Several global gross primary production (GPP) and evapotranspiration (ET) remote sensing products exist, mainly provided by machine-learning (e.g. MPI-BGC) and semi-empirical (e.g. MODIS) approaches. ...Process-based approaches have the advantage of representing the atmosphere-vegetation-soil system and associated fluxes as an organic integration, but their sophistication results in a lack of high spatiotemporal resolution continuous products. Targeting this gap, we reported a new set of global 8-day composite 1-km resolution GPP and ET products from 2000 to 2015, using a simplified process-based model, the Breathing Earth System Simulator (BESS). BESS couples atmosphere and canopy radiative transfer, photosynthesis and evapotranspiration, and uses MODIS atmosphere and land data and other satellite data sources as inputs. We evaluated BESS products against FLUXNET observations at site scale (total of 113 sites, 742 site years), and against MPI-BGC products at global scale. At site scale, BESS 8-day products agreed with FLUXNET observations with R2=0.67 and RMSE=2.58gCm−2d−1 for GPP, and R2=0.62 and RMSE=0.78mmd−1 for ET, respectively, and they captured a majority of seasonal variability, about half of spatial variability, and a minority of interannual variability in FLUXNET observations. At global scale, BESS mean annual sum GPP and ET maps agreed with MPI-BGC products with R2=0.93 and RMSE=229gCm−2y−1 for GPP, and R2=0.90 and RMSE=118mmy−1 for ET, respectively. Over the period of 2001–2011, BESS quantified the mean global GPP and ET as 122±25PgCy−1 and 65×103±11×103km3y−1, respectively, with a significant ascending GPP trend by 0.27PgCy−2 (p<0.05), similar to MPI-BGC products as well. Overall, BESS GPP and ET estimates were comparable with FLUXNET observations and MPI-BGC products. The process-based BESS can serve as a set of independent GPP and ET products from official MODIS GPP and ET products.
•Present global 8-day 1-km GPP and ET products over 2000–2015 using BESS•Achieved similar accuracy with MODIS products using FLUXNET2015 as reference•Achieved better consistency with MPI-BGC than MODIS at global and decadal scale•Can serve as independent GPP and ET products from official MODIS products
Incident shortwave radiation (SW), photosynthetically active radiation (PAR), and diffuse PAR (PARdif) at the land surface drive a multitude of processes related to biosphere-atmosphere interactions ...and play a critical role in the Earth climate system. Previous global solar radiation products were spatially coarse (> 50-km resolution) or temporally short (a few years), which hindered scaling-up ground based observations of the land surface processes into regional, continental, and global scales across multiple time scales. Here, we report Breathing Earth System Simulator (BESS) SW, PAR, and PARdif products over the global land surface at a 5 km resolution with 4 day intervals between 2000 and 2016. We combined an atmospheric radiative transfer model with an artificial neural network (ANN) to compute SW, PAR, and PARdif. A series of MODerate Resolution Imaging Spectroradiometer (MODIS) atmosphere and land products were used as inputs to run the ANN. We test the performance of the products using data from 158 (SW), 77 (PAR), and 22 (PARdif) stations collected in the Baseline Surface Radiation Network (BSRN) and flux tower networks, which covered a range of climatic zones from polar to tropical zones. BESS had strong linear relationships with in-situ SW data (R2 = 0.95, relative bias = - 2.3%), PAR (R2 = 0.94, relative bias = 1.7%), and PARdif (R2 = 0.84, relative bias = 0.2%). BESS captured the interannual variability of SW at both the site (a majority of long-term BSRN sites) and continental levels. Over the study period, global annual SW, PAR, and PARdif values did not show any dimming or brightening trends, although these trends appeared at regional levels, e.g. dimming in India. Mean annual SW over the global land surface was 184.8 W m- 2 (875 ZJ yr- 1, zetta = 1021); 46% of SW was partitioned to PAR, which was further split into direct (59%) and diffuse (41%) components. The developed products will be useful in solar energy harvesting research and will improve water, carbon, and energy flux estimates of terrestrial ecosystems from local to the global scales.
•Soil organic carbon (SOC) was measured in an urban park.•SOC stock showed ten-fold difference across different land cover types.•Average topsoil SOC concentration increased 256% over the ten ...years.•Land use change mainly contributed to increase in topsoil SOC concentration.
