Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satelli...
This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were...
Tea ( L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan's annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and qual...
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...
This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water quality, ecosystems, and public health. Traditional detection methods, like satellite imagery and unmann...
Neural networks : the official journal of the International Neural Network Society
40088832
Land use and land cover (LULC) classification is a popular research area in remote sensing. The information of single-modal data is insufficient for accurate classification, especially in complex scenes, while the complementarity of multi-modal data ...
Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Conventional methods for mapping heavy metal distribution, which rely on soil sampling followed by che...
Environmental science and pollution research international
40257731
Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challe...
Accurate prediction of maize yields is crucial for effective crop management. In this paper, we propose a novel deep learning framework (CNNAtBiGRU) for estimating maize yield, which is applied to typical black soil areas in Northeast China. This fra...
The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya's key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the intera...