Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Satellite imagery to map topsoil organic carbon content over cultivated areas: an overview
There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper
horizons or plough layer for enabling decision support and land management, while …
horizons or plough layer for enabling decision support and land management, while …
An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning …
X Meng, Y Bao, Y Wang, X Zhang, H Liu - Remote Sensing of Environment, 2022 - Elsevier
Abstract Knowledge of the soil organic carbon (SOC) content is critical for environmental
sustainability and carbon neutrality. With the development of remote sensing data and …
sustainability and carbon neutrality. With the development of remote sensing data and …
Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data
Conservation tillage practices can bring benefits to agricultural sustainability. Accurate
spatial and temporal resolved information of field-scale crop residue cover, which reflects …
spatial and temporal resolved information of field-scale crop residue cover, which reflects …
[HTML][HTML] Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network
Abstract Visible-to-near-infrared (vis–NIR) and mid-infrared (MIR) spectroscopy have been
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
Regional and global hotspots of arsenic contamination of topsoil identified by deep learning
Topsoil arsenic (As) contamination threatens the ecological environment and human health.
However, traditional methods for As identification rely on on-site sampling and chemical …
However, traditional methods for As identification rely on on-site sampling and chemical …
Rapid retrieval of cadmium and lead content from urban greenbelt zones using hyperspectral characteristic bands
M Arif, Y Qi, Z Dong, H Wei - Journal of Cleaner Production, 2022 - Elsevier
Greenbelts around roads are an essential part of the ecosystem that can reduce heavy metal
contamination from traffic and contribute to sustainable development. However, only limited …
contamination from traffic and contribute to sustainable development. However, only limited …
Mapping of soil organic matter in a typical black soil area using Landsat-8 synthetic images at different time periods
C Luo, W Zhang, X Zhang, H Liu - Catena, 2023 - Elsevier
Mapping of soil organic matter (SOM) in cultivated land is one of the important aspects of
digital soil mapping, and its results are of great significance for agricultural precision …
digital soil mapping, and its results are of great significance for agricultural precision …
Data mining of urban soil spectral library for estimating organic carbon
Accurate quantification of urban soil organic carbon (SOC) is essential for understanding
anthropogenic changes and further guiding effective city managements. Visible and near …
anthropogenic changes and further guiding effective city managements. Visible and near …
A comparison of multiple deep learning methods for predicting soil organic carbon in Southern Xinjiang, China
Soil organic carbon (SOC) plays an important role in soil functioning and also global C
balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective …
balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective …