Mapping crop leaf area index at the parcel level via inverting a radiative transfer model under spatiotemporal constraints: A case study on sugarcane-Reference-Cited by-同舟云学术

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Mapping crop leaf area index at the parcel level via inverting a radiative transfer model under spatiotemporal constraints: A case study on sugarcane

Published:2022-07 Issue: Volume:198 Page:107003
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Yang Yingpin,Huang Qiting,Wu Zhifeng,Wu Tianjun,Luo Jiancheng,Dong Wen,Sun Yingwei,Zhang Xin,Zhang Dongyun

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference59 articles.

1. Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada;Abuelgasim;IEEE Trans. Geosci. Remote Sens.,2006

2. Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in Southeastern Turkey;Alganci;Photogramm. Eng. Remote Sens.,2013

3. The sensitivity of models of gross primary productivity to meteorological and leaf area forcing: A comparison between a Penman-Monteith ecophysiological approach and the MODIS Light-Use Efficiency algorithm;Alton;Agric. For. Meteorol.,2016

4. Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models;Atzberger;Remote Sens. Environ.,2004

5. Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery;Atzberger;Remote Sens. Environ.,2012

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2. A Systematic Review of Radiative Transfer Models for Crop Yield Prediction and Crop Traits Retrieval;Remote Sensing;2023-12-27

3. Arbuscular mycorrhiza enhances maize grain yield and nitrogen uptake during the grain filling stage with contrasting nitrogen status in two types of soils;Plant Growth Regulation;2023-08-07

4. Parcel level staple crop type identification based on newly defined red-edge vegetation indices and ORNN;Computers and Electronics in Agriculture;2023-08

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