市井 和仁

Abstract As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Recent advances in satellite-borne optical sensors led to important developments in the monitoring of tropical ecosystems in Asia, which have been strongly affected by recent anthropogenic activities and climate change. Based on our feasibility analyses conducted in Indonesia in Sumatra and Sarawak, Malaysia in Borneo, we discuss the current situation, problems, recent improvements, and future tasks regarding plant phenology observations and land-cover and land-use detection. We found that the Multispectral Instrument (MSI) on board the Sentinel-2A/2B satellites with a 10-m spatial resolution and 5-day observational intervals could be used to monitor phenology among tree species. For the Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary satellite with a 1,000-m spatial resolution and 10-min observational intervals, we found that the time-series in vegetation indices without gaps due to cloud contamination may be used to accurately detect the timing and patterns of phenology among tree species, although the spatial resolution of the sensor requires further improvement. We also found and validated that text and pictures with geolocation information published on the Internet, and historical field notes could be used for ground-truthing land cover and land use in the past and present time. The future development of both high frequency (≤ 10 min) and high spatial resolution (≤ 10 m) optical sensors aboard satellites is expected to dramatically improve our understanding of ecosystems in the tropical Asia.

The fate of a boreal forest may depend on the trend in its normalized difference vegetation index (NDVI), such as whether the NDVI has been increasing significantly over the past few decades. In this study, we analyzed the responses of two Siberian larch forests at Spasskaya Pad and Elgeeii in eastern Siberia to various waterlogging-induced disturbances, using satellite-based NDVI and meteorological data for the 2000-2019 period. The forest at Spasskaya Pad experienced waterlogging (i.e. flooding events caused by abnormal precipitation) during 2005-2008 that damaged canopy-forming larch trees and increased the abundance of water-resistant understory vegetation. By contrast, the forest at Elgeeii did not experience any remarkable disturbance, such as tree dieback or changes in the vegetation community. Significant increasing NDVI trends were found in May and June-August at Elgeeii (p < 0.05), whereas no significant trends were found at Spasskaya Pad (p > 0.05). NDVI anomalies in May and June-August at Elgeeii were significantly associated with precipitation or temperature depending on the season (p < 0.05), whereas no significant relationships were found at Spasskaya Pad (p > 0.05). Thus, the 20 year NDVI trend and NDVIerature-precipitation relationship differed between the two larch forests, although no significant trends in temperature or precipitation were observed. These findings indicate that nonsignificant NDVI trends for Siberian larch forests may reflect waterlogging-induced dieback of larch trees, with a concomitant increase in water-resistant understory vegetation.

<title>Abstract</title>The measurements of one of the major greenhouse gases, carbon dioxide (CO₂), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by a three-way partnership between the Japan Aerospace Exploration Agency (JAXA), the Ministry of Environment (MoE) and the National Institute for Environmental Studies (NIES), and the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO₂ fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO₂ data have also made significant progress. We attempt, for the first time, to evaluate the CO₂ fluxes simulated by an earth system model (MIROC-ES2L) and the fluxes estimated by an inverse model (MIROC4-Inv) using in situ data by comparing with GOSAT and OCO-2 observations. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating total column CO₂ mole fraction (XCO₂) that are sampled at the time and location of the satellite measurements. Both the ACTM simulations agreed well with the GOSAT and OCO-2 satellite observations, within 2 ppm for the spatial maps and time evolutions of the zonal mean distributions. Our results suggest that the inverse model using in situ data is more consistent with the OCO-2 retrievals, compared with those of the GOSAT XCO₂ data due to the higher accuracy of the former. This suggests that the MIROC4-Inv fluxes are of sufficient quality to evaluate MIROC-ES2L simulated fluxes. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO₂ fluxes, compared with that from the ACTM_InvF. This difference is revealed by greater XCO₂ differences for ACTM_ES2LF vs GOSAT, compared with those of ACTM_InvF vs GOSAT. Using remote sensing–based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO₂ measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities.

