朴 慧美

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 (R-e), 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 (R-2) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (R-e) based on a relationship between the in situ GWT and flux-tower-observed R-e. 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) R-e, GPP, FE, and NBE from whole peatlands in the study area (6 degrees N-11 degrees S, 95-141 degrees 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. R-e and FE were influenced by El Nino, and the value of the NBE was also high in the El Nino 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 R-e with delineations of types of anthropogenic impacts (e.g., drainages and fires).

Gross primary production (GPP) is a crucial factor in the carbon cycle especially for the absorption of carbon dioxide into the biosphere from the atmosphere. A large discrepancy between a satellite-based GPP product named MOD17A2H GPP and in-situ data measured at eddy covariance flux towers has been identified over East Asia where the biome types and climatic characteristics are heterogeneous with rugged terrain. For that reason, this study focuses on two potential major error sources in MOD17A2H GPP, which are the coarse resolution land cover information and inappropriate meteorological parameters. The finer resolution observation and monitoring global land cover (FROM-GLC) and the MODIS land cover product collection 5.1 (MCD12Q1) were used to describe biome types in detail, by combining spatial distribution from FROM-GLC and the phenological characteristics of land cover from MCD12Q1. Meteorological parameters were optimized using the 55-years Japanese reanalysis meteorological data (JRA-55). The light use efficiency of the MOD17 GPP algorithm was modified using the combined land cover information (FROM-MCD). Although the use of FROM-MCD and JRA-55 did not improve MOD17A2H GPP, the optimization of two meteorological parameters-daily minimum air temperature (TMIN) and vapor pressure deficit (VPD) significantly improved the GPP algorithm for East Asia. The results show that the root mean square errors (RMSEs) between the estimated and in situ GPPs decreased from 21.83 (gC/m(2)/8days) to 16.11 (gC/m(2)/8days) through optimizing the two parameters at 9 flux tower sites. The optimized TMIN and VPD thresholds in the MOD17 GPP algorithm were applied to the entire study area (i.e., East Asia) according to the Koppen-Geiger climate classes. The estimated GPP using the proposed approach was compared to GPPs from widely used process-based models (i.e., VISIT, BEAMS, and BESS), which confirmed that the proposed approach with locally optimized meteorological parameters improved on the underestimation of the MOD17 GPP algorithm for East Asia. The uncertainty of the VPDmin parameter was revealed to be larger than that of TMINmax.