Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution

Abstract

The terrestrial carbon cycle is strongly affected by natural phenomena, terrain heterogeneity, and human-induced activities that alter carbon exchange via vegetation and soil activities. In order to accurately understand terrestrial carbon cycle mechanisms, it is necessary to estimate spatial and temporal variations in carbon flux and storage using process-based models with the highest possible resolution. We estimated terrestrial carbon fluxes using a biosphere model integrating eco-physiological and mechanistic approaches based on satellite data (BEAMS) and observations with 1-km grid resolution. The study area is the central Far East Asia region, which lies between 30° and 50° north latitude and 125° and 150° east longitude. Aiming to simulate terrestrial carbon exchanges under realistic land surface conditions, we used as many satellite-observation datasets as possible, such as the standard MODIS, TRMM, and SRTM high-level land products. Validated using gross primary productivity (GPP), net ecosystem production (NEP), net radiation and latent heat with ground measurements at six flux sites, the model estimations showed reasonable seasonal and annual patterns. In extensive analysis, the total GPP and NPP were determined to be 2.1 and 0.9 PgC/year, respectively. The total NEP estimation was + 5.6 TgC/year, meaning that the land area played a role as a carbon sink from 2001 to 2006. In analyses of areas with complicated topography, the 1-km grid estimation could prove to be effective in evaluating the effect of landscape on the terrestrial carbon cycle. The method presented here is an appropriate approach for gaining a better understanding of terrestrial carbon exchange, both spatially and temporally.

Introduction

It is difficult to estimate a detailed spatial variation of the land carbon sink. This is mainly because land cover is generally more heterogeneous than the sea surface and there is much uncertainty about land cover change emissions (Denman et al., 2007 in IPCC AR4, Chapter 7), leading to an incomplete understanding of terrestrial ecosystem processes. In fact, the Intergovernmental Panel on Climate Change (IPCC, 2007) showed that the uncertainty for the land carbon sink is much larger than that of the oceanic carbon sink. Some model inter-comparison projects have also reported that spatial and temporal variations of the land carbon sink contribute significantly to the differences among existing studies and future scenarios (e.g., Cramer et al., 2001, Cramer et al., 1999, Friedlingstein et al., 2006, Murakami et al., 2009).

It is important to estimate terrestrial carbon fluxes with detailed spatial resolution. There are complex and diverse local-scale phenomena and events that continually occur on the surface of the land (e.g., climate and meteorological changes, deforestation, and urbanization). These local-scale events have significant effects on climate change and global carbon dynamics (Pongratz, Raddatz, et al., 2009, Saigusa et al., 2010), and consequently have a major impact on the global carbon cycle. Furthermore, with increased human activity, the effect of local events on the global carbon cycle is likely to increase steadily with each passing year (Pongratz, Reick, et al., 2009, Strassmann et al., 2008).

Unfortunately, we currently have a poor understanding of such local-scale changes in terrestrial carbon sinks. Previous studies have indicated that local-scale land surface events are a major controllable factor in terrestrial carbon dynamics. For example, in the summer of 2003, a rainy season front hovered over the vicinity of Japan for slightly longer than usual (Japan Meteorological Agency, 2005). The extended time that the stationary rainy front lingered greatly limited measured gross primary production (GPP) and ecosystem respiration in some parts of Japan because the amount of sunlight reaching the surface and the air temperature both decreased, leading to a large decrease in annual land carbon uptake in the entire region (Saigusa et al., 2010, Saigusa et al., 2008). Another example is the fact that humans continue to selectively cut timber from the earth's forests. An existing study analyzing terrestrial carbon fluxes in the Amazon region suggests that recent Amazonian deforestation, specifically in moist tropical forest areas, may decrease net primary production (NPP) (Potter et al., 1998). Finally, IPCC's Fourth Assessment Report: Climate Change 2007 (AR4) (IPCC, 2007) predicts that differences in air temperature and precipitation among continents and latitudinal belts might be gradually increasing due to increased concentrations of atmospheric carbon dioxide. Local-scale climate changes affect global terrestrial carbon dynamics via all ecosystem activities, but we have not established an approach for making detailed, high-resolution assessments of the effect of local-scale climate and meteorological changes on carbon exchanges.