Rising atmospheric CO(2)concentration ([CO2]) enhances photosynthesis and reduces transpiration at the leaf, ecosystem, and global scale via the CO(2)fertilization effect. The CO(2)fertilization effect is among the most important processes for predicting the terrestrial carbon budget and future climate, yet it has been elusive to quantify. For evaluating the CO(2)fertilization effect on land photosynthesis and transpiration, we developed a technique that isolated this effect from other confounding effects, such as changes in climate, using a noisy time series of observed land-atmosphere CO(2)and water vapor exchange. Here, we evaluate the magnitude of this effect from 2000 to 2014 globally based on constraint optimization of gross primary productivity (GPP) and evapotranspiration in a canopy photosynthesis model over 104 global eddy-covariance stations. We found a consistent increase of GPP (0.138 0.007% ppm(-1); percentile per rising ppm of [CO2]) and a concomitant decrease in transpiration (-0.073% 0.006% ppm(-1)) due to rising [CO2]. Enhanced GPP from CO(2)fertilization after the baseline year 2000 is, on average, 1.2% of global GPP, 12.4 g C m(-2)yr(-1)or 1.8 Pg C yr(-1)at the years from 2001 to 2014. Our result demonstrates that the current increase in [CO2] could potentially explain the recent land CO(2)sink at the global scale.

Recent advancements in new generation geostationary satellites have facilitated the application of their datasets to terrestrial monitoring. In this application, geolocation accuracy is an essential issue because land surfaces are generally heterogeneous. In the case of the Advanced Himawari Imager (AHI) onboard Himawari-8, geometric correction of the Himawari Standard Data provided by the Japan Meteorological Agency (JMA data) was conducted using thermal infrared band with 2km spatial resolution. Based on JMA data, the Center for Environmental Remote Sensing (CEReS) at Chiba University applied a further geometric correction using a visible band with 500m spatial resolution and released a dataset (CEReS data). JMA data target more general users mainly for meteorological observations, whereas CEReS data aim at terrestrial monitoring for more precise geolocation accuracy. The objectives of this study are to clarify the temporal and spatial variations of geolocation errors in these two datasets and assess their stability for unexpected large misalignment. In this study, the temporal tendencies of the relative geolocation difference between the two datasets were analyzed, and temporal fluctuations of band 3 reflectances of JMA data and CEReS data at certain fixed sites were investigated. A change in the geolocation trend and occasional shifts greater than 2 pixels were found in JMA data. With improved image navigation performance, the geolocation difference was decreased in CEReS data, suggesting the high temporal stability of CEReS data. Overall, JMA data showed an accuracy of less than 2 pixels with the spatial resolution of band 3. When large geolocation differences were observed, anomalies were also detected in the reflectance of JMA data. Nevertheless, CEReS data successfully corrected the anomalous errors and achieved higher geolocation accuracy in general. As CEReS data are processed during the daytime due to the availability of visible bands, we suggest the use of CEReS data for effective terrestrial monitoring during the daytime.

Abstract. FLUXNET assembles globally-distributed eddy covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary in order to obtain global-scale estimates of biosphere-atmosphere exchange from the flux tower network. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVM), we provide here a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing datasets, and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent among FLUXCOM and DGVM ensembles (R2 &gt; 0.94 at 1° spatial resolution) while the majority of DGVMs are outside the FLUXCOM range for 70 % of the land surface. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations of independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2 ~ 0.75) than with GPP from DGVMs (mean global pixel wise R2 ~ 0.6). Seasonal variations of FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2 &gt; 0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which uses also meteorological inputs shows a strong co-variation of interannual patterns with inversions (R2 = 0.88 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred gC m2 yr−1. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site-history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth Observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

© 2020 by the authors. Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (Re), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (GWT) by incorporating LST and precipitation into the Keetch-Byram Drought Index (KBDI). The GWT was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (R2) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (Re) based on a relationship between the in situ GWT and flux-tower-observed Re. Fire emissions are a large direct source of CO2 from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) Re, GPP, FE, and NBE from whole peatlands in the study area (6°N-11°S, 95-141°E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. Re and FE were influenced by El Niño, and the value of the NBE was also high in the El Niño period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of Re with delineations of types of anthropogenic impacts (e.g., drainages and fires).

