Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances
The key elements that underpin food security require the adaptation of agricultural systems
to support productivity increases while minimizing inputs and the adverse effects of climate …
to support productivity increases while minimizing inputs and the adverse effects of climate …
[HTML][HTML] Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions
Satellite time series with high spatial resolution is critical for monitoring land surface
dynamics in heterogeneous landscapes. Although remote sensing technologies have …
dynamics in heterogeneous landscapes. Although remote sensing technologies have …
Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale
Soil moisture has a considerable impact on the hydrological cycle, runoff generation,
drought development, and water resources management. Soil moisture products provided …
drought development, and water resources management. Soil moisture products provided …
Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation
Importance of target-oriented validation strategies for spatio-temporal prediction models is
illustrated using two case studies:(1) modelling of air temperature (T air) in Antarctica, and …
illustrated using two case studies:(1) modelling of air temperature (T air) in Antarctica, and …
Machine learning for hydrologic sciences: An introductory overview
The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling
Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles.
However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed …
However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed …
Physics‐constrained machine learning of evapotranspiration
Estimating ecosystem evapotranspiration (ET) is important to understanding the global water
cycle and to study land‐atmosphere interactions. We developed a physics constrained …
cycle and to study land‐atmosphere interactions. We developed a physics constrained …
Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing
Accurate estimation of evapotranspiration (ET) is essential for several applications in water
resources management. ET models using remote sensing data have flourished in recent …
resources management. ET models using remote sensing data have flourished in recent …
Review of pixel-level remote sensing image fusion based on deep learning
Z Wang, Y Ma, Y Zhang - Information Fusion, 2023 - Elsevier
The booming development of remote sensing images in many visual tasks has led to an
increasing demand for obtaining images with more precise details. However, it is impractical …
increasing demand for obtaining images with more precise details. However, it is impractical …
Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery
Urban land cover and land use map** plays an important role in urban planning and
management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and …
management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and …