相关论文: Comparison of downscaling techniques for high reso…
Environmental variables are increasingly affecting agricultural decision-making, yet accessible and scalable tools for soil assessment remain limited. This study presents a robust and scalable modeling system for estimating soil properties…
Soil moisture is a quantity of interest in many application areas including agriculture and climate modeling. Existing methods are not suitable for scale applications due to large deployment costs in high-resolution sensing applications…
Soil moisture is an essential parameter in agriculture. It determines several environmental and agricultural activities such as climate change, drought prediction, irrigation, etc. Smart irrigation management requires continuous soil…
Soil moisture (SM) is referred to as a finite amount of water molecules within the pore spaces and it is a crucial parameter of Hydro-Meteorological processes. The behaviour of soil moisture water changes spatially and temporally in…
We present a methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations. The InSAR time series analysis consists of five processing steps. A…
This article expands the tau-omega model to properly simulate L-band microwave emission of the soil-snow-vegetation continuum through a closed-form solution of Maxwell's equations, considering the intervening dry snow layer as a loss-less…
The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data…
Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar…
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global,…
Remote sensing of soil moisture and vegetation water content from space often requires underdetermined inversion of a zeroth-order approximation of the forward radiative transfer equation in L-band---known as the $\tau$-$\omega$ model. This…
Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the…
Soil moisture estimation is an important task to enable precision agriculture in creating optimal plans for irrigation, fertilization, and harvest. It is common to utilize statistical and machine learning models to estimate soil moisture…
The work which we present takes place within the framework of mission SMOS of the ESA which will consist to send a radiometer (1.4 GHz) in space. The goal of the research which we propose is the improvement of the comprehension of the…
In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation…
Super resolution offers a way to harness medium even lowresolution but historically valuable remote sensing image archives. Generative models, especially diffusion models, have recently been applied to remote sensing super resolution…
Proximal gamma-ray spectroscopy supported by adequate calibration and correction for growing biomass is an effective field scale technique for a continuous monitoring of top soil water content dynamics to be potentially employed as a…
The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of…
The work which we present takes place within the framework of mission SMOS of the ESA which will consist to send a radiometer (1.4 GHz) in space. The goal of the research which we propose is the improvement of the comprehension of the…
We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP…
Radio maps reflect the spatial distribution of signal strength and are essential for applications like smart cities, IoT, and wireless network planning. However, reconstructing accurate radio maps from sparse measurements remains…