Urban parks offer valuable ecosystem services to citizens and they have long been recognized for their recreational service; however, less attention has been paid to their carbon sequestration value. Here, we report on soil organic carbon (SOC) stocks in an urban park, Seoul Forest Park, which was built in 2004. We had two objectives: (1) to estimate SOC stocks (to a depth of 1m) in different land-cover types (wetland, forest, lawn, and bare soil) and (2) quantify the change in the SOC concentration in topsoil in different land-use types over a 10 year period (2003–2013). We found a tenfold difference in SOC stocks across the different land-cover types within the park. Wetland soils had the highest stocks of SOC (13.99±1.05kgm−2), followed by forest, lawn, and bare soils. We found that a “cultural layer” that preserved previous land use history located deep in the soil profile substantially increased SOC stocks in the wetland. SOC concentrations in the topsoil were approximately three times higher in 2013 than in 2003 (256±130%). The normalized difference vegetation index (NDVI) derived from MODIS and Landsat satellite images revealed that land-use history, expansion of plant areas and growth of plants could explain the increase in SOC concentrations in topsoil over the 10 year period. These findings imply that urban park soils could act as a carbon sink, and understanding the land-use history and the choice of land-cover types in park planning can substantially influence the carbon budget of urban parks.
Quantifying global terrestrial photosynthesis is essential to understanding the global carbon cycle and the climate system. Remote sensing has played a pivotal role in advancing our understanding of ...photosynthesis from leaf to global scale; however, substantial uncertainties still exist. In this review, we provide a historical overview of theory, modeling, and observations of photosynthesis across space and time for decadal intervals beginning in the 1950s. Then we identify the key uncertainties in global photosynthesis estimates, including evaluating light intercepted by canopies, biophysical forcings, the structure of light use efficiency models and their parameters, like photosynthetic capacity, and relationships between sun-induced chlorophyll fluorescence and canopy photosynthesis. Finally, we review new opportunities with big data and recently launched or planned satellite missions.
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•Reviewed history of global photosynthesis since 1950s•Reviewed uncertainties in remote sensing of global photosynthesis•Reviewed emerging opportunities with recent and new satellite missions
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 R2 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.
•The escape ratio of near-infrared SIF can be estimated using NIRV and fPAR.•The approach applies broadly, including sparse canopies with bright soil backgrounds.•The approach allows estimation of total emitted SIF from directional SIF data.
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•Developed a system that monitors VIs, fPAR, and LAI automatically.•Displayed linear responses to light intensities in the lab and rice field.•Agreed to a reference spectrometer and ...LAI-2200.•Tracked green LAI by integrating LED and micro-camera.
Continuous monitoring of vegetation indices (VIs) the fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) through satellite remote sensing has advanced our understanding of biosphere–atmosphere interactions. Substantial efforts have been put into monitoring individual variables in the field, but options to concurrently monitor VIs, fPAR, and LAI in-situ have been lacking. In this paper, we present the Smart Surface Sensing System (4S), which automatically collects, transfers and processes VIs, fPAR and LAI data streams. The 4S consists of a microcomputer, controller and camera, a multi-spectral spectrometer built in with a light-emitting diode (LED) and an internet connection. Lab testing and field observations in a rice paddy site that experiences wet summer monsoon seasons confirmed the linear response of 4S to light intensities in the blue, green, red and near-infrared spectral channels, with wide ranging temperatures and humidity having only a minor impact on 4S throughout the growing season. Applied over an entire rice growing season (day of year DOY 120 - 248), VIs and fPAR from 4S were linearly related to corresponding VIs from a reference spectrometer (R2 = 0.98; NDVI, R2 = 0.96; EVI) and the LAI-2200 instrument (R2 = 0.76), respectively. Integration of gap fraction-based LAI from LED sensors and a green index from the micro-camera allowed tracking of the seasonality of green LAI. The continuous and diverse nature of 4S observations highlights its potential for evaluating satellite remote sensing products. We believe that 4S will be useful for the expansion of ecological sensing networks across multiple spatial and temporal scales.
► The sensitivity of vegetation phenology to climate change varies among biomes. ► Key weaknesses in our current understanding of phenology drivers are identified. ► Phenology controls many feedbacks ...of vegetation to the climate system. ► The size and seasonality of these feedbacks will shift as phenology shifts. ► Models that couple the land surface to the climate system need better phenology.
