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Método de extracción de la distribución del arroz basado en series temporales de datos de Sentinel-2

  • role： First author第一作者
  • Affiliation：

    Donghai Academy East Sea Marine Ecology Research Center, Ningbo University, Ningbo 315211, China

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

  • Email：15314620328@163.com
  • Introduction：娄轶峰，研究方向为农业遥感。E-mail： 15314620328@163.com
LOU Yifeng12,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

HUANG Ke3,  
  • role： Corresponding author通信作者
  • Affiliation：

    Donghai Academy East Sea Marine Ecology Research Center, Ningbo University, Ningbo 315211, China

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo 315211, China

  • Email：yanggang@nbu.edu.cn
  • Introduction：杨刚，研究方向为遥感影像数据质量改善与信息提取、遥感滨海健康监测技术与应用。E-mail： yanggang@nbu.edu.cn
YANG Gang124*,  
  • Affiliation：

    Donghai Academy East Sea Marine Ecology Research Center, Ningbo University, Ningbo 315211, China

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo 315211, China

SUN Weiwei124,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

SHAO Chunchen2,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

LIU Weiwei2,  
  • Affiliation：

    Donghai Academy East Sea Marine Ecology Research Center, Ningbo University, Ningbo 315211, China

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo 315211, China

WANG Lihua124,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

HU Jing2

resumen

El arroz es uno de los cultivos de cereales más importantes del mundo, y cerca del 50% de la población mundial se alimenta de arroz. El crecimiento del arroz requiere una gran cantidad de recursos hídricos, y el riego de los campos de arroz es una de las principales fuentes de emisiones de metano. Por lo tanto, comprender la distribución del cultivo de arroz es importante para garantizar la seguridad alimentaria y proteger el medio ambiente. Los métodos actuales de cartografía del arroz enfrentan problemas como altas demandas de muestras, configuración de parámetros complejos y baja aplicabilidad, entre otros. Por lo tanto, para cartografiar la distribución del arroz de manera rápida y precisa, este estudio desarrolló un nuevo índice óptico para el arroz NOPRI (New Optical Paddy Rice Index) basado en series temporales de NDVI y MNDWI y lo comparó con el método TWDTW y el índice SPRI en verificaciones aplicadas en 4 regiones del mundo con climas y modos de cultivo de arroz diferentes. Los resultados mostraron que el NOPRI podía generar con precisión la distribución del arroz, con una precisión general superior a 0.945 y un F1 superior a 0.907; en comparación con los métodos y conjuntos de datos existentes, el NOPRI tiene una mayor aplicabilidad en regiones con una mayor heterogeneidad del paisaje. Gracias a su construcción simple y umbrales estables, el NOPRI puede satisfacer las necesidades de cartografía a gran escala del arroz, proporcionando una base confiable para una planificación agrícola precisa y una evaluación de la seguridad alimentaria.

palabra clave

teledetección; arroz; Sentinel-2; series temporales; análisis armónico temporal; índices de arroz

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