Spatial Estimation of Soil Moisture and Salinity With Neural Kriging

Zheng, Zicheng; Zhang, Fengrong; Chai, Xurong; Zhu, Zhiping; Ma, Fuying

doi:10.1007/978-1-4419-0211-5_51

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Taking the sensitive vegetation index as the input variable of the model, three methods were used to construct the winter wheat soil salinity inversion model, namely, BPNN, SVM and RF. BPNN is a multi-layer feedforward neural network trained according to the error back propagation algorithm, and it has also been applied to the salt inversion problem [ 39 , 40 ]. SVM is a new machine learning method from linear separable to linear nonseparable based on the principle of minimizing structural risk according to the statistical theory.…”

Section: Methodsmentioning

confidence: 99%

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

Zhao

2020

Sensors

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Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R2 = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective “Satellite-UAV-Ground” collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production.

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Section: Methodsmentioning

confidence: 99%

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

Zhao

2020

Sensors

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“…The Kriging estimation, in a location j, is a weighted average of sample data pi from locations i [22]:…”

Section: Krigingmentioning

confidence: 99%

Spatial interpolation

Fazio

Roisenberg

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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In spatial interpolation domains, a popular method is Kriging. The contribution of this study is to prove mathematically that Kriging and RBF Networks methods produce identical results if properly configured, and that RBF networks are much faster. Complexity was calculated for both methods to show the relative speed of RBF networks. It is shown that both methods share a common structure and, as a consequence, all improvements in one method can be applied to the other. Finally, two experiments were made to show in practice the theoretical results obtained. The RBF networks were 200 times faster than Kriging in one particular experiment, and this difference increases as the data set gets larger.

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“…Moreover, it was widely used in hydrology and pedology for mapping purposes due to its capability of describing nonlinear relationships [50][51][52][53][54]. ANNs have historically been combined with geostatistical methods in hydrological studies for spatial distribution of variables, e.g., soil organic matter, salinity, soil water content and total N, but they have recently been employed for mapping soil properties [55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Topsoil Texture Classes Using a Hybridized Classical Statistics–Artificial Neural Networks Approach and Relief Data

et al. 2022

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The demand for high quality and low-cost spatial distribution information of soil texture classes (STCs) is of great necessity in developing countries. This paper explored digital mapping of topsoil STCs using soil fractions, terrain attributes and artificial neural network (ANN) algorithms. The 4493 soil samples covering 10 out of 12 STCs were collected from the rice fields of the Guilan Province of Northern Iran. Nearly 75% of the dataset was used to train the ANN algorithm and the remaining 25% to apply a repeated 10-fold cross-validation. Spatial prediction of soil texture fractions was carried out via geostatistics and then a pixel-based approach with an ANN algorithm was performed to predict STCs. The ANN presented reasonable accuracy in estimating USDA STCs with a kappa coefficient of 0.38 and pixel classification accuracy percentage of 52%. Hybridizing soil particles with relief covariates yielded better estimates for coarse- and medium-STCs. The results also showed that clay particle and terrain attributes are more important covariates than plant indices in areas under single crop cultivation. However, it is recommended to examine the approach in areas with diverse vegetation cover.

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Spatial Estimation of Soil Moisture and Salinity With Neural Kriging

Cited by 7 publications

References 14 publications

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

Spatial interpolation

Digital Mapping of Topsoil Texture Classes Using a Hybridized Classical Statistics–Artificial Neural Networks Approach and Relief Data

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