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Potential for artificial intelligence (AI) and machine learning (ML) applications in biodiversity conservation, managing forests, and related services in India
The recent advancement in data science coupled with the revolution in digital and satellite
technology has improved the potential for artificial intelligence (AI) applications in the …
technology has improved the potential for artificial intelligence (AI) applications in the …
A review on snowmelt models: progress and prospect
The frequency and intensity of flood events have been increasing recently under the
warming climate, with snowmelt floods being a significant part. As an effective manner of …
warming climate, with snowmelt floods being a significant part. As an effective manner of …
Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches
The fusion of active and passive microwave measurements is expected to provide more
robust surface soil moisture (SSM) map** across various environmental conditions …
robust surface soil moisture (SSM) map** across various environmental conditions …
Machine learning prediction and interpretation of the impact of microplastics on soil properties
The annual microplastic (MP) release into soils is 4–23 times higher than that into oceans,
significantly impacting soil quality. However, the mechanisms underlying how MPs impact …
significantly impacting soil quality. However, the mechanisms underlying how MPs impact …
Random forest-based soil moisture estimation using Sentinel-2, Landsat-8/9, and UAV-based hyperspectral data
Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount
importance for various applications ranging from food production to climate change …
importance for various applications ranging from food production to climate change …
Seasonal forecast of soil moisture over Mediterranean-climate forest catchments using a machine learning approach
Seasonal forecast of soil moisture at large spatial scale over forested landscape has
numerous implications in forest hydrology and bushfire risk planning. Remotely sensed …
numerous implications in forest hydrology and bushfire risk planning. Remotely sensed …
A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture
The objective of this study is to develop a novel multi-level pre-processing framework and
apply it for multi-step (one and seven days ahead) daily forecasting of Surface soil moisture …
apply it for multi-step (one and seven days ahead) daily forecasting of Surface soil moisture …
[HTML][HTML] A method of soil moisture content estimation at various soil organic matter conditions based on soil reflectance
Soil moisture is one of the most important components of all the soil properties affecting the
global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil …
global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil …
Throughfall spatial variability in a neotropical forest: Have we correctly accounted for time stability?
The complexity of rainfall-canopy interactions is likely to increase under extreme weather
events. Extreme conditions may affect forest structure and change the throughfall (TF) spatial …
events. Extreme conditions may affect forest structure and change the throughfall (TF) spatial …
Surface soil moisture retrieval based on transfer learning using SAR data on a local scale
Retrieving surface soil moisture on a local scale using Synthetic Aperture Radar (SAR) data
and Deep Learning (DL) models necessitates a substantial volume of data, which may not …
and Deep Learning (DL) models necessitates a substantial volume of data, which may not …