[HTML][HTML] Integration of remote sensing and machine learning for precision agriculture: a comprehensive perspective on applications

J Wang, Y Wang, G Li, Z Qi - Agronomy, 2024 - mdpi.com
Due to current global population growth, resource shortages, and climate change, traditional
agricultural models face major challenges. Precision agriculture (PA), as a way to realize the …

Digital map** of soil pH and carbonates at the European scale using environmental variables and machine learning

Q Lu, S Tian, L Wei - Science of the Total Environment, 2023 - Elsevier
Soil pH and carbonates (CaCO 3) are important indicators of soil chemistry and fertility, and
the prediction of their spatial distribution is critical for the agronomic and environmental …

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 …

Map** 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
Map** of soil organic matter (SOM) in cultivated land is one of the important aspects of
digital soil map**, and its results are of great significance for agricultural precision …

[HTML][HTML] Soil organic matter prediction model with satellite hyperspectral image based on optimized denoising method

X Meng, Y Bao, Q Ye, H Liu, X Zhang, H Tang… - Remote Sensing, 2021 - mdpi.com
In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the
potential of satellite hyperspectral data for predicting soil properties, we took MingShui …

Towards spatially continuous map** of soil organic carbon in croplands using multitemporal Sentinel-2 remote sensing

P Shi, J Six, A Sila, B Vanlauwe, K Van Oost - ISPRS Journal of …, 2022 - Elsevier
Intensified human activities can augment soil organic carbon (SOC) losses from the world's
croplands, making SOC a highly dynamic parameter both in space and time. Sentinel-2 …

Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning algorithms

C Luo, Y Wang, X Zhang, W Zhang, H Liu - Catena, 2022 - Elsevier
Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great
significance for regional sustainable development. However, due to the strong spatial …

[HTML][HTML] Identifying soil groups and selecting a high-accuracy classification method based on multi-textural features with optimal window sizes using remote sensing …

M Duan, X Song, Z Li, X Zhang, X Ding, D Cui - Ecological Informatics, 2024 - Elsevier
Determining the spatial distribution of soil groups accurately is crucial for managing soil
resources. However, limitations persist in the map** of soil groups using multi-textural …

Map** the soil types combining multi-temporal remote sensing data with texture features

M Duan, X Song, X Liu, D Cui, X Zhang - Computers and Electronics in …, 2022 - Elsevier
With the rapid development of remote sensing (RS) technology, remote sensing images
provide an important data basis for soil type map**. In remote sensing images, temporal …

Remote estimates of soil organic carbon using multi-temporal synthetic images and the probability hybrid model

X Wang, L Wang, S Li, Z Wang, M Zheng, K Song - Geoderma, 2022 - Elsevier
Soil organic carbon (SOC) plays a key role in soil function, ecosystem services, and the
global carbon cycle. Digital SOC map** is essential for agricultural production …