Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data
Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely o...
Main Authors: | Peilei Luo, Huichun Ye, Wenjiang Huang, Jingjuan Liao, Quanjun Jiao, Anting Guo, Binxiang Qian |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-11-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/14/21/5624 |
Similar Items
-
Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images
by: Anting Guo, et al.
Published: (2023-03-01) -
A hybrid model coupling PROSAIL and continuous wavelet transform based on multi-angle hyperspectral data improves maize chlorophyll retrieval
by: Anting Guo, et al.
Published: (2024-08-01) -
Microbiology Combined with the Root Metabolome Reveals the Responses of Root Microorganisms to Maize Cultivars under Different Forms of Nitrogen Supply
by: Guan Tian, et al.
Published: (2024-08-01) -
Protocol: optimised electrophyiological analysis of intact guard cells from <it>Arabidopsis</it>
by: Chen Zhong-Hua, et al.
Published: (2012-05-01) -
Nitrogen Recoveries and Nitrogen Use Efficiencies of Organic Fertilizers with Different C/N Ratios in Maize Cultivation with Low-Fertile Soil by <sup>15</sup>N Method
by: Rosalina Armando Tamele, et al.
Published: (2020-07-01)