The human impact on life on Earth has increased sharply since the 1970s, driven by the demands of a growing population with rising average per capita income. Nature is currently supplying more materials than ever before, but this has come at the high cost of unprecedented global declines in the extent and integrity of ecosystems, distinctness of local ecological communities, abundance and number of wild species, and the number of local domesticated varieties. Such changes reduce vital benefits that people receive from nature and threaten the quality of life of future generations. Both the benefits of an expanding economy and the costs of reducing nature’s benefits are unequally distributed. The fabric of life on which we all depend—nature and its contributions to people—is unravelling rapidly. Despite the severity of the threats and lack of enough progress in tackling them to date, opportunities exist to change future trajectories through transformative action. Such action must begin immediately, however, and address the root economic, social, and technological causes of nature’s deterioration.

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47µm and 0.51µm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET.

<title>Abstract</title> Spectral vegetation index time series data, such as the normalized difference vegetation index (NDVI), from moderate resolution, polar-orbiting satellite sensors have widely been used for analysis of vegetation seasonal dynamics from regional to global scales. The utility of these datasets is often limited as frequent/persistent cloud occurrences reduce their effective temporal resolution. In this study, we evaluated improvements in capturing vegetation seasonal changes with 10-min resolution NDVI data derived from Advanced Himawari Imager (AHI), one of new-generation geostationary satellite sensors. Our analysis was focused on continuous monitoring sites, representing three major ecosystems in Central Japan, where <italic>in situ</italic> time-lapse digital images documenting sky and surface vegetation conditions were available. The very large number of observations available with AHI resulted in improved NDVI temporal signatures that were remarkably similar to those acquired with <italic>in situ</italic> spectrometers and captured seasonal changes in vegetation and snow cover conditions in finer detail with more certainty than those obtained from Visible Infrared Imaging Radiometer Suite (VIIRS), one of the latest polar-orbiting satellite sensors. With the ability to capture <italic>in situ</italic>-quality NDVI temporal signatures, AHI “hypertemporal” data have the potential to improve spring and autumn phenology characterisation as well as the classification of vegetation formations.

© 2019 IEEE. Peatland is a natural carbon reservoir in terrestrial ecosystem. The ground water table in peatland is a key factor for carbon exchange through soil decomposition or carbon sink. Especially, Indonesia has the biggest peatland area in Asia. This study focused on the carbon dioxide budget between emissions by ecosystem respiration including fire event and the absorption by photosynthesis. As the result, the annual average of net biome ecosystem carbon dioxide exchange during 12 years were reached to 195.03 MtC/yr. over the three times of CO2 from fire emissions were emitted by ecosystem respiration from whole peatlands in Indonesia.

An integrated understanding of the biogeochemical consequences of climate extremes and land use changes is needed to constrain land-surface feedbacks to atmospheric CO2 from associated climate change. Past assessments of the global carbon balance have shown particularly high uncertainty in Southeast Asia. Here, we use a combination of model ensembles to show that intensified land use change made Southeast Asia a strong source of CO2 from the 1980s to 1990s, whereas the region was close to carbon neutral in the 2000s due to an enhanced CO2 fertilization effect and absence of moderate-to-strong El Niño events. Our findings suggest that despite ongoing deforestation, CO2 emissions were substantially decreased during the 2000s, largely owing to milder climate that restores photosynthetic capacity and suppresses peat and deforestation fire emissions. The occurrence of strong El Niño events after 2009 suggests that the region has returned to conditions of increased vulnerability of carbon stocks.

