A machine‐learning based ConvLSTM architecture for NDVI forecasting-Reference-Cited by-同舟云学术

A machine‐learning based ConvLSTM architecture for NDVI forecasting

Published:2020-10-22 Issue: Volume: Page:
ISSN:0969-6016
Container-title:International Transactions in Operational Research
language:en
Short-container-title:Intl. Trans. in Op. Res.

Author:

Ahmad Rehaan¹,Yang Brian¹,Ettlin Guillermo²,Berger Andrés³,Rodríguez‐Bocca Pablo²

Affiliation:

1. Cupertino High School 10100 Finch Avenue Cupertino CA 95014 USA

2. Facultad de Ingeniería Instituto de Computación, Universidad de la República Julio Herrera y Reissig 565 Montevideo 11300 Uruguay

3. Instituto Nacional de Investigación Agropecuaria La Estanzuela, Ruta 50 km 11 Colonia 70006 Uruguay

Funder

Comisión Sectorial de Investigación Científica

Publisher

Wiley

Subject

Management of Technology and Innovation,Management Science and Operations Research,Strategy and Management,Computer Science Applications,Business and International Management

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/itor.12887

Reference32 articles.

1. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology

2. Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries

3. Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems

4. Learning long-term dependencies with gradient descent is difficult

5. Predicting the normalized difference vegetation index (NDVI) by training a crop growth model with historical data;Berger A.;Computers and Electronics in Agriculture,2019

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