Remote sensing retrieval of total precipitable water under clear-sky atmosphere from FY-4A AGRI data by combining physical mechanism and random forest algorithm

Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems

Methods for Retrieval of Forest Parameters from Satellite Remote Sensing

FOREMMS Methods for retrieval of forest parameters from satellite remote sensing 2 Norsk Regnesentral NOTAT SAMBA/26/01 Sammendrag/Abstract: One of the key questions in the FOREMMS project, is what kind of forest parameters satellite sensors can provide. In this note, we review previous approaches to estimation of forest parameters from existing satellite optical data, and discuss basic methodo...

Comparison of Shannon Entropy Data Mining Techniques and Random Forest Algorithm to Preparing Underground Water Potential Map of Jahrom

Introduction: Iran's plateau, especially in different regions of Iran, is composed of different climates, while in its southern areas summer temperatures reach 50 degrees above zero, the Caspian Mediterranean region is running wet weather. One of the most important indicators of climate difference is the issue of water. Today, water supply is one of the most important concerns to realize the go...

Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variatio...

Integration of remote sensing and meteorological data to predict flooding time using deep learning algorithm

Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...