New data-driven estimation of terrestrial CO2
 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

Abstract. The knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation.

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Parallel Multiclass Support Vector Machine for Remote Sensing Data Classification on Multicore and Many-Core Architectures

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2017.2713126 ◽

2017 ◽

Vol 10 (10) ◽

pp. 4387-4398 ◽

Cited By ~ 5

Author(s):

Weijia Li ◽

Haohuan Fu ◽

Yang You ◽

Le Yu ◽

Jiarui Fang

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Remote Sensing Data ◽

Data Classification ◽

Support Vector ◽

Sensing Data ◽

Multiclass Support Vector Machine ◽

Many Core

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Improving the accuracy of estimation of eutrophication state index using a remote sensing data-driven method: A case study of Chaohu Lake, China

Water SA ◽

10.4314/wsa.v41i5.18 ◽

2015 ◽

Vol 41 (5) ◽

pp. 753 ◽

Cited By ~ 5

Author(s):

Bo Xiang ◽

Jing-Wei Song ◽

Xin-Yuan Wang ◽

J Zhen

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Data Driven ◽

Chaohu Lake ◽

Sensing Data ◽

State Index

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Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China

Hydrology Research ◽

10.2166/nh.2020.184 ◽

2020 ◽

Vol 51 (5) ◽

pp. 942-958 ◽

Cited By ~ 1

Author(s):

Jianzhu Li ◽

Siyao Zhang ◽

Lingmei Huang ◽

Ting Zhang ◽

Ping Feng

Keyword(s):

Remote Sensing ◽

Prediction Models ◽

Meteorological Data ◽

Remote Sensing Data ◽

Kernel Functions ◽

Support Vector ◽

Sensing Data ◽

Model Driven ◽

Drought Prediction ◽

Svm Model

Abstract Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

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