Mr. Samit Shivadekar Profile

Samit Shivadekar

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Proceedings Article | 22 April 2020 Paper

Satellite data fusion of multiple observed XCO2 using compressive sensing

Phuong Nguyen, Samit Shivadekar, Sai Sree Laya Chukkapalli, Milton Halem

Proceedings Volume 11423, 114230Y (2020) https://doi.org/10.1117/12.2558319

KEYWORDS: Satellites, Data fusion, Compressed sensing, Carbon dioxide, Sensors, Image fusion, Spatial resolution, Signal processing, Earth observing sensors, Carbon

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When it entered into the era of big data, Earth observing systems developed into a new stage, namely characterized by low cost, multi-national, multi-sensor and multi-modal with varying spatial and spectral resolutions confronting new challenges and opportunities. Climate data records from multiple data sources are used to infer seasonal and interannual variations which will advance and promote the development of data fusion methods. Compressed sensing is a new framework in which data acquisition and data processing are merged. It provides a new fantastic way to handle multiple observations of the same field view from complementary remote sensing instruments, allowing us to recover information at very low signal-to-noise ratio. We will particularly point out that a Compressive Sensing based framework is flexible enough for combining the two measurement systems by fusing the data from the two satellites, NASA Orbiting Carbon Observatory -2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT) to calculate the interannual Net XCO2 variability over land for three latitudinal regions, Alaska/Canada, United States and the Amazon/Brazil. The OCO-2 design is optimized for sensitivity to XCO2 variations, with an unprecedented combination of spatial resolution (about 3km) with narrow nadir coverage, while GOSAT provides broader spatial coverage (10km) with wider scanning coverage. There are different temporal degradations of both instruments over time because GOSAT was launched in 2009 and OCO-2 was launched in 2014. Both instruments infer CO2 concentration from high-resolution measurements of reflected sunlight and use similar inversion algorithms to retrieve CO2 concentrations. Both are passive satellites providing on-orbit global measurements of the greenhouse gas, XCO2, for the years 2015 -2018. The results of the CS data fusion framework show that the fused data have Root Mean Square Error (RMSE) varying from 1.31 ppm to 4.12 ppm compared with original data, depending on the region of study and gridding resolution. Validation of fused data compared with AmeriFlux station towers observations shows RMSE of 2.68 ppm.

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