Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

Remote Sensing - Switzerland
doi 10.3390/rs9010035
Full Text
Abstract

Available in full text

Date
Authors
Publisher

MDPI AG

Related search

Time Series Forecasting Using Evolutionary Neural Network

International Journal of Computer Applications
2013English

Evaluation of a Global Vegetation Model Using Time Series of Satellite Vegetation Indices

Geoscientific Model Development
EarthSimulationPlanetary SciencesModeling
2011English

Estimating Time-Dependent Vegetation Biases in the SMAP Soil Moisture Product

2018English

Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network

IEEE Transactions on Geoscience and Remote Sensing
EarthPlanetary SciencesElectricalElectronic Engineering
2012English

Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products

ISPRS Journal of Photogrammetry and Remote Sensing
DevelopmentComputers in Earth SciencesMolecular Physics,PlanningEngineeringComputer Science ApplicationsAtomicOpticsGeography
2017English

Time Series Data Analysis Using Probabilistic and Neural Network

2006English

Sequential Assimilation of Satellite-Derived Vegetation and Soil Moisture Products Using SURFEX_v8.0: LDAS-Monde Assessment Over the Euro-Mediterranean Area

Geoscientific Model Development
EarthSimulationPlanetary SciencesModeling
2017English

Soil Moisture Retrieval Under Agricultural Vegetation Using Fully Polarimetric SAR

2012English

Error Characterisation of Global Active and Passive Microwave Soil Moisture Data Sets

Hydrology and Earth System Sciences Discussions
EarthEarth-Surface ProcessesPlanetary Sciences
2010English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy