| 16 Mar 2026
Enhanced methane monitoring: A globally harmonized daily 0.1° XCH4 through machine learning-based fusion of GOSAT, GOSAT-2, and TROPOMI
Abstract. Accurate global monitoring of atmospheric methane (CH4) is essential for tracking progress toward climate mitigation targets such as the Global Methane Pledge (GMP). Ground-based measurement networks are too sparse to provide sufficient spatial coverage, while satellite-derived retrievals are hindered by systematic biases and uncertainties, limiting their reliability for consistent global monitoring. We present the first global fusion of GOSAT, GOSAT-2, and TROPOMI to generate a globally consistent daily 0.1° land dataset for 2020–2023 for enhanced global XCH4 mapping. The framework employs a three-step machine-learning (ML) approach: (1) sensor-specific bias correction using TCCON observations, (2) cross-sensor harmonization to GOSAT-2, the sensor with the strongest post-correction TCCON agreement, and (3) priority-based fusion. Tree-based ensemble regressors were trained with satellite retrieval parameters to reduce systematic biases and inter-sensor discrepancies. Independent validation at three withheld TCCON stations demonstrates robust generalization of the fused product (R2 = 0.81, RMSE = 10.78 ppb), outperforming standard and operational bias-corrected satellite products and previously reported ML-based approaches. Regional assessments show that fusion substantially improves data availability and reduces systematic errors, delivering up to 12 % relative coverage gains and 33–94 % bias reductions compared to TROPOMI operational products in challenging regions (South Asia, Amazon Basin, Eastern Siberia).The fused dataset reveals intensifying positive XCH4 anomalies (+60 ppb) over South Asia, East Asia, and Central Africa during 2020–2023, linked to MODIS-derived agricultural and urban land classes as well as known oil and gas fields. The dataset provides a scalable resource for regional CH4 emissions assessment and continuous monitoring, with the framework extendable to upcoming satellite missions (GOSAT-GW, CO2M) for long-term GMP progress tracking.
This study builds a 0.1° × 0.1° daily XCH4 product covering 2020–2023. The authors do this by bias-correcting GOSAT-2 with TCCON as reference, then bias-correcting GOSAT and TROPOMI with the TCCON-informed GOSAT-2 product as reference, and then finally filling each daily 0.1° × 0.1° grid cell with the bias-corrected data from the three sensors, giving priority to GOSAT-2, then TROPOMI, then GOSAT. This fused product performs well against withheld TCCON data and provides additional coverage past that of TROPOMI in challenging retrieval environments. The presentation quality is excellent, but I encourage the authors to consider the following comments at their discretion to improve the scientific quality and significance.
General comments
Specific comments
Line 39: misspelled reference?
Line 46: consider defining XCH4
Line 61: Lorente et al. (2021) uses a small-area approximation, not TCCON
Line 99: please check the 30 times number (cf. Table 1 in Jacob et al., 2022)
Line 224: extra “and” at the end of the sentence
Line 253: specify northern-hemisphere/boreal summer and autumn
Figure 4: metrics (e.g. RMSE in subplots c,f,h) look much more optimistic than the LOSOCV column of Table S6
Figure 5: why not leave one season out in your cross validation if you are going to plot like this?
SI: double-check (e.g., no bolds in Tables S6 and S7, no Table S8, etc.)
Line 525: Jacob et al. (2022) no longer is in Discussion