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Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.

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.

Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R2 = 0.76) and APAR and GPP (R2 = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R2 = 0.83 and R2 = 0.88, respectively. Relationships between LUEp (=GPP/APAR) and LUEf (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R2 = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUEp. These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales.

Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRVP) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIRVP captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIRVP showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIRVP data for vegetation monitoring and also indicate the potential of using SIF and NIRVP in combination to extract physiological information from SIF.

International audience; 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.

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.

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 R 2 = 0.67 and RMSE = 2.58 gC m − 2 d − 1 for GPP, and R 2 = 0.62 and RMSE = 0.78 mm d − 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 R 2 = 0.93 and RMSE = 229 gC m − 2 y − 1 for GPP, and R 2 = 0.90 and RMSE = 118 mm y − 1 for ET, respectively. Over the period of 2001–2011, BESS quantified the mean global GPP and ET as 122 ± 25 PgC y − 1 and 65 × 10 3 ± 11 × 10 3 km 3 y − 1 , respectively, with a significant ascending GPP trend by 0.27 PgC y − 2 ( p

article Vegetation indices are important remotely sensed metrics for ecosystem monitoring and land surface process assessment, among which Normalized Difference Vegetation Index (NDVI) has been most widely used. The newly launched Landsat 8 Operational Land Imager (OLI) sensor, together with its predecessor Landsat 7 Enhanced Thematic Mapper Plus (ETM+), provides continuous earth observations with an 8-day interval. The design improvements of the new sensor, including narrower near-infrared waveband, higher signal-to-noise ratio (SNR), and greater radiometric sensitivity highlight the need for investigating the land surface observation properties, especially its consistency with data from its predecessors and other satellite sensors. This study aims to evaluate the characteristics of Landsat 8 OLI-derived NDVI against Landsat 7 ETM+ by cross-comparison and by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI)-derived NDVIs as well as in-situ NDVI measurements. Simulations of Top of Atmosphere (TOA) reflectance and surface reflectance of broadleaf trees and water were conducted for Landsat 8 OLI, Landsat 7 ETM+, and MODIS in order to evaluate the impact of band pass difference on NDVI calculation. Four consecutive pairs of Landsat 8 OLI and Landsat 7 ETM+ data over China and Korea were examined, and NDVIs derived from TOA reflectance and surface reflectance by three atmospheric correction methods were evaluated. Both simula- tions and comparisons showed that NDVIs derived from atmospherically-corrected surface reflectance had good consistency, while the simulation showed that the agreement varied with atmospheric characteristics. The four pairs of Landsat 8 OLI and Landsat 7 ETM+ NDVI had a mean bias error within ±0.05, and R 2 from 0.84 to 0.98.

article i nfo Emerging near-surface remote sensing techniques have advanced our ability to monitor forest canopy phenolo- gy. Thus far, however, little effort has been made to monitor the phenologies of the various canopies of multi- layer forestsseparately,despite theirimportance inregulating forestbiogeochemicalcycles. Here we reportphe- nological changesinmulti-layercanopiesofdeciduousbroadleafandevergreenneedleleaf forestsintheRepublic of Korea during the spring of 2013. We installed light-emitting diode (LED) sensors at four different canopy heights at two sites to measure the normalized difference vegetation index (NDVI) using red and near-infrared (NIR) spectral reflectance and to estimate leaf area index (LAI) using the blue band gap fraction. LED-sensors identified leaf-out dates of over- and understory canopies at both sites; leaves unfolded 8-11 days earlier in the understory canopy than the overstory canopy. At the deciduous forest site, LED-NDVI failed to capture the leaf-out date in the overstory canopy, because all four LED-sensors started to see green-up from the understory canopy while the overstory canopy was leafless. LED-LAI identified different leaf-out dates for the over- and un- derstory canopy, because the gap fraction was measured explicitly for each canopy layer. In the evergreen forest site, LED-NDVI signals between the top of the tower and beneath the overstory canopy were decoupled be- cause of the dense evergreen overstory canopy. Both LED-NDVI and LED-LAI identified new needle expan- sion in the overstory canopy and understory canopy development. MODIS NDVI agreed well with LED-NDVI data (R 2 = 0.96, RMSE = 0.04) at the deciduous forest site, and we discovered that understory canopy de- velopment determined the onset of greenness based on MODIS NDVI data. LED-LAI data agreed well with independent estimates from the other instruments, indicating that LED-sensors could be used to monitor multi-layer canopy LAI. Continuous, in-situ observation of multi-layer canopy phenology will aid in the in- terpretation of satellite remote sensing phenology products and improve land surface models that adopt a multi-layer canopy model.

Statistical gross primary productivity (GPP) estimation from remote sensing observations has mostly been attempted on the basis of multispectral observations. To make full use of the information contained in vegetation spectra, however, hyperspectral observations should be used in combination with appropriate multivariate methods. Nevertheless, only very few previous studies attempted to estimate GPP directly from hyperspectral observations and did so on the basis of reflectance, with only a limited number of temporally discontinuous observations. In this study, we used long-term, continuous, half-hourly hyperspectral observations covering the visible and near-infrared spectral range to estimate GPP directly from upwelling irradiance using partial least square (PLS) regression in a rice paddy. To gain a better understanding of processes underlying the PLS estimation, we used extensive complementary field observations to run process-based simulations using the SCOPE model. We then applied PLS regression to the simulated hyperspectral data in the same way as for the observations and disentangled contributions related to relevant physiological processes, namely sun-induced chlorophyll fluorescence (SIF) and xanthophyll cycle-related spectral changes (XC). We found that upwelling hyperspectral irradiance in the visible and near-infrared spectral range predicted GPP better than reflectance. Furthermore, PLS-based GPP estimates outperformed both far-red SIF and widely used vegetation index-based methods. However, the most relevant information for the observation-based PLS-models was not clearly related to XC or SIF as the near-infrared spectral range showed comparable performance. Also, the simple average of upwelling irradiance over the 850–900 nm range outperformed the other non-multivariate approaches, including far-red SIF. These results held for the evaluation in terms of the seasonal variation of GPP, while there was apparently a small contribution of SIF and XC for the diurnal variation. The simulation-based analysis showed that SIF and XC contributed useful information to both GPP and photosynthetic light use efficiency (LUE) estimates at both seasonal and diurnal time scales. The strongest unique contribution from either SIF or XC, however, was to the diurnal variation of GPP and XC showed considerably better performance than SIF. We did not find improvements when combining the spectral regions of XC (500–570 nm) and SIF (650–800 nm) to estimate GPP. While SIF showed improvements when combined with the remaining spectral information excluding XC, this was not the case for XC. Our approach combines the strengths of process-based modeling with multivariate statistical analysis to improve our understanding of the usable information content in vegetation spectra and is highly relevant for further developing suitable methods for GPP estimation at large scales.

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.

Clumping index, the measure of foliage grouping relative to a random distribution of leaves in space, is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. In this study, we retrieve the clumping index using the original 275 m resolution data of the Multi-angle Imaging SpectroRadiometer (MISR) instrument over a set of sites representing diverse biomes and different canopy structures. Also for the first time, the MISR derived clumping index values are directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. Our results illustrate that MISR data with 275 m allow clumping index estimates at much more pertinent scales (both spatial and temporal) than previous maps from Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) for modeling local carbon and energy fluxes.