Lan Anh Dinh

Lan Anh Dinh
Observatoire de Paris · Laboratory for Space Studies and Instrumentation in Astrophysics

Master of Science

About

5
Publications
391
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
9
Citations
Introduction
Lan Anh Thi Dinh is a PhD student at LERMA (Laboratoire d’Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères) - Paris Observatory, France. Her research is focusing on crop yield simulation using statistical and machine learning models, from the monitoring to the seasonal and climate forecasting.
Additional affiliations
March 2019 - August 2019
Ecole Normale Supérieure de Paris
Position
  • Intern
Description
  • Master 2 internship
April 2018 - July 2018
Observatoire de Paris
Position
  • Intern
Description
  • Mater 1 internship
April 2017 - July 2017
Observatoire de Paris
Position
  • Intern
Description
  • Bachelor internship
Education
September 2018 - September 2019
École Polytechnique
Field of study
  • Water, Air, Pollution, and Energy
September 2017 - July 2018
Université Paris-Sud 11
Field of study
  • General Physics

Publications

Publications (5)
Article
Full-text available
Weather and climate strongly impact coffee; however, few studies have measured this impact on robusta coffee yield. This is because the yield record is not long enough, and/or the data are only available at a local farm level. A data-driven approach is developed here to 1) identify how sensitive Vietnamese robusta coffee is to weather on district a...
Article
Full-text available
The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterized by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimize the model parameter...
Article
Full-text available
Documenting the large scale variability of tropical forest structure and function is needed for improved understanding of the carbon and water cycles. The seasonal and diurnal cycles of passive and active microwave satellite observations are jointly analyzed for the first time, using the Global Precipitation Mission (GPM). Collocated backscattering...
Preprint
Full-text available
The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterised by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimise the model parameter...
Article
River Discharge (RD) estimates are necessary for many applications, including water management, flood risk, and water cycle studies. Satellite-derived long-term GIEMS-D3 Surface Water Extent (SWE) maps and HydroSHEDS data, at 90 m resolution, are here used to estimate several hydrological quantities at a monthly time scale over a few selected locat...

Network

Cited By

Projects

Projects (3)
Archived project
Vegetation is a key component of an ecosystem. Vegetation analysis is used in ecological surveys, land mapping and classification, and climate study. Over the last decades, the remote sensing satellites have been providing great data for studying vegetation. Especially, active and passive microwave satellite observations are used for many vegetation and soil moisture monitoring applications. This project aims to study the potential of using these satellite observations in evaluating the diurnal cycle of vegetation.
Archived project
The work will be related to the improvement of the estimation of the surface water extent and dynamics from satellites, at high spatial resolution, over long time series, and globally. A 90m spatial resolution dataset has been developed at LERMA. It is derived from multi-satellite observations and topography information. From this dataset, it is expected to calculate the river width and the water volume change. This work will contribute to the Surface Water and Ocean Topography (SWOT) NASA/CNES satellite mission.
Project
Exploiter un jeu de données climatologiques, hydro(géo)logiques et GPS pour caractériser et quantifier les processus à l'origine de la déformation de la surface terrestre.