采用 PSO-NN-PROSAIL 模型的冬小麦叶面积指数反演研究

IF 3 3区地球科学 Q2 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

International Journal of Remote Sensing Pub Date : 2024-04-22 DOI:10.1080/01431161.2024.2339200

Zhong Gao, Xiaoping Lu, Xiaoxuan Wang, Zenan Yang, Ruyi Wang

引用次数: 0

摘要

依赖物理和经验模型的叶面积指数（LAI）评估方法被认为是目前最常用的方法，但其估算效率和准确性却受到了质疑。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Study on winter wheat leaf area index inversion employing the PSO-NN-PROSAIL model

Leaf area index (LAI) assessment methods relying on physical and empirical models are considered to be the most commonly used method at present, but their estimation efficiency and accuracy are def...

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来源期刊

International Journal of Remote Sensing 工程技术-成像科学与照相技术

CiteScore

7.00

自引率

5.90%

发文量

219

审稿时长

4.8 months

期刊介绍： The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include: • Remotely sensed data collection, analysis, interpretation and display. • Surveying from space, air, water and ground platforms. • Imaging and related sensors. • Image processing. • Use of remotely sensed data. • Economic surveys and cost-benefit analyses. • Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).