Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data

Authors: Rahaman, Mashoukur ^{a, ⁎}, Southworth, Jane ^a, Amanambu, Amobichukwu Chukwudi ^b, Tefera, Bewuket B. ^a, Alruzuq, Ali R. ^{a, c}, Safaei, Mohammad ^a, Hasan, Md Muyeed ^a, Smith, Audrey Culver ^a

Source: In Journal of Environmental Management March 2025 376

Prediction of Vegetation Indices Series Based on SWAT-ML: A Case Study in the Jinsha River Basin

Authors: Chong Li, Qianzuo Zhao, Junyuan Fei, Lei Cui, Xiu Zhang, Guodong Yin

Source: Remote Sensing, Vol 17, Iss 6, p 958 (2025)

Subject Terms: vegetation dynamics, vegetation prediction, SWAT, machine learning, SIF, NDVI, Science

File Description: electronic resource

Relation: https://www.mdpi.com/2072-4292/17/6/958; https://doaj.org/toc/2072-4292

Access URL: https://doaj.org/article/c75d6211122e4ef69e34650d4872411c

Sensitivity of Vegetation to Climate in Mid-to-High Latitudes of Asia and Future Vegetation Projections

Authors: Jiangfeng Wei, Xiaocong Liu, Botao Zhou

Source: Remote Sensing, Vol 15, Iss 10, p 2648 (2023)

Subject Terms: climate change, leaf area index, vegetation prediction, Science

File Description: electronic resource

Relation: https://www.mdpi.com/2072-4292/15/10/2648; https://doaj.org/toc/2072-4292

Access URL: https://doaj.org/article/48281c55537146828ca80f81d51c6044

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