A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data

Ma, Linhua; Cui, Yuanlai; Liu, Bo; Liao, Bin; Wei, Jun; Han, Huanhao; Tian, Wenxiang

doi:10.1016/j.scitotenv.2022.159917

Cited by 6 publications

(2 citation statements)

References 57 publications

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“…The MOD15A2H LAI [27] was downloaded from the Moderate Resolution Imaging Spectroradiometer (MODIS), which was retrieved every 8 days ranging from 2011 to 2018 with a 500 m spatial resolution. The LAI can effectively represent vegetation growth dynamics.…”

Section: Data Sourcementioning

confidence: 99%

The Time Lag Effects and Interaction among Climate, Soil Moisture, and Vegetation from In Situ Monitoring Measurements across China

Wang,

Bao,

Wang

et al. 2024

Remote Sensing

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The interaction between soil moisture (SM) and vegetation dynamics has been proven in previous studies. In situ measurements have provided reliable data to investigate and validate the time effect in different zones, which is important in the hydrology and agriculture fields. There were 845 SM in situ monitoring measurements utilized with the correlation between SM and vegetation across various soil depths and climate zones in China. The impact of climate and teleconnection factors on SM and the leaf area index (LAI) are also discussed. The results indicate that SM increases from northwest to southeast in China. The time lag responses of SM to temperature, precipitation, relative humidity, and sunshine duration are 0–3 days, 3–7 days, 1–3 days, and 3–15 days, respectively. The LAI is most strongly correlated with the climate of the current month. When the LAI leads SM, a negative correlation is observed, whereas a positive correlation is observed when SM leads the LAI. This proves that vegetation growth restricts the increase in SM, and soil drying further restricts the growth of vegetation. There was a response time of 2–4 months between the LAI and SM. The effect of vegetation and deeper SM was significant in the arid zone, while they were coupled with shallow SM in the humid zone. Additionally, the El Niño–Southern Oscillation (ENSO) showed a significant positive correlation with SM in 2015–2016 with signals of 9–14 months. The results provide support for balancing the contradiction between future vegetation restoration and water resource scarcity.

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Section: Data Sourcementioning

confidence: 99%

The Time Lag Effects and Interaction among Climate, Soil Moisture, and Vegetation from In Situ Monitoring Measurements across China

Wang,

Bao,

Wang

et al. 2024

Remote Sensing

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show abstract

“…Например, основываясь на механизме и структуре широко используемой модели CH4MOD, был предложен основанный на ГИС метод расчета региональных выбросов CH4 (GRMC) путем введения нескольких источников изображений дистанционного зондирования, включая MOD09A1, MOD11A2, MOD15A2H, а также местные стандарты управления водными ресурсами. [2] При этом, достаточно важным аспектом при переходе к использованию информационных технологий, является налаживание общей аппаратной экосистемы, работающей на отечественном программном обеспечении. В ином случае использование зарубежных технологий ставит под удар обеспчение продовольственной безопасности страны -зависимость от технического сервиса и облачных данных иностранных компаний подвергает опасности стабильную и бесперебойную работу систем на территории Российской Федерации.…”

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