Present study was carried out to develop multiple linear regression (MLR) model of surface CH₄ flux emission from monthly atmospheric clearness index (8 km), day-night land surface temperature (LST) at 1 km and surface soil moisture (25 km) from Kalpana-1, MODIS TERRA and GCOM-W1 satellites, respectively. All these products were aggregated to GOSAT level-4A product resolution. 2° × 2° grids representing homogeneous agro-ecosystems were used to draw data samples. Initial results showed that methane flux (from GOSAT) produced significant coefficient of determination (R² = 0.84) with tri-variate (LST, surface soil moisture and atmospheric transmissivity) as compared to bi-variate (LST-soil moisture, LST-atmospheric transmissivity, soil moisture-atmospheric transmissivity) MLR models. These have been utilised for predicting surface methane flux for monthly scale. Validation of predicted methane flux with actual GOSAT methane flux was carried out and RMSE of 4.2–15.9% was obtained using variance-based bias correction. All these scaling models may be utilised to predict CH₄ flux at regional level using high-resolution LST from thermal remote sensing and soil moisture from Synthetic Aperture Radar.

目前的研究是根据月大气清洁度指数（8 km），昼夜地面温度（LST）（1 km）和地表土壤湿度（25 km）建立地面CH ₄通量排放的多元线性回归（MLR）模型分别来自Kalpana-1，MODIS TERRA和GCOM-W1卫星。所有这些产品均汇总为GOSAT 4A级产品分辨率。代表均质农业生态系统的2°×2°网格用于绘制数据样本。初步结果表明，甲烷通量（来自GOSAT）产生了很大的测定系数（R ² 与三变量（LST-土壤水分，LST-大气透射率，土壤水分-大气透射率）MLR模型相比，三变量（LST，地表土壤水分和大气透射率）为0.84）。这些已被用于预测每月规模的表面甲烷通量。用实际的GOSAT甲烷通量验证了预测的甲烷通量，并使用基于方差的偏差校正获得了4.2-15.9％的RMSE。所有这些缩放模型都可以利用热遥感中的高分辨率LST和合成孔径雷达中的土壤湿度来预测区域水平的CH ₄通量。