Soil Moisture Prediction Using Support Vector Machines

Gill, M. Kashif; Asefa, Tirusew; Kemblowski, M. W.; McKee, Mac

doi:10.1111/j.1752-1688.2006.tb04512.x

Cited by 251 publications

(127 citation statements)

References 17 publications

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“…However, it inevitably suffers from the overfitting problem. Fortunately, some researchers reported the SVM method resolves the problem of overfitting encountered when analyzing high-dimensional data [36] and has been used to soil moisture [37], hourly typhoon rainfall [38], long-lead stream flows [39], leaf area index, and leaf chlorophyll density [40,41]. These studies have shown that the SVM approach is preferable to the ANN approach for these applications because of its greater generalizability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

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Abstract:The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation OPEN ACCESS Remote Sens. 2015, 7 14940 accuracies using the six metrics that were the coefficients of determination for the calibration (R 2 C) and validation (R 2 V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R² C, R² V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R² C, R² V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings...

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Section: Introductionmentioning

confidence: 99%

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

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“…In using SVMs, an upper bound is minimized by the principle of structural risk minimization (SRM) whereas in using ANNs, traditional empirical risk minimization (ERM) is employed [30]. Recently, SVMs have been applied to estimate soil moisture [31], leaf area index, leaf chlorophyll density [32] and leaf infections [33].…”

Section: Introductionmentioning

confidence: 99%

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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Abstract:Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R 2 ) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation.

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“…This makes SVM a powerful tool for modeling the nonlinear complex environmental problems (Bhagwat and Maity 2012). Several studies reported the use of SVM in forecasting the soil water (Wu et al 2008), soil moisture prediction (Gill et al 2006), estimation of soil hydraulic parameters (Twarakavi et al 2009), modeling soil diffuse reflectance spectra (Rossel and Behrens 2010) and soil type classification (Kovačević et al 2010). Liao et al (2014) also used SVM, multiple stepwise regression (MSR) and ANN models to estimate the CEC from easily measured physicochemical properties (e.g., texture, soil organic matter, pH) based on 208 soil samples in Qingdao City, China.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction

Jafarzadeh

Pal

Servati

et al. 2015

Int. J. Environ. Sci. Technol.

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The aim of this study was to compare the performance of support vector machine and artificial neural network techniques to predict the soil cation exchange capacity of an agricultural research station in terms of soil characteristics (clay, silt, sand, gypsum, organic matter). The data consist of 380 soil samples collected from different horizons of 80 soil profiles located in the Khoja (Khajeh) region of Azerbaijani provinces, Iran. The support vector machine and artificial neural network models predict the cation exchange capacity from the above soil characteristics of the samples. The models' results are compared using three criteria, i.e., root-mean-square errors, NashSutcliffe and the correlation coefficient. A comparison of support vector machine results with artificial neural network method indicates that artificial neural network is better than the support vector machine method in prediction of the cation exchange capacity.

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Soil Moisture Prediction Using Support Vector Machines

Cited by 251 publications

References 17 publications

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction

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