The increasing strength of land CO2 uptake in the 2000s has been attributed to a stimulating effect of rising atmospheric CO2 on photosynthesis (CO2 fertilization). Using terrestrial biosphere models, we show that enhanced CO2 uptake is induced not only by CO2 fertilization but also an increasing uptake by plant regrowth (accounting for 0.33 ± 0.10 Pg C/year increase of CO2 uptake in the 2000s compared with the 1960s–1990s) with its effect most pronounced in eastern North America, southern-eastern Europe, and southeastern temperate Eurasia. Our analysis indicates that ecosystems in North America and Europe have established the current productive state through regrowth since the 1960s, and those in temperate Eurasia are still in a stage from regrowth following active afforestation in the 1980s–1990s. As the strength of model representation of CO2 fertilization is still in debate, plant regrowth might have a greater potential to sequester carbon than indicated by this study.

The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In this paper, we analyze the spatiotemporal variations in the terrestrial water storage (TWS) of the ACTR and three of the largest pan-Arctic river basins (Lena, Mackenzie, Yukon). To do this, we utilize monthly Gravity Recovery and Climate Experiment (GRACE) data from 2002 to 2016. Together with global land reanalysis, and river runoffdata, we identify declining TWS trends throughout the ACTR that we attribute largely to increasing evapotranspiration driven by increasing summer air temperatures. In terms of regional changes, large and significant negative trends in TWS are observed mainly over the North American continent. At basin scale, we show that, in the Lena River basin, the autumnal TWS signal persists until the spring of the following year, while in the Mackenzie River basin, the TWS level in the autumn and winter has no significant impact on the following year. As expected global warming is expected to be particularly significant in the northern regions, our results are important for understanding future TWS trends, with possible further decline.

北アフリカから中東、中央アジアを経てモンゴルに至る地域は世界最大の乾燥地帯であり、人々の多くは気象条件への依存性の強い乾燥地農業や牧畜を生業としている。このような地域において、温暖化による気候変化がもたらす気温・降水量などの変化は、耕作・牧畜適性に影響を与え、生活の基盤を脅かす重大な問題である。<br><br>IPCCの第五次評価報告書などによれば、気候モデルの平均値では、今世紀最後の20年間を1986-2005年と比べた場合、シナリオによらず北米大陸南部～南米大陸北部、アフリカ南部、地中海周辺で乾燥化が進み、北アフリカ～モンゴルの乾燥地では、カスピ海周辺を除いて、横ばいあるいは若干の湿潤化が予測されている。<br><br>ここでは、一つ一つのモデルのデータを解析し、それぞれのモデルの中でどのようなプロセスが生じているかを理解し、この地域の将来変化について考察する。<br><br>まず、MIROC-ESMを対象に、低位安定（RCP2.6）および高位安定シナリオ（RCP8.5）実験（2006-2100）を解析対象とした。解析の際は、まず月平均から年平均値を計算し、初期値の異なる３メンバーの平均を取って用い、時間（年）に対する線形回帰式の傾きを算出する。<br><br>まず、降水量は、地中海沿岸からアラビア半島を経てイラン高原に至る地域で減少しており、その他の地域では増加していた。この傾向は、RCP8.5でより強かった。次に、純一次生産量（NPP）を見ると、RCP2.6では、アラビア半島、イラン高原、アナトリア半島で減少しており、アジア―アフリカ乾燥地域に含まれるその他の地域では、やや増加していた。またサハラ砂漠南縁部では増加がみられた。この傾向はRCP8.5ではさらに強くなる。全体的に、NPPは降水量とよく対応しており、乾燥地においては降水量が生産量を決めていることが確認できる。<br><br>次に、NPPのうち、C3草本植物とC4草本植物のみのものに注目してみてみると、RCP2.6ではC3草本のNPPはあまり変化がないところが多いが、モンゴル北中部では顕著に減少している。一方、RCP8.5では、同地域の減少傾向は弱まる。C4草本については全NPPとよく似た傾向を示している。RCP2.6については、モンゴル北中部ではやや増加しており、これがC3草本が減った分を埋め合わせているため、全NPPには顕著な減少はみられない。RCP8.5では、イラン高原で顕著な減少がみられる一方、モンゴル周辺では顕著な増加がみられる（RCP8.5では、モンゴル周辺の全NPPは増加傾向であった）。

