GPP) calculated based on MODIS images. Vegetation trends were estimated using the Mann-Kendall test, the unanimous trends by six vegetation indicators were derived, and the concordance ratio in estimated trends by each pair of indicators was investigated among different biomes and their sensitivity to changes in climate were investigated. We found that the estimated greening and browning varied between 12-23% and 2-13% of the vegetated areas, respectively. There was more net greening estimated from NDVI and MOD-GPP (around 19%) compared to the other indicators (8-11%). The concordance ratio between EVI2 and NIRv was much higher (>94%) than other combinations (61-76%) at the global scale. The concordance ratio between one specific indicator and the other indicators gradually increased with its change magnitude. Unanimous results from these six indicators were exhibited in less than two-fifths (38.71%) of vegetated areas. Apart from the EVI2 and NIRv, the concordance ratio varied among different biomes: high in cropland (67-81%), but low in deciduous needle-leaf forest (51-62%) and very low in evergreen broadleaf forest between PML-GPP and others (37-49%). The diverse sensitivity to changes in climate contributed to the discrepancies: greening was more pronounced during warming conditions for the GPP products when compared with the greenness indices. At the global scale, the concordance ratio was higher during cooling conditions among all the indicators except between PML-GPP. Datasets supporing these findings are available online at https://github.com/FuzhouSIRC/Greening_6indicators.2

Here, we present a conceptual and quantitative model to describe the role of the Cytochrome b(6)f complex in controlling steady-state electron transport in C-3 leaves. The model is based on new experimental methods to diagnose the maximum activity of Cyt b(6)f in vivo, and to identify conditions under which photosynthetic control of Cyt b(6)f is active or relaxed. With these approaches, we demonstrate that Cyt b(6)f controls the trade-off between the speed and efficiency of electron transport under limiting light, and functions as a metabolic switch that transfers control to carbon metabolism under saturating light. We also present evidence that the onset of photosynthetic control of Cyt b(6)f occurs within milliseconds of exposure to saturating light, much more quickly than the induction of non-photochemical quenching. We propose that photosynthetic control is the primary means of photoprotection and functions to manage excitation pressure, whereas non-photochemical quenching functions to manage excitation balance. We use these findings to extend the Farquhar et al. (Planta 149:78-90, 1980) model of C-3 photosynthesis to include a mechanistic description of the electron transport system. This framework relates the light captured by PS I and PS II to the energy and mass fluxes linking the photoacts with Cyt b(6)f, the ATP synthase, and Rubisco. It enables quantitative interpretation of pulse-amplitude modulated fluorometry and gas-exchange measurements, providing a new basis for analyzing how the electron transport system coordinates the supply of Fd, NADPH, and ATP with the dynamic demands of carbon metabolism, how efficient use of light is achieved under limiting light, and how photoprotection is achieved under saturating light. The model is designed to support forward as well as inverse applications. It can either be used in a stand-alone mode at the leaf-level or coupled to other models that resolve finer-scale or coarser-scale phenomena.

High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun-induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high-temperature experiment, Temperature Free-Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0 degrees C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high-temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high-temperature stress (partial correlation r = 0.60 and -0.23). Near-infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (phi(F)) signals. phi(F) further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that phi(F) outperformed SIF yield in responding to physiological stress (r = -0.37). Our findings highlight that phi(F) sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. phi(F), if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.