Deep learning in environmental remote sensing: Achievements and challenges

Q Yuan, H Shen, T Li, Z Li, S Li, Y Jiang, H Xu… - Remote sensing of …, 2020 - Elsevier
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

H Tao, ZS Al-Khafaji, C Qi… - Engineering …, 2021 - Taylor & Francis
River sedimentation is an important indicator for ecological and geomorphological
assessments of soil erosion within any watershed region. Sediment transport in a river basin …

The effects of projected climate change and extreme climate on maize and rice in the Yangtze River Basin, China

X Chen, L Wang, Z Niu, M Zhang, J Li - Agricultural and Forest …, 2020 - Elsevier
Abstract crop yield is highly sensitive to climate change and extreme climate. Here, the
impact of climate change and extreme climate was assessed based on the climate variable …

Climate change impact on photovoltaic power potential in China based on CMIP6 models

J Niu, W Qin, L Wang, M Zhang, J Wu… - Science of the Total …, 2023 - Elsevier
China has the largest worldwide cumulative installed photovoltaic (PV) capacity, which is
expected to be 1300 GW in 2050. Industrial production, population explosion and fossil fuel …

[HTML][HTML] Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land

J Wei, Y Peng, J Guo, L Sun - Atmospheric Environment, 2019 - Elsevier
This study evaluates the long-term Terra and Aqua Moderate Resolution Imaging
Spectroradiometer (MODIS) Collection 6.1 (C6. 1) Level 3 atmospheric aerosol products …

Quantitative soil wind erosion potential map** for Central Asia using the Google Earth Engine platform

W Wang, A Samat, Y Ge, L Ma, A Tuheti, S Zou… - Remote Sensing, 2020 - mdpi.com
A lack of long-term soil wind erosion data impedes sustainable land management in
develo** regions, especially in Central Asia (CA). Compared with large-scale field …

Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

Y Wang, Q Yuan, T Li, H Shen, L Zheng… - Atmospheric Environment, 2019 - Elsevier
In this study, we evaluated the performance of the Moderate Resolution Imaging
Spectroradiometer (MODIS) Collection 6.1 (C6. 1) aerosol optical depth (AOD) products and …

Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

HVH Ayala, D Habineza, M Rakotondrabe… - Applied Soft …, 2020 - Elsevier
The present work deals with the application of coevolutionary algorithms and artificial neural
networks to perform input selection and related parameter estimation for nonlinear black-box …

Satellite-derived spatiotemporal PM2. 5 concentrations and variations from 2006 to 2017 in China

W Xue, J Zhang, C Zhong, D Ji, W Huang - Science of the Total …, 2020 - Elsevier
The PM 2.5 concentration is an important evaluation index for the global Sustainable
Development Goals (SDGs) for its negative impacts on human health. Last decade, several …

Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia

X Su, Y Wei, L Wang, M Zhang, D Jiang… - Science of the Total …, 2022 - Elsevier
The deep blue (DB) aerosol algorithm applied to four satellite instruments, AVHRR,
SeaWiFS, MODIS, and VIIRS, produced a long-term aerosol data set since 1989. This study …