Logo Lanfrica

Code for: Synergistic Integration of Optical and Radar Data for Dynamic Soil Moisture Retrieval in Rain-Fed Wheat Fields of Ethiopia

Domaine:

agriculture

Type de record:

software
Créateur:
BirGesAbe
Éditeur:
Zenodo
Hôte:avatar
Python code for RFE feature selection, Optuna hyperparameter optimization, cross-validation, and machine learning model training (Random Forest and Gradient Boosting) for soil moisture retrieval using Sentinel-1/2 data.

Tags

soil moisture, machine learning,Random Forest,Gradient Boosting,Sentinel-1,Sentinel-2,remote sensing

Licenses

MIT Licensehttps://opensource.org/licenses/MIT

Similaires

A new reflectivity index for the retrieval of surface soil moisture from radar data

International audience A new approach based on the change detection technique is prop

Sensitivity of surface soil moisture retrieval to satellite-derived vegetation descriptors over wheat fields in the Kairouan plain

International audience Soil moisture estimation is a key component in hydrological pr

Soil moisture retrieval using GNSS-R data

International audience The aim of this study is to propose an inversion algorithm for

Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and th

Potentiality of optical and radar satellite data at high spatio-temporal resolutions for the monitoring of irrigated wheat crops in Morocco

International audience The potential of FORMOSAT-2 and ENVISAT/ASAR for the monitorin

Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco

International audience The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) s