The combinatorial use of geostationary and polar-orbiting satellites is expected to provide a new approach to Earth observation with a wide range of applications; however, differences in the observation conditions and sensor specifications can introduce biases into the outputs of the various satellites. These differences are also known to depend on the land cover type, and this feature of the data requires thorough investigation. This study compared the solar reflective bands measured by the Advanced Himawari Imager (AHI) and an established sensor MODIS onboard Terra satellite. The comparison was made using data collected over a forested region on the Shikoku Island (30 km by 140 km) located in the south of Japan under similar view zenith angles of approximately 40 degrees. The reflectances of the visible and near-infrared bands (four bands) were processed to correct the molecular scattering and ozone absorption effect. A comparison was made before and after the atmospheric correction. Our results showed that the reflectance differences over the region fell mainly within the relevant standard deviations (reflectance variations within the relevant region), except for the green band. The larger difference between the green band reflectances measured by the two sensors was attributed to differences in the band positions. The band-4 of MODIS (green) covers 545-565 nm, whereas the AHI counterpart (band-2) covers 490-530 nm, providing little overlap with MODIS. These results suggested that special caution is needed when using data collected from these two sensors simultaneously or continuously if the green band is involved in the algorithm.

Carbon dioxide (CO2) fluxes by different methods vary largely at global, regional and local scales. The net CO2 fluxes by three bottom-up methods (tower observation (TWR), biogeochemical models (GTM), and a data-driven model (SVR)), and an ensemble of atmospheric inversions (top-down method, INV) are compared in Yakutsk, Siberia for 2004-2013. The region is characterized by highly homogeneous larch forest on a flat terrain. The ecosystem around Yakutsk shows a net sink of CO2 by all the methods (means during 2004-2007 were 10.9 g C m-2 month-1 by TWR, 4.28 g C m-2 month-1 by GTM, 5.62 g C m-2 month-1 and 0.863 g C m-2 month-1 by SVR at two different scales, and 4.89 g C m-2 month-1 by INV). Absorption in summer (June-August) was smaller by three bottom-up methods (ranged from 88.1 to 191.8 g C m-2 month-1) than the top-down method (223.6 g C m-2 month-1). Thus the peak-to-trough amplitude of the seasonal cycle is greater for the inverse models than bottom-up methods. The monthly-mean seasonal cycles agree among the four methods within the range of inter-model variations. The interannual variability estimated by an ensemble of inverse models and a site-scale data-driven model (the max-min range was 35.8 g C m-2 month-1and 34.2 g C m-2 month-1) is more similar to that of the tower observation (42.4 g C m-2 month-1) than those by the biogeochemical models and the large-scale data-driven model (9.5 g C m-2 month-1 and 1.45 g C m-2 month-1). The inverse models and tower observations captured a reduction in CO2 uptake after 2008 due to unusual waterlogging.

© 2017 The Author(s). The powerful El Niño event of 2015-2016 - the third most intense since the 1950s - has exerted a large impact on the Earth&#039;s natural climate system. The column-averaged CO2dry-air mole fraction (XCO2) observations from satellites and ground-based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Niño has contributed to an excess CO2emission from the Earth&#039;s surface (land + ocean) to the atmosphere in the range of 2.4 ± 0.2 PgC (1 Pg = 1015g) over the period of July 2015 to June 2016. The excess CO2flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO2flux anomaly (range: 4.4-6.7 PgC) estimated for the 1997/1998 El Niño. The annual total sink is estimated to be 3.9 ± 0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport.

Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface-atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface-atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/ or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean +/- 1 SD: 0.8 +/- 0.1 PgC yr(-1), positive numbers are sources to the atmosphere), Russia (0.1 +/- 0.4 PgC yr(-1)), East Asia (1.6 +/- 0.3 PgC yr(-1)), South Asia (0.3 +/- 0.1 PgC yr(-1)), Australia (0.2 +/- 0.3 PgC yr(-1)), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of 5.4 +/- 2.0 PgC yr(-1). By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001-2010 indicates that the true value of NCE is a net CO2 source of 4.3 +/- 0.1 PgC yr(-1). This mismatch of nearly 10 PgC yr(-1) highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO(2) data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water-atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over-or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.

