Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Meng, Xiangtian; Bao, Yilin; Ye, Qiang; Liu, Huanjun; Zhang, Xinle; Tang, Haitao; Zhang, Xiaohan

doi:10.3390/rs13122273

Cited by 59 publications

(39 citation statements)

References 76 publications

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“…The hyperspectral reflectance of the canopy contains redundancy. Hence, the db4 mother wavelet function was employed in decomposition and reconstruction on a binary scale to remove noise [84][85][86]. The db4 mother wavelet function decomposes the hyperspectral reflectance into feature spectra of different sub-bands, where each layer characterizes specific details of the original signal; the reconstructed spectra emphasize the relevant dominant signals and attenuate or filter minor signals [87].…”

Section: Prerequisites For Accurate Estimationmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

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Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

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Section: Prerequisites For Accurate Estimationmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

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“…To our knowledge, no reference was found about simulated spectra for other forthcoming hyperspectral satellites such as CHIME, SHALOM or HypXim. Some recent Chinese studies used the hyperspectral data of the Gaofen-5 satellite with a 30 m resolution and bandwidth of 60 km [40][41][42]. In parallel, with the emerging of precision agriculture, field-scale approaches to SOC modeling have also been developed from satellite sensors with higher spatial resolution: IKONOS with 4 m resolution [43], PlanetScope with 3 m resolution [44] and Worldview 2 with 2.5 m resolution [45,46].…”

Section: Satellites Spectral Informationmentioning

confidence: 99%

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

et al. 2022

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There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

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“…George et al [47] used different hyperspectral indices generated by EO-1 Hyperion data and the SVM method to draw various soil salinity severity levels in the Mathura region of the Indo-Gangetic plain of India, and the overall classification accuracy was 78.13%. Based on GF-5 data, Meng et al [48] used the TE model to predict SOMC in Mingshui County, Heilongjiang Province, China, and the root mean square error of the model was 3.36 g/kg. In this study, six SVM models (linear SVM, quadratic SVM, cubic SVM, fine Gaussian SVM, medium Gaussian SVM, coarse Gaussian SVM) and two TE models (boosted trees, bagged trees) were compared for SOMC estimation in bare soil and vegetation-covered areas.…”

Section: Application Assessment Of the Optimal Sismentioning

confidence: 99%

Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

Yang

Shang

Xiao

et al. 2022

IJGI

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Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient () and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654, 679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551, 1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654, 679) and V-RR-DSI(551, 1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.

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Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Cited by 59 publications

References 76 publications

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

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