Kazuhito Ichii

Large seasonal swings in leaf area of Amazon rainforests

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation–atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of ≈25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.

Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation

Interannual trends in annual and seasonal vegetation activities from 1982 to 1990 on a global scale were analysed using the Pathfinder AVHRR Land NDVI data set corrected by utilising desert and high NDVI areas. Climate effects on interannual variations in NDVI were also investigated using temperature and precipitation data compiled from stational observations. In the northern middlehigh latitudes, vegetation activities increased over broad regions because of a gradual rise in temperature. NDVI increases were also detected in the tropical regions, such as western Africa and south-eastern Asia. Plant photosynthetic activities on the other hand, decreased remarkably in some arid and semi-arid areas in the Southern Hemisphere, because annual rainfall decreased during this period.

Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990

The relationship between the Normalized Difference Vegetation Index (NDVI) and climatic variables was analysed on a global scale using the Pathfinder AVHRR Land NDVI data set, and observed climate data for the period 1982-1990. A significant correlation between interannual NDVI and temperature variation was recognized in the northern mid- to high latitude areas between spring and autumn. A significant correlation was also identified between the NDVI, temperature and precipitation in northern and southern semiarid regions. A comparison of global NDVI trends show that NDVI increases in the northern mid- and high latitudinal zones are related to temperature rise, and NDVI decreases in southern semiarid regions are due to a precipitation decrease in the survey period. Although the cause of NDVI increases in the equatorial regions remains unclear, the combined effects of forest regrowth, deforestation and fertilization may impact on the NDVI trend.

Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine

Application of remote sensing data to extrapolate evapotranspiration (ET) measured at eddy covariance flux towers is a potentially powerful method to estimate continental-scale ET. In support of this concept, we used meteorological and flux data from the AmeriFlux network and an inductive machine learning technique called support vector machine (SVM) to develop a predictive ET model. The model was then applied to the conterminous U.S. In this process, we first trained the SVM to predict 2000-2002 ET measurements from 25 AmeriFlux sites using three remotely sensed variables [land surface temperature, enhanced vegetation index (EVI), and land cover] and one ground-measured variable (surface shortwave radiation). Second, we evaluated the model performance by predicting ET for 19 flux sites in 2003. In this independent evaluation, the SVM predicted ET with a root-mean-square error (rmse) of 0.62 mm/day (approximately 23% of the mean observed values) and an R2 of 0.75. The rmse from SVM was significantly smaller than that from neural network and multiple-regression approaches in a cross-validation experiment. Among the explanatory variables, EVI was the most important factor. Indeed, removing this variable induced an rmse increase from 0.54 to 0.77 mm/day. Third, with forcings from remote sensing data alone, we used the SVM model to predict the spatial and temporal distributions of ET for the conterminous U.S. for 2004. The SVM model captured the spatial and temporal variations of ET at a continental scale

Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models 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. Our study indicates that spatial climate covariation drives the global carbon cycle response.

Interannual variations in vegetation activities and climate variability caused by ENSO in tropical rainforests

Interannual variations in terrestrial carbon cycle over tropical rainforests affect the global carbon cycle. Terrestrial ecosystem models show the interannual relationship between climate changes due to El Niño‐Southern Oscillation (ENSO) and net primary production over tropical rainforests. However, we need an independent analysis using satellite‐based vegetation index and climate parameters. In the present study, we extracted the ENSO‐related interannual variations from time‐series in Normalized Difference Vegetation Index (NDVI) and climate data from 1981 to 2000, and analysed their relevance. We detected relationships among NDVI, ENSO, and climate parameters from long‐term data with negative NDVI–ENSO, NDVI–temperature, and positive NDVI–precipitation relations. These correlations suggest that interannual variability in vegetation activities over tropical rainforests could be extracted from NDVI time‐series despite noise components in NDVI data, and that interannual changes in precipitation and temperature caused by ENSO play a more important role in vegetation activities over tropical rainforests than in incoming surface solar radiation.

