Relationships between soil respiration and hyperspectral vegetation indexes and crop characteristics under different warming and straw application modes

Ding, Sicheng; Yao, Xuewen; Wang, Jin; Deng, Ximing; Zhang, Miaomiao; Long, Jilan; Chen, Shutao; Hu, Zhenghua; Wang, Dan; Wang, Yanling; Wang, Jun; Zhang, Tingting

doi:10.1007/s11356-021-13612-3

Cited by 5 publications

(3 citation statements)

References 59 publications

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“…11,12 Besides the data fit models, the researchers have also developed a physical model, a characteristic coefficient model and a machine learning model to detect soil MC. 13 Similar works have been carried out on moisture monitoring in precision agriculture 14 and textile production. 15 Recently, online monitoring of power equipment via HSI has been impressive, while the attention of the existing work has been mainly given to the application of insulator pollution degree analysis.…”

Section: Introductionmentioning

confidence: 92%

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology

Xia,

Ren,

Liu

et al. 2024

Analyst

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Section: Introductionmentioning

confidence: 92%

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology

Xia,

Ren,

Liu

et al. 2024

Analyst

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“…Recently, scholars have preliminarily explored ESDD and MC pattern recognition and visualization methods by incorporating the spectral database and prior knowledge, inspired by the application of hyperspectral imaging (HSI) technology for fine nondestructive testing [11][12][13][14]. Qiu et al [7] first combined HSI and extreme-learning-machine-based classification method for ESDD detection on the insulators surface, achieving an impressive classification accuracy of over 87.5%.…”

Section: Introductionmentioning

confidence: 99%

Visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber surface with hyperspectral imaging technology

Xia

Ren

Wang

et al. 2023

J. Phys. D: Appl. Phys.

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The application of high temperature vulcanized (HTV) silicone rubber materials in transmission lines insulators has been extensive. However, with the accumulation of pollution and moisture on the surface of HTV silicone rubber, the insulation reliability gradually decreases, which may even lead to flashover. In this paper, hyperspectral imaging technology (HSI) is utilized as a rapid and effective tool for the visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber. Based on the analysis of reflection spectra, it has been determined that the pollution degree has a positive effect and the moisture content has a negative effect on the reflection characteristics of HTV silicone rubber, respectively. Further quantitative analysis of reflectance values at various moisture conditions reveals that exponential relationships can be observed between reflectance values and moisture contents at a certain wavelength. At the same time, linear correlations between reflectance values and pollution degrees (quantified as equivalent salt deposit density, ESDD) at a certain wavelength are also proposed. By integrating the exponential and linear relationships, a double-exponential model is formulated to concurrently characterize the ESDD and moisture content (MC). Furtherly, from the perspective of engineering application, to enhance the model’s robustness, a set of equations combining spectral indexes and spectral shape feature parameters is initially formulated for the calculating of ESDD and MC. Given the synchronous inversion and visualization capabilities of pollution and moisture parameters of the polluted HTV silicone rubber surface, the visual characterization model serves as a valuable tool for protecting against pollution flashover failures and designing power equipment insulation.

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“…of the most important vegetation structure parameters in biogeochemical cycle [3], is defined as half the sum of total leaf area per unit surface area [4]. It is closely related to the transpiration [5], respiration [6], and photosynthesis of crops [7], as well as nitrogen, potassium, and water cycles in the ecosystem [8]. LAI measurement is important for crop growth monitoring and evaluation [9], crop yield prediction [10] and vegetation coverage estimation [11].…”

mentioning

confidence: 99%

Estimating Winter Wheat LAI Using Hyperspectral UAV Data and an Iterative Hybrid Method

Ling,

Zeng,

Shi

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Leaf area index (LAI) is an important indicator 1 for crop growth monitoring. Due to the small number of 2 ground-measured samples, the hybrid method, using the radiative 3 transfer model (RTM) to generate simulated samples and com-4 bining with the regression model, is popular for LAI estimation. 5 However, there is still difference between simulated spectrum 6 and measured spectrum, which may affect the inversion results. 7 In this study, an iterative hybrid method combines BP neural 8 network, PROSAIL model and an optimal sample selection 9 method for crop LAI estimation was proposed. A small number 10 of ground-measured samples and unmanned aerial vehicle (UAV) 11 hyperspectral data were used to estimate LAI firstly. Then the 12 initial LAI result was used as the parameter of PROSAIL model 13 to generate the simulated spectrum. The simulated spectrum with 14 high similarity with the UAV spectrum and corresponding LAI 15 value would be treated as new samples for BP neural network. 16 After several iterations, a reasonable sample set was obtained to 17 estimate winter wheat LAI. The method proposed in this study 18 is evaluated using ground-measured test samples and compared 19 with the common hybrid methods. Results indicate that with 20 the increase of the number of training samples, the accuracy 21 of estimation model is improved (RMSE/MAE decreased from 22 0.4685/0.0301 to 0.4377/0.0272, respectively, while R 2 increased 23 from 0.5857 to 0.6384). Also, the accuracy of proposed iterative 24 hybrid model is higher than that of commonly used hybrid model. 25 The experiments demonstrate the relatively high accuracy of 26 the proposed iterative hybrid method, which could be used for 27 vegetation parameter estimation with only a small number of 28 ground samples.

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Relationships between soil respiration and hyperspectral vegetation indexes and crop characteristics under different warming and straw application modes

Cited by 5 publications

References 59 publications

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology

Visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber surface with hyperspectral imaging technology

Estimating Winter Wheat LAI Using Hyperspectral UAV Data and an Iterative Hybrid Method

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