Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region

Guo, Hengliang; Zhang, Rongrong; Dai, Wenhao; Zhou, Xiaowen; Zhang, Dujuan; Yang, Yaohuan; Cui, Jian

doi:10.3390/agronomy12092111

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…However, the evaluation of model performance, as measured using R 2 and RMSE, varies significantly across different studies because of variations in sample collection methods, spectral processing techniques, and model construction approaches. These variations can significantly impact the accuracy and reliability of spectral modeling [52,53]. The PLSR model can identify system information and noise and permits regression modeling provided that the number of sample points is less than the number of variables.…”

Section: Feasibility Of Inverse Som Using Hyperspectral Datamentioning

confidence: 99%

Spectral Heterogeneity Analysis and Soil Organic Matter Inversion across Differences in Soil Types and Organic Matter Content in Dryland Farmland in China

Shen,

Zhang,

Xia

2023

Sustainability

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Soil organic matter (SOM) plays an important role in agricultural production and arable land quality improvement. Hyperspectral technology enables frequent surveys over large areas. In this study, we explored the spectral heterogeneity of differences in soil types and SOM content, and proposed a method for measuring SOM content in large areas using spectroscopy. The results indicate regional variations in factors affecting soil spectral absorption peaks, with noticeable latitudinal disparities. The first-order differential partial-least-squares method provided the best prediction for the SOM inversion. The coefficient of determination (R2) for the SOM inversion model was 0.93, and the root mean square error (RMSE) was 3.42, with an 8.49 g/kg difference in the SOM content. When the difference in SOM content fell between 8 and 15 g/kg, the inversion effect model performed best. The optimal model R2 exceeded 0.95, and the RMSE was less than 5. The comprehensive analysis showed that the organic matter content was an important factor affecting the SOM content estimate and must be considered in the real process. In addition, it is crucial to categorize soil samples on the basis of distinct soil types while maintaining a consistent range of SOM content within the same soil type, ideally between 8 and 15 g/kg. Subsequently, the first-order differential partial least squares method is applicable. These results are expected to contribute to the acquisition of high-quality information on variations in the SOM of complex large-scale areas.

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Section: Feasibility Of Inverse Som Using Hyperspectral Datamentioning

confidence: 99%

Spectral Heterogeneity Analysis and Soil Organic Matter Inversion across Differences in Soil Types and Organic Matter Content in Dryland Farmland in China

Shen,

Zhang,

Xia

2023

Sustainability

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“…Especially for spectral estimation of soil salt, changes in water content will affect the content and movement of soil salt, which will have a greater impact on the spectrum [35]. Therefore, it is necessary to research the spectral impact of soil moisture and its removal methods [36]. In addition, if the estimation model based on the outdoor air-dried soil spectrum is extended to the outdoor wet soil spectrum [37], the impact of soil moisture on the spectrum must also be eliminated [38].…”

Section: Background and Motivationmentioning

confidence: 99%

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

Zhao,

Wang,

Zhang

2023

Preprint

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Soil salinization is an important limiting factor for agricultural and environmental sustainable development. To achieve rapid and accurate identification of soil salt content, a classification model called Attention-bidirectional gate recurrent unit recurrent neural network (Att-BiGRU-RNN) is designed, incorporating the fusion of attention mechanism. In the encoding and decoding modules of the model, BiGRU and RNN structures are used, enabling the extraction of deep spectral features by leveraging the correlation between spectral information in different bands of hyperspectral data. The attention mechanism is introduced to dynamically allocate weight information based on the differences in spectral information, thereby increasing the contribution of important spectral features to the classification model and improving the accuracy of the model. The research area is initially set in Dinge County, Shaanxi Province, China. Field spectroscopy measurements of 120 samples of original and air-dried soils are conducted using a ground-based spectrometer. Different mixed models for estimating soil salt content, including FDT-SVR, FDT-CNN, BiGRU-RNN, and Att-BiGRU-RNN, are constructed and validated and compared. The results show that compared to other models, the Att-BiGRU-RNN model optimized by the attention mechanism exhibits the highest prediction accuracy, with a coefficient of determination R2 = 0.932 and root mean square error RMSE = 0.012. Additionally, the model's recall curve at different precision levels is obtained to meet the parameter selection requirements under different estimation demands. This method can effectively identify areas with high soil salt content or severe salinization based on portable hyperspectral sensors and unmanned aerial vehicle platforms, and statistically analyze the distribution of soil salt content.

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“…Studies have shown that the d-factor can be used to evaluate the uncertainty of an estimation model [19,48]. The degree of uncertainty of an estimation model is proportional to the calculated value of the d-factor; that is, the degree of uncertainty of the estimated model will increase with the increase in the calculated value of the d-factor, and the d-factor calculation formula is as follows:…”

Section: Construction and Evaluation Of The Tn Content Estimation Modelmentioning

confidence: 99%

Estimation of the Total Soil Nitrogen Based on a Differential Evolution Algorithm from ZY1-02D Hyperspectral Satellite Imagery

Zhang

Cui

et al. 2023

Agronomy

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Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content.

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Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region

Cited by 11 publications

References 51 publications

Spectral Heterogeneity Analysis and Soil Organic Matter Inversion across Differences in Soil Types and Organic Matter Content in Dryland Farmland in China

Spectral Heterogeneity Analysis and Soil Organic Matter Inversion across Differences in Soil Types and Organic Matter Content in Dryland Farmland in China

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

Estimation of the Total Soil Nitrogen Based on a Differential Evolution Algorithm from ZY1-02D Hyperspectral Satellite Imagery

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