Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach

Li, Qiquan; Yue, Tianxiang; Wang, Changquan; Zhang, Wenjiang; Yang, Yu; Li, Bing; Yang, Juan; Bai, Gen-chuan

doi:10.1016/j.catena.2012.11.012

Cited by 106 publications

(65 citation statements)

References 34 publications

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“…This is not the case for bias, which is in several cases higher for the estimation of total C org stocks. The RMSE of our estimations of soil (ranging from 40.79%-58.99%) and total C org (ranging between 52.67% and 72.52%) are in line with results of other studies using SOM [12,19,51] or kNN [9] to classify C org , where values range between 44.85% and 70.49%. RMSE for vegetation C org is higher (118.46%-158.32%) in our estimation.…”

Section: Discussionsupporting

confidence: 78%

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Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Suchenwirth

Stümer²,

Schmidt

et al. 2014

Forests

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Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (C org ) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial C org distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total C org stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest C org stocks. While vegetation C org has very high RMSEs, outcomes for soil and total C org stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations. OPEN ACCESSForests 2014, 5 1636

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Section: Discussionsupporting

confidence: 78%

“…Li et al [19] applied an artificial neural network based approach for predicting soil matter across China. For the estimation of C org , Stümer et al [12] successfully applied SOM and compared it with the kNN algorithm for the assessment of biomass (and thus C org ) in Thuringian forests.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Suchenwirth

Stümer²,

Schmidt

et al. 2014

Forests

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“…Alternative powerful, yet more complex, structures involve recurrent artificial neural networks (RANNs) and radial basis function neural networks (RBFNN), which only recently went into application to map vegetation properties (Chai et al, 2012;Li et al, 2013;Wang et al, 2013). Due to the complexity of ANNs, the incentive increasingly is to replace them in many applications with alternative, simpler to train MLRAs.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Verrelst

Camps‐Valls

Muñoz-Marí

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

523

428

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“…Not only did it need to re-modeled and re-trained as the model lacked robustness and stability, but it also avoided over-fitting because the sample variations disrupted the one-to-one relationships between the pixel value of the auxiliary variables and the samples of SOM. In other words, the first subset produced was random, yet the validation set was independent in the subsequent process of modeling analysis [14,31]. Table 1 summarizes the descriptive statistics of SOM and the auxiliary variables at all sites in the study area.…”

Section: Accuracy Evaluation Of Prediction Performancementioning

confidence: 99%

“…In fact, RK still displays a low prediction accuracy because of its weak analytical ability in non-linear relationships and layer structures between target data and multi-source auxiliary variables. Since the development of artificial intelligence and machine learning, neural networks (NN) have been used to solve the complex non-linear problems between soil properties and auxiliary variables, which results in a higher precision than when using classic linear methods [29][30][31]. However, traditional artificial neural networks (ANN) have a low implementation efficiency, which is needed to adjust the complex parameters from the algorithm structure and to avoid the influence of a locally optimal solution; in particular, they require a longer running time when the mapping resolution is increased.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

Song

Lian-an

et al. 2017

Sustainability

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An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0-20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R 2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg −1 ). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale.

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Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach

Cited by 106 publications

References 34 publications

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

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