Groundwater is an integral component of the water cycle, and it also influences the carbon cycle by supplying moisture to ecosystems. However, the extent and determinants of groundwater-vegetation interactions are poorly understood at the global scale. Using several high-resolution data products, we show that the spatial patterns of ecosystem gross primary productivity and groundwater table depth are correlated during at least one season in more than two thirds of the global vegetated area. Positive relationships, i.e., larger productivity under shallower groundwater table, predominate in moisture-limited dry to mesic conditions with herbaceous and shrub vegetation. Negative relationships, i.e., larger productivity under deeper groundwater, predominate in humid climates with forests, possibly indicating a drawdown of groundwater table due to substantial ecosystem water use. Interestingly, these opposite groundwater-vegetation interactions are primarily associated with differences in vegetation than with climate and surface characteristics. These findings put forth the first evidence, and a need for better representation, of extensive and non-negligible groundwater-vegetation interactions at the global scale.

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8days are reproduced (e.g., r(2)=0.73 and 0.42 for 8day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r(2)=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems(1-3). It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales(3-14). Here we use empirical models based on eddy covariance data(15) and process-based models(16,17) to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance(3-6,9,11,12,14). Our study indicates that spatial climate covariation drives the global carbon cycle response.

Forest ecosystems sequester large amounts of atmospheric CO2, and the contribution from forests in Asia is not negligible. Previous syntheses of carbon fluxes in Asian ecosystems mainly employed estimates of eddy covariance measurements, net ecosystem production (NEP), gross primary production (GPP), and ecosystem respiration (RE); however, to understand the variability within carbon cycles, fluxes such as autotropic respiration (AR), net primary, production (NPP), litterfall, heterotrophic respiration (HR), and soil respiration (SR) need to be analyzed comprehensively in conjunction with NEP, GPP, and RE. Here we investigated the spatial variability of component fluxes of carbon balance (GPP, AR, NPP, litterfall, HR, SR, and RE) in relation to climate factors, between carbon fluxes, and to NEP using observations compiled from the literature for 22 forest sites in monsoon Asia. We found that mean annual temperature (MAT) largely relates to the spatial variability of component fluxes in monsoon Asian forests, with stronger positive effect in the mid-high latitude forests than in the low latitude forests, but even stronger relationships were identified between component fluxes regardless of regions. This finding suggests that the spatial variability of carbon fluxes in monsoon Asia is certainly influenced by climatic factors such as MAT, but that the overall spatial variability of AR, NPP, litterfall, HR, SR, and RE is rather controlled by that of productivity (i.e., GPP). Furthermore, component fluxes of the mid-high and low latitude forests showed positive and negative relationships, respectively, with NEP. Further investigation identified a common spatial variability in NEP and annual aboveground biomass changes with respect to GPP. The relationship between GPP and NEP in the mid-high latitudes implies that productivity and net carbon sequestration increase simultaneously in boreal and temperate forests. Meanwhile, the relationship between GPP and NEP in the low latitudes indicates that net carbon sequestration decreases with productivity, potentially due to the regional contrast in nitrogen depositions and stand age within sub-tropical and tropical forests; however, it requires further data syntheses or modelling investigations for confirmation of its general validity. These unique features of monsoon Asian forest carbon fluxes provide useful information for improving ecosystem model simulations, which still differ in their predictability of carbon flux variability. (C) 2016 The Authors. Published by Elsevier B.V.

