Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression-Reference-Cited by-同舟云学术

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Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression

Published:2023-06 Issue:6 Volume:9 Page:e17432
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Amankulova Khilola^ORCID,Farmonov Nizom^ORCID,Akramova Parvina,Tursunov Ikrom,Mucsi László^ORCID

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference49 articles.

1. County-level soybean yield prediction using deep CNN-LSTM model;Sun;Sensors,2019

2. Soybean yield prediction from UAV using multimodal data fusion and deep learning;Maimaitijiang;Remote Sens. Environ.,2020

3. Optimal county-level crop yield prediction using MODIS-based variables and weather data: a comparative study on machine learning models;Ju;Agric. For. Meteorol.,2021

4. Optimal soybean (Glycine max L.) land suitability using GIS-based multicriteria analysis and sentinel-2 multitemporal images;Radocaj;Rem. Sens.,2020

5. Identifying the contributions of multi-source data for winter wheat yield prediction in China;Cao;Rem. Sens.,2020

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