Earticle

초록

영어

Currently, TM images has a very high practical value and widely used in all aspects of agricultural. Unmanned Aerial Vehicles (UAV) remote sensing platform mounted ADC-Lite multi-spectral sensor has consistent channels response functions with TM sensor in TM2, TM3 and TM4, demonstrated to compete with TM sensor, due to low operational cost, high operational flexibility, high spatial resolution of imagery (0.018m with flight altitude 50m) and heterogeneity both at time and spatial-scale. In order to make sure whether it has widely used as TM sensor, moreover, the aim of this work is to assess ADC-Lite performance such as its adaptability and practicability. In this paper, ADC-Lite multi-spectral data, ground truth ASD hyperspectral and Leaf area index (LAI) data were acquired in soybean planting area, Jiaxiang County, Shandong Province on September18th, 2015. Since the ADC-Lite has different spatial scales with TM, this paper used TM simulation data transformed by ground truth ASD data, constructed LAI inversion model by empirical model based on two sensors and ground measured data, using 5 vegetation indices: ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil adjust vegetation index (SAVI), difference vegetation index (DVI) and triangular vegetation index (TVI). Determination coefficient R2, root mean square error (RMSE) and the estimation accuracy (EA) 3 indicators were acquired to assess the model. This work suggests that the established model of ADC-Lite sensor with TM simulation sensor has high consistency in accuracy. NDVI linear regression model derived from both of them presented a strong correlation with ground-measured LAI. It’s preliminarily shown that ADC-Lite images assess soybean LAI is feasible. This is anticipated to have tremendous implications that ADC-Lite can be made supplement for existing satellites, aerial and ground sensing, provide important information for Crop condition monitoring and critical data to support crop maturity, nutrition monitoring and fertilization management.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Introduction of the Experiment Zone
  2.2. Ground Data Acquisition
  2.3. TM Remote Sensing Data Acquisition and Processing
  2.4. ADC-Lite Multi-Spectral Data Acquisition and Pre-Processing
 3. Results and Discussion
  3.1. Construction of Soybean LAI Inversion Model
  3.2. LAI Inversion Model Accuracy Comparison and Analysis
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Qi Zhang [ College of Electronic Engineering and Information, Northeast Agricultural University, Harbin 150030, China, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China, Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China, Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China ]
• Zhongbin Su [ College of Electronic Engineering and Information, Northeast Agricultural University, Harbin 150030, China ] Corresponding author
• Guijun Yang [ National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China, Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China, Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China ]
• Minghui Wang [ College of Electronic Engineering and Information, Northeast Agricultural University, Harbin 150030, China ]
• Weizheng Shen [ College of Electronic Engineering and Information, Northeast Agricultural University, Harbin 150030, China ]
• Xiaowei Teng [ National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China, Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China, Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China ]
• Jinhui Dong [ National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China, Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China, Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605