In order to discover the precise role played by the carbon cycle in local-scale land surface condition changes, we need to derive a detailed spatial variation of terrestrial carbon fluxes using a model with the highest spatial resolution possible. The higher the spatial resolution of the model, the more we can understand local-scale land surface characteristics. By making use of downscaling approaches (high-resolution simulation, nesting, and statistical methods) with meteorological model simulations, an existing model has been estimated with a grid resolution of approximately 5 to 20 km (IPCC, 2007). In concert with such high-resolution analysis of climate change, it is important to assess the major factors affecting carbon dynamics with as high a grid resolution as possible, including experiments to estimate the terrestrial carbon cycle at higher resolution. The spatial resolutions of such models are not yet high enough to allow detailed local analyses, but many studies have assessed the effect of local factors on global land carbon dynamics. For example, for global NPP, grid resolutions have ranged from about 250 m to 2.5° (e.g., Nemani et al., 2003, Quaife et al., 2008, Raich et al., 1991, Running et al., 2004, Zaks et al., 2007, Zhao et al., 2006). For net ecosystem production (NEP), the spatial grid resolutions have ranged from 0.5° to 2.5° (Cramer et al., 2001, Sitch et al., 2003). Some studies have produced fairly high resolutions using just model simulation (Ito, 2008), whereas others have used a combination of satellite data and models (Heinsch et al., 2003, Heinsch et al., 2006, Sasai et al., 2007). Heinsch et al., 2003, Heinsch et al., 2006 attempted estimates of global GPP and NPP with 1-km grid resolution by combining Moderate resolution Imaging Spectroradiometer (MODIS) data with a simple, original algorithm, but did not arrive at an estimate of NEP because remote sensing cannot directly observe below-ground information. We need to apply information gathered by remote sensing into a biosphere model to estimate observation-based NEP. Ito (2008) demonstrated spatial variations in NPP and NEP with a 1-km grid resolution over Far East Asia, excluding built-up areas, using a prognostic-type biosphere model that required only climate and land cover maps as forcing data.

Satellite observation is one of the most efficient approaches to realistic, high-resolution carbon flux estimation at several spatial scales for the following reasons: 1) Since satellite observation is based on measurements, we can deal with realistic land carbon dynamics. 2) Some earth observation satellite sensors make continuous periodic observations (e.g., NOAA/AVHRR, Terra/MODIS and Aqua/MODIS, and TRMM), giving us the ability to cope with an ever-changing land surface environment using continuous time-series observation data. 3) Recent satellite monitoring permits extensive, short duration observations with relatively high resolution, yielding satellite data that has sufficient spatial resolution to examine certain small-scale events. (e.g., the MODIS product can generate a spatial grid resolution in the range from 1 km to 250 m). Generally, since terrestrial carbon fluxes cannot be directly observed by satellite sensors, they are estimated via diagnostic-type models and remote-sensing algorithms requiring satellite data products as inputs. Some parameters used in the models and algorithms cannot be observed by satellite sensors, and so we cannot readily estimate terrestrial carbon fluxes from satellite information only. An auxiliary way to compensate for such insufficient information is to input climate, vegetation, and soil data into the model. Many existing models estimate carbon fluxes from satellite data such as those obtained by NOAA/AVHRR, Terra/MODIS, and Aqua/MODIS (e.g., Goetz et al., 2000, Heinsch et al., 2006, Nemani et al., 2003, Potter et al., 1993, Prince and Goward, 1995, Ruimy et al., 1994, Sasai et al., 2005). Hence, by combining as much time-series satellite data as possible into a model, we can estimate detailed temporal carbon fluxes with more realistic land surface conditions. This combination approach could also be applied to global analysis with possible higher spatial resolution.

In order to analyze the spatial variation of NEP at a high resolution on a global and regional scale, we attempt to develop a satellite-driven approach for estimating terrestrial carbon fluxes that include urbanization's deforestation effects on land carbon dynamics. This suggests a need to propose an applicable method for evaluating the effect of local-scale events on carbon exchange in any area and on any spatial scale. Therefore, we attempt to develop a satellite-driven approach with a 1-km grid resolution. The approach basically requires the MODIS Land Products provided by NASA's Earth Observation System (NASA-EOS), and as subsidiary data, the Tropical Rainfall Measuring Mission (TRMM) product, the Shuttle Radar Topography Mission (SRTM) product, and others. We used the biosphere model integrating eco-physiological and mechanistic approaches using satellite data (BEAMS), which we expected to provide an efficient approach to calculating terrestrial carbon exchanges from satellite observations in any region of the world (Sasai et al., 2005, Sasai et al., 2007).