Optimization for Vis/NIRS Prediction Model of Soil Available Nitrogen Content

Liusan, 汪六三 Wang; Cuiping, 鲁翠萍 Lu; Ru-jing, 王儒敬 Wang; Wei, 黄伟 Huang; Hong-yan, 郭红燕 Guo; Yu-bing, 汪玉冰 Wang; Zhi-dan, 林志丹 Lin; Jian, 王键 Wang; Qing, 蒋庆 Jiang; Liang-tu, 宋良图 Song

doi:10.3788/fgxb20183907.1016

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, there are four main methods of SM remote-sensing monitoring, namely thermal inertia [3,4], microwave remote sensing [5], the vegetation index [6], and hyperspectral remote sensing. By comparing these methods, the application of hyperspectral remote sensing in retrieving SM has attracted the attention of researchers [7][8][9][10][11][12][13][14][15][16], because it can be used to identify the absorption characteristics of SM at different spectral bands with high spectral resolution and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information

Yuan

Wang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

The variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface water. However, remote sensors do not measure soil water content directly. Therefore, it is of great importance to establish a SM retrieval model. In this paper, the relationship between SM and diffuse reflectance was first derived using the absorption coefficient and scattering coefficient related to SM. Then, based on Kubelka–Munk (KM) theory, the SM retrieval model using reflectance information was further derived, which is a semi-empirical model with an unknown parameter obtained either from fitting or from experimental measurements. The validity and reliability of the model were confirmed with the validation set. The results showed that the root mean square errors of prediction (RMSEPs) of four soils were generally less than 0.017, while the coefficients of determination (R2s) of four soils were generally more than 0.85, and the ratios of the performance to deviation (RPDs) of four soils were greater than 2.5 (470–2400 nm). Therefore, the model has high prediction accuracy, and can be well applied to the prediction of water content in different sorts of soils.

show abstract

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information

Yuan

Wang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

Hyper-spectral estimation of soil organic matter in apple orchard based on CWT

Gao

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

In the quantitative estimation by spectra of soil organic matter (SOM), there are great difficulties in the extraction of characteristic spectral information. It is difficult to effectively improve the correlation with SOM by ordinary spectral transformations, and the spectral estimation models are not high in accuracy and applicability. The purpose of this paper is to explore a more accurate and more suitable non-destructive evaluation model for apple orchard soil. In this paper, an apple orchard in Shuangquan Town, Changqing District, Jinan City, was selected as the study area. The continuous wavelet transform (CWT) was used to process the original soil spectra at multiple scales, and the effect of the estimation model on the SOM correlation at different resolutions was investigated. The results showed that the CWT treatment significantly improved the degree of spectral response to SOM in the orchard. Compared with the original spectra, the prediction accuracy of the model constructed by CWT was higher. After wavelet algorithm processing, the prediction ability of the model tends to increase with the decrease of spectral resolution. The best prediction accuracy was found at the spectral resolution of 20 nm, with the coefficient of determination R2= 0.7547 and the root means square error RMSE=0.0042; the validation accuracy R2=0.8015 and the root mean square error RMSE=0.0039. The model achieved a more satisfactory prediction result at the spectral resolution of 5 nm also. The results show that the broad-band spectral data processed by CWT can be used for accurate monitoring of SOM in apple orchards.

show abstract

Optimization for Vis/NIRS Prediction Model of Soil Available Nitrogen Content

Cited by 2 publications

References 0 publications

Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information

Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information

Hyper-spectral estimation of soil organic matter in apple orchard based on CWT

Contact Info

Product

Resources

About