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Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications
Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere
interaction and has thus attracted wide interest from geoscientists. Owing to the rapid …
interaction and has thus attracted wide interest from geoscientists. Owing to the rapid …
Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods
Understanding crop responses to climate change is crucial for ensuring food security. Here,
we reviewed∼ 230 statistical crop modeling studies for major crops and summarized recent …
we reviewed∼ 230 statistical crop modeling studies for major crops and summarized recent …
China's vegetation restoration programs accelerated vegetation greening on the Loess Plateau
The vegetation greening on the Loess Plateau, China, over recent decades, has been
primarily driven by a series of vegetation restoration programs (VRPs) and other natural …
primarily driven by a series of vegetation restoration programs (VRPs) and other natural …
Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield
Statistical crop modeling is pivotal for understanding climate impacts on crop yields. Choices
of models matter: Linear regression is interpretable but limited in predictive power; machine …
of models matter: Linear regression is interpretable but limited in predictive power; machine …
Framework for near real-time forest inventory using multi source remote sensing data
Forestry inventory update is a critical component of sustainable forest management,
requiring both the spatially explicit identification of forest cover change and integration of …
requiring both the spatially explicit identification of forest cover change and integration of …
Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images
It is important to predict the spatial distribution of SOC accurately for migrating carbon
emission and sustainable soil management. Environmental variables influence the accuracy …
emission and sustainable soil management. Environmental variables influence the accuracy …
Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement
Long-term land surface temperature (LST) variation is vital for the study of climate change
and environmental monitoring. Change detection methods provide access to recovery …
and environmental monitoring. Change detection methods provide access to recovery …
A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection
Forest disturbances greatly affect the ecological functioning of natural forests. Timely
information regarding extent, timing and magnitude of forest disturbance events is crucial for …
information regarding extent, timing and magnitude of forest disturbance events is crucial for …
Forecasting of wastewater treatment plant key features using deep learning-based models: A case study
The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend
and predict the plant behavior to support process design and controls, improve system …
and predict the plant behavior to support process design and controls, improve system …
Assessing combinations of Landsat, Sentinel-2 and Sentinel-1 time series for detecting bark beetle infestations
Bark beetle infestations are among the most substantial forest disturbance agents
worldwide. Moreover, as a consequence of global climate change, they have increased in …
worldwide. Moreover, as a consequence of global climate change, they have increased in …