Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China

Gao, Jinlong; Meng, Baoping; Liang, Tiangang; Feng, Qisheng; Ge, J.; Yin, Jianpeng; Wu, Caixia; Cui, Xia; Hou, Mian; Liu, Jie; Xie, Hongjie

doi:10.1016/j.isprsjprs.2018.11.015

Cited by 55 publications

(47 citation statements)

References 70 publications

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“…Because this study is unique in this regard, there is a lack of literature to compare with. Still, the accuracy found here is similar to or even higher than those obtained by modeling different stresses effects in plants [21,[24][25][26][27].…”

Section: Discussionsupporting

confidence: 77%

“…Recently, machine learning approaches have been used in modeling the hyperspectral response of different conditions associated with vegetation [21]. The popular techniques used for analyzing data include regression analysis, vegetation indices, linear polarizations, wavelet-based filtering, and, currently, machine learning algorithms like random forest, decision tree, support vector machine (SVM), k-nearest neighbor (kNN), artificial neural networks (ANN), naïve Bayes (NB), and others [22][23][24][25]. To evaluate the hyperspectral response of plants, machine learning has already been implemented in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

et al. 2019

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Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.

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Section: Discussionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

et al. 2019

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“…Machine learning algorithms are a robust and intelligent technique that can model different types of data [43,44]. These algorithms have the advantage of being non-parametric and non-linear while being able to analyze noised and imperfect data [45][46][47]. They are also capable to perform numerous combinations and calculations in a matter of seconds, achieving relative success in remote sensing applications regarding plant analysis [48,49].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

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“…Random forest (RF) is a machine learning method widely used in the field of classification and regression in recent years [25]. Multiple soil properties have been linked to the Vis-NIR spectrum through this algorithm, such as soil organic carbon (SOC) [26], soil cadmium (Cd) [27], forage phosphorus (P) [28] and soil pH [29]. Numerous research results indicate that RF provides better prediction results than the classical partial least-squares regression (PLSR) [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Zhu

Zhang

Wang

et al. 2020

Sensors

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Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

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Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China

Cited by 55 publications

References 70 publications

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

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