Integrations remote sensing mapping with the environmental model to quantify emissions from rice paddies in Thailand

Wetland rice soils have been identified as an important source of GHG emissions at the global scale, particularly methane emissions. As paddy rice cropland in Thailand accounts for 52% of all cultivated land in the country and 6% of the world's rice paddies, accurately estimating emissions from rice paddies has become important in this country for GHG inventories or mitigation policies. This research integrated biogeochemical models with remote sensing technology to advance the DNDC regional application. This research identified spatio-temporal patterns of GHG emissions (CO
₂
, CH
₄
, and N
₂
O) from rice fields in Thailand (Lopburi province). New Method and database system were developed to increase accuracy of DNDC model; moreover, spatial and temporal characteristics of phenological information derived from remote sensing data (MODIS) were used in DNDC to quantify emissions. The results demonstrate the influence of human management, climate variation, and physical geography on the change of GHG emissions. Phenology of rice and human management were the major factors effecting the changes of CH
₄
emissions. The change of CO
₂
emissions showed rapid changes in extreme climate years. N
₂
O emission was strongly related to climate variation, especially rainfall changes. Rice intensification with longer length of flooding period and high application rates of fertilizer extremely enhanced CH
₄
production. Light soil texture produces higher emission than heavy soil texture. The results suggest that practical mitigation options should be carefully regulated to more efficiently balance among the emission types as well as to maintain or improve grain yields.