based on Landsat 8 Remote sensing image of the northern region of Changchun
Abstract
with Landsat 8 Remote sensing image to data source , using flaash atmospheric Correction Model
Atmospheric correction of remote sensing images ,Combining field soil sampling Organic matter Assay
Data , using stepwise regression analysis , quantitative inversion of soil organic matter content in the study
area . results show , Soil organic matter content with Landsat 8 Remote sensing image reflectivity has strong
negative correlation in near infrared band , An appropriate mathematical transformation of the reflectivity can
effectively increase the correlation with the organic matter , The stepwise regression model established by this
method , Its decision factor r2 =0. 925, Total root Variance Flmse =0. 171, describes the inverse The model has
higher precision and stability . based on the above inversion model , Combining remote sensing image
classification results , inversion of soil organic matter content in the study area , knot to show , content of soil
organic matter in the study area showed a tendency of East High west , East , The content of soil organic matter in
the South is generally higher than that of 3, and West , North Region Soil organic matter content is generally
lower than 2%.
References
Yang , primary Red , Jia Wei, , etc,----------() * . organic matter content of different soil types in Three Rivers
source area hyperspectral inversion J . Remote sensing technology and Applications , 2015, 1 : 186 -198. 2. Lanzeying , Liu yang . High-spectral indirect inversion model of heavy metal content in soil of Lean
River basin ]. type and its spatial distribution characteristics J. Geography and Geographic information
Science , 2015,31 (3) :26-31. 3. tensioned l , qu Wei , Yinguanghua , etc . surface soils based on multispectral remote sensing images have spatial
pattern inversion J . Journal of Applied Ecology , 2010,21(4) : 884-888. 4. Gu Xiaohe, Wang Yu, , Pan Yu Spring, etc. . Is based on the HJ 1 A - HSI The arable land of hyperspectral imagery
has mass Remote sensing inversion E . Geography and Geographic information Science , 2011,27 (6) : 70-73. 5. li hong. . The distribution and hyperspectral inversion of soil organic matter in the stilling zone of Guanting
Reservoir research [D]. Beijing : Capital Normal University ,2014. 6. Xu Jianbo, Song Lixeng, , Zhao's heavy , etc . near a to Maduo County grassland vegetation in the Yellow River
source Area degraded Remote sensing dynamic monitoringJ . Arid Area Geography , 2012,35 (4) : 615-622. 7. shadow , Jiang , Lin Nan. . Resource Number C star Data in land use classification Apply J . Science and
Remote Sensing | 7
engineering , 2014,14:260-264. 8. Zhang Juan , Shu , Yiaoxia , etc . Estimating soil total nitrogen content based on hyperspectral
J . Journal of Natural Resources , in 5 : 881-890. 9. Xu Han Autumn, Tangfi . New 1 Generation Landsat series Satellite : Landsat 8 remote image new increasing
features and their ecological significance J ]. Ecology Report , 2013,: 3249-3257. 10. Dong Lili , Wu Kening , Wei Hongbin , , and so on . constraints on the quality of arable land in the main grain
producing areas of central China and Countermeasures for improvement J . Jiangsu Agricultural Science , 2016,44:419-424. Doi : 10.15889/ J . ISSN . 1002-1302.2016.12.127
Copyright (c) 2018 Remote Sensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution-Noncommercial 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit, that the work is not used for commercial purposes, and that in the event of reuse or distribution, the terms of this license are made clear. With this license, the authors hold the copyright without restrictions and are allowed to retain publishing rights without restrictions as long as this journal is the original publisher of the articles.