Vegetation phenology is highly sensitive to climate change. Phenology also controls many feedbacks of vegetation to the climate system by influencing the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, CO2 and biogenic volatile organic compounds. In this review, we first discuss the environmental drivers of phenology, and the impacts of climate change on phenology, in different biomes. We then examine the vegetation-climate feedbacks that are mediated by phenology, and assess the potential impact on these feedbacks of shifts in phenology driven by climate change. We finish with an overview of phenological modeling and we suggest ways in which models might be improved using existing data sets. Several key weaknesses in our current understanding emerge from this analysis. First, we need a better understanding of the drivers of phenology, particularly in under-studied biomes (e.g. tropical forests). We do not have a mechanistic understanding of the role of photoperiod, even in well-studied biomes. In all biomes, the factors controlling senescence and dormancy are not well-documented. Second, for the most part (i.e. with the exception of phenology impacts on CO2 exchange) we have only a qualitative understanding of the feedbacks between vegetation and climate that are mediated by phenology. We need to quantify the magnitude of these feedbacks, and ensure that they are accurately reproduced by models. Third, we need to work towards a new understanding of phenological processes that enables progress beyond the modeling paradigms currently in use. Accurate representation of phenological processes in models that couple the land surface to the climate system is particularly important, especially when such models are being used to predict future climate.
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 (fesc) 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 (R2 = 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 fesc, 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 fesc, 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.
•A mechanistic decomposition of canopy SIF for three in situ crop datasets.•The canopy structure and radiation factor outperforms SIF for GPP estimation.•Canopy escape fraction of SIF correlates with photosynthetic light use efficiency.•Correcting SIF for canopy scattering improves the correlation to APAR but not GPP.•Estimates of physiological SIF yield show no clear seasonal patterns.
Solar-induced chlorophyll fluorescence (SIF) provides us with new opportunities to understand the physiological and structural dynamics of vegetation from leaf to global scales. However, the ...relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. Here, we report the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, we investigated the relationships of light use efficiency (LUEp), chlorophyll fluorescence yield (ΦF), and the fraction of emitted SIF photons escaping from the canopy (fesc) separately. We found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. Also, there were differential responses of LUEp and ΦF to air temperature. While LUEp reached saturation at high air temperatures, ΦF did not saturate. We found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level ΦF (R2 = 0.84) implying that ΦF could be more closely related to pigment pool changes rather than LUEp. In addition, we found that the fesc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUEp to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forest.
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•Assessed relationships between GPP and SIF via LUEP, ΦF, fesc in an ENF site.•Found a strongly non-linear relationship between GPP and SIF.•While LUEp reached saturation at high air temperatures, ΦF did not saturate.•ΦF-CCI relationship was strongly linear.•fesc enhanced SIF-GPP correlation.
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•Measured soil respiration (Rs) in six different land cover types in an urban park.•There is a three-fold difference in annual Rs across different land cover types.•Soil organic ...carbon stocks explain spatial variations in annual Rs.•Soil temperature explains temporal variations in Rs.•Certain land cover types exhibit a drought-reduced sensitivity of Rs to soil temperature.
Soil respiration (Rs) determines land surface carbon balance; however, there have been few studies that measured Rs in heterogeneous urban landscapes. Here, we investigated the spatial and temporal variations in Rs in six land cover types (mixed forest, deciduous broadleaf forest, evergreen needleleaf forest, lawn, wetland, and bare land) in Seoul Forest Park, Republic of Korea, between March 2013 and September 2014, which included a wet (2013) and an extremely dry (2014) summer. Spatially, there was a three-fold difference (0.48–1.45kgCm−2) in annual Rs among the six land cover types. The soil organic carbon stock at a depth of 0.1m explained 72% of the spatial variation in the annual Rs across the land cover types. During the entire study period, the soil temperature explained 82–97% of the temporal variation in Rs among different land cover types. Comparing the two summers, the 2014 drought only resulted in a decrease in Rs in the lawn plots (25%), which was driven by a reduction in the leaf area index and the fine root density. The temperature sensitivity of Rs in 2014 (dry summer) compared to 2013 (wet summer) was significantly lower in mixed forest, deciduous broadleaf forest, and lawn, and did not change in evergreen needleleaf forest, wetland, or bare land. The differences in Rs in these drought responses highlight the importance of the careful selection of land cover type during park planning to better manage carbon cycles.
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