Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of...

Descrición completa

Detalles Bibliográficos

Principais autores:	Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2024-09-01
Series:	Land
Assuntos:	sugarcane yield estimation earth observation Sentinel-2 Landsat machine learning
Acceso en liña:	https://www.mdpi.com/2073-445X/13/9/1481
Tags:	Engadir etiqueta Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!