We present a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia using multiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food and Agriculture Organization-Forest Resource Assessment (FAO-FRA 2015 country-level inventory estimates), the Emission Database for Global Atmospheric Research (EDGARv4.3), the 'Houghton' bookkeeping model that incorporates FAO-FRA data, an ensemble of 8 state-of-the-art Dynamic Global Vegetation Models (DGVM), and 2 recently published independent studies using primarily remote sensing techniques. The estimates are aggregated spatially to Southeast, East, and South Asia and temporally for three decades, 1980-1989, 1990-1999 and 2000-2009. Since 1980, net carbon emissions from LULCC in Asia were responsible for 20%-40% of global LULCC emissions, with emissions from Southeast Asia alone accounting for 15%-25% of global LULCC emissions during the same period. In the 2000s and for all Asia, three estimates (FAO-FRA, DGVM, Houghton) were in agreement of a net source of carbon to the atmosphere, with mean estimates ranging between 0.24 to 0.41 Pg C yr-1, whereas EDGARv4.3 suggested a net carbon sink of -0.17 Pg C yr-1. Three of 4 estimates suggest that LULCC carbon emissions declined by at least 34% in the preceding decade (1990-2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to include emissions from carbon rich peatlands and land management, such as shifting cultivation and wood harvesting, which appear to be consistently underreported.

The latitudinal gradient of the start of the growing season (SOS) and the end of the growing season (EOS) were quantified in Alaska (61 degrees N to 71 degrees N) using satellite-based and ground-based datasets. The Alaskan evergreen needleleaf forests are sparse and the understory vegetation has a substantial impact on the satellite signal. We evaluated SOS and EOS of understory and tundra vegetation using time-lapse camera images. From the comparison of three SOS algorithms for determining SOS from two satellite datasets (SPOT-VEGETATION and Terra-MODIS), we found that the satellite-based SOS timing was consistent with the leaf emergence of the forest under story and tundra vegetation. The ensemble average of SOS over all satellite algorithms can be used as a measure of spring leaf emergence for understory and tundra vegetation. In contrast, the relationship between the ground-based and satellite-based EOSs was not as strong as that of SOS both for boreal forest and tundra sites because of the large biases between those two EOSs (19 to 26 days). The satellite-based EOS was more relevant to snowfall events than the senescence of understory or tundra. The plant canopy radiative transfer simulation suggested that 84-86% of the NDVI seasonal amplitude could be a reasonable threshold for the EOS determination. The latitudinal gradients of SOS and EOS evaluated by the satellite and ground data were consistent and the satellite derived SOS and EOS were 3.5 to 5.7 days degree(-1) and -2.3 to -2.7 days degree(-1), which corresponded to the spring (May) temperature sensitivity of -2.5 to -3.9 days degrees C-1 in SOS and the autumn (August and September) temperature sensitivity of 3.0 to 4.6 days degrees C-1 in EOS. This demonstrates the possible impact of phenology in spruce forest understory and tundra ecosystems in response to climate change in the warming Artic and sub-Arctic regions. (C) 2016 The Authors. Published by Elsevier Inc.

Greenhouse gases Observing SATellite (GOSAT) is the operational satellite dedicated to atmospheric CO2 observations. Assimilation of data provided by GOSAT is expected to yield reliable CO2 fluxes in semi-arid regions because of frequent observations owing to clear skies. Here we estimated net CO2 flux over semi-arid regions of the Southern Hemisphere using the GOSAT column averaged CO2 (X-CO2) and surface CO2 measurements. Assimilation of GOSAT X-CO2 indicated that semi-arid regions are integral components of recent terrestrial CO2 uptake, accounting for 44% globally. Compared with estimates assimilated from surface measurements, estimates by GOSAT X-CO2 suggest a 50% reduction in the semi-arid CO2 uptake, amounting to 1.1 Pg C yr(-1). Significant estimation differences occurred for South America and South Africa, where the GOSAT makes frequent measurements but where surface CO2 measurements are limited. In comparison, the two estimates varied less in Australia, where more surface measurements are available. These results suggest that GOSAT X-CO2 is effective at regulating excess estimates of semi-arid CO2 uptake in regions that are less constrained by surface CO2 measurements. To promote understanding of climate change effects in semi-arid regions, it is important to continue monitoring trends in CO2 uptake with GOSAT.

Spatio-temporal fields of land-atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R-2 &lt; 0.5), ecosystem respiration (R-2 &gt; 0.6), gross primary production (R-2 &gt; 0.7), latent heat (R-2 &gt; 0.7), sensible heat (R-2 &gt; 0.7), and net radiation (R-2 &gt; 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R 2 &gt; 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R-2 &lt; 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.