# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 4 Discussions
### NBE and Niino.3 Index  
As mentioned above, \(R_{e}\) and the GPP are generated by meteorological input variables, and datasets represented relatively homogenous distributions of 461.65 and 283.77 g C/m\({}^{2}\)/year for the spatial standard deviations when using every pixel, respectively. In the NBE equation, \(R_{e}\) and the GPP compensated for each other. Therefore, both datasets hardly contributed to NBE fluctuations. The temporal changes in the NBE seemed to be dominated by variations in the fire emissions because NEE (\(R_{e}-\text{GPP}\)) was overall constant (Figure 12). In addition, climatic impacts were analyzed by using the Nino.3 index, which represents how the sea surface temperature (SST) in 5\({}^{\circ}\)N-5\({}^{\circ}\)S 90-150\({}^{\circ}\)E differs from a reference SST. The Nino.3 index provided by the Japan Meteorological Agency (JMA) showed El Nino or La Nina if the index value was positive or negative (Figure 12). When the Nino.3 index was positive and negative, the averages of the NBE were 30.60 and 28.35 Mt C/month, respectively. FE in the El Nino and La Nina months were 2.58 and 1.25 Mt C/month, respectively. Usually, in the study area, during an El Nino period, the air temperature and precipitations were high and low, respectively--and vice versa in a La Nina period [63]. El Nino years are known for droughts [64] and more severe fire events than in normal years [65]. From the analysis of this study with the Nino.3 index, higher emissions were consistently found in the El Nino years. Furthermore, the other studies also revealed that higher CO\({}_{2}\) emissions occurred in El Nino years (2009-2010 and 2015-2016) from tropical regions through the analysis of the column-averaged CO\({}_{2}\) dry air mole fraction (XCO\({}_{2}\)) by using the orbiting carbon observatory-2 (OCO-2), and the greenhouse gases observing satellite (GOSAT) [66; 67; 68]. Hence, higher \(R_{e}\) values due to high temperature and more fire emissions were considered as the reasons for the high NBE in the El Nino periods. However, the peak of the NBE tended to appear slightly ahead of Nino.3 peaks. Moreover, the peak of the NBE usually happened in the dry season from November to April, while the Nino.3 index was still increasing until April. Many fires occurred in the timing of on-set of the Nino.3 index. It might be concluded that the annual peak of the NBE appeared during the dry season of this region, and the timing of its peak was related to the on-set stage of the Nino.3 index. In terms of \(R_{e}\), there were drastically decreasing periods: November 2009, March 2011, February 2016, and August 2019. The \(GWT\) data were investigated, since the \(GWT\) was only one factor for estimating \(R_{e}\) in this study. The average (\(\pm\) standard deviation) of the \(GWT\) in the listed four months above and the other months were \(-0.14\) (\(\pm 0.08\)) and \(-0.25\) (\(\pm 0.34\)) cm, respectively. Anomalies of the two periods were also calculated. The averages of the anomalies of the selected periods (low \(R_{e}\)) (0.59 cm) were higher than the other months (0.26 cm). Therefore, we concluded that the low values \(R_{e}\) in the listed four periods were induced by an abnormally shallow (high) \(GWT\). Moreover, after the on-set of the Nino.3 index, the \(GWT\) was increased; that is, P was increased. Some previous studies have mentioned the impact of fire haze to generate more rainfall or fog in inducing cloud condensation nuclei [69; 70]. Consistently, in this study, the low \(R_{e}\) in the listed periods appeared in the month after huge fire emissions. In addition, [PERSON] et al. (2004) [71] found that haze from fires could remain in the atmosphere for 20 days-to-one month after a fire event in Malaysia. Therefore, the low \(R_{e}\) values happened in the month after large fire events, and the haze from the fires could have provided cloud condensation nuclei for one month after the fire events. Consequently, high precipitation associated with haze as resultant in a high \(GWT\), which led low \(R_{e}\) values.