Comparison of global net primary production trends obtained from satellite-based normalized difference vegetation index and carbon cycle model

The global terrestrial net primary production (NPP) trend was estimated from two independent methods, satellite observation data and a carbon cycle model, and the results were compared for validation. The satellite-based NPP trend was estimated from the incoming surface solar radiation data set and a National Oceanic and Atmospheric Administration/ Advanced Very High Resolution Radiometer data set that was corrected by normalized difference vegetation index in areas of desert and dense vegetation. The increase in NPP from the Goddard Institute for Space Studies solar radiation data set and from the LaRC solar radiation data set over 10 years in the 1980s was estimated to be 1.8 and 4.4%, respectively. The NPP trend based on a carbon cycle model was estimated from a simple carbon cycle model that was established for the period 1850–1990 with biospheric and oceanic carbon cycle history constraints. The historical trend obtained from the model correlates well with the time variation of not only the observed atmospheric CO2 but also the biospheric and oceanic carbon cycle history. Terrestrial NPP shows an increasing trend beginning in 1930 and is estimated to increase at a rate of 1.1% over the 10-year period in the 1980s. Although all these methods show a recent increase in NPP, satellite-based estimation using the LaRC data set shows a larger trend than the others. A comparison of he trends estimated by these methods indicates that it is necessary to improve the accuracy of incoming surface radiation data, CO2 emission history from changes in land-use change and model structure.

Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO2 exchanges: GOSAT CO2 inversion and empirical eddy flux upscaling

We examined the consistency between terrestrial biosphere fluxes (terrestrial CO2 exchanges) from data-driven top-down (GOSAT CO2 inversion) and bottom-up (empirical eddy flux upscaling based on a support vector regression (SVR) model) approaches over 42 global terrestrial regions from June 2009 to October 2011. Seasonal variations of the biosphere fluxes by the two approaches agreed well in boreal and temperate regions across the Northern Hemisphere. Both fluxes also exhibited strong anomalous signals in response to contrasting anomalous spring temperatures observed in North America and boreal Eurasia. This indicates that the CO2 concentration data integrated in the GOSAT inversion and the meteorological and vegetation data in the SVR models are equally effective in producing spatiotemporal variations of biosphere flux. Meanwhile, large differences in seasonality were found in subtropical and tropical South America, South Asia, and Africa. The GOSAT inversion showed seasonal variations that pivoted around CO2 neutral, while the SVR model showed seasonal variations that tended toward CO2 sink. Thus, a large difference in CO2 budget was identified between the two approaches in subtropical and tropical regions across the Southern Hemisphere. Examination of the integrated data revealed that the large tropical sink of CO2 by the SVR model was an artifact due to the underrepresented biosphere fluxes predicted by limited eddy flux data for tropical biomes. Because of the global coverage of CO2 concentration data, the GOSAT inversion provides better estimates of continental CO2 flux than the SVR model in the Southern Hemisphere.

New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

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., r2=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 (r2=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. (Less)

Satellite-Based Modeling of the Carbon Fluxes in Mature Black Spruce Forests in Alaska: A Synthesis of the Eddy Covariance Data and Satellite Remote Sensing Data

Abstract Scaling up of observed point data to estimate regional carbon fluxes is an important issue in the context of the global terrestrial carbon cycle. In this study, the authors proposed a new model to scale up the eddy covariance data to estimate regional carbon fluxes using satellite-derived data. Gross primary productivity (GPP) and ecosystem respiration (RE) were empirically calculated using the normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the model input is evaluated by comparing with the field data, then established and tested the model at the point scale, and then extended it into a regional scale. At the point scale, the empirical model could reproduce the seasonal and interannual variations in the carbon budget of the mature black spruce forests in Alaska and Canada sites, suggesting that seasonality of the NDVI and LST could explain the carbon fluxes and that the model is robust within ...