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Soil moisture retrieval using extremely randomized trees over the Shandian river basin

  • role： First author第一作者
  • Affiliation：

    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：shyaabb@163.com
  • Introduction：程渊，1996年生,男,硕士研究生, 研究方向为定量遥感、智能信息处理与控制。E-mail: shyaabb@163.com
CHENG Yuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：liyuxia@uestc.edu.cn
  • Introduction：李玉霞，1979年生，女，副教授，研究方向为定量遥感，智能信息处理与控制。E-mail:liyuxia@uestc.edu.cn
LI Yuxia1*,  
  • Affiliation：

    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

LI Fan1,  
  • Affiliation：

    School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China

HE Lei2

résumé

Soil moisture plays an important role in the survival of animals and surface plants, the energy and material cycle between atmosphere and surface. Meanwhile, the large-scale monitoring of soil moisture is a critical indicator for water cycle, climate change, agricultural monitoring, ecological environment, geological disasters, fire and other applications. However, because of the influence of soil types, soil structure conditions, terrain characteristics, vegetation environment and human activities, the distribution of soil moisture has spatial heterogeneity characteristic, so it is still hard to find an optimal method to monitor the distribution of soil moisture in large areas (such as watershed scale). In the comprehensive experiments, the Shandian River Basin was selected as the experiment area. Vegetation Indices (VIs) retrieved from MODIS reflectance data and Land Surface Temperature (LST) were calculated as the input parameters, and then the soil moisture measured was taken as the expected output parameters. Further, the random forest model is used to calculate the feature importance of each input parameters to complete feature selection, and a soil moisture inversion model based on extreme random tree was constructed. Compared with empirical models, the soil moisture retrieval model based on extreme random tree has stronger nonlinear expression ability, and can introduce more input parameters; compared with traditional machine learning methods such as Support Vector Machine(SVM) and Neural Network(NN), extreme random tree can achieve better retrieval accuracy on small sample set by integrating several “weak learners” into “strong learners”; Compared with random forest, extreme random tree can reduce the variance and the deviation of the model to achieve better integration effects. Considering the difficulty of surface temperature measurements and the requirements of regional soil moisture monitoring, the research selected Short wave infrared Transformed Reflectance (STR) instead of LST to establish the extreme random tree model, and inversed the soil moisture map of 2°× 2° area covering experiment area of Shandian River Basin. The results show that: (1) when LST is used as the input parameter, the soil moisture retrieval model based on extreme random tree, (root mean square error 0.054 m3m-3 and correlation coefficient 0.69), has better performance than other models (support vector machine and random forest); (2) when STR is used as the input parameter, the root mean square error of prediction result is 0.060 m3m-3 and the correlation coefficient is 0.66. So it is feasible to introduce STR instead of LST to predict soil moisture in large area. Further, the spatial distribution of soil moisture is basically consistent with the actual situation, which can meet the requirements of general application. Focused on the limitation of MODIS data quality and the scale difference between single point measurement data and satellite data, the accuracy of soil moisture estimation still need to be improved. At the same time, due to the lack of long-term measured data of soil moisture, the spatiotemporal scalability of soil moisture inversion model needs to be further verified. Optical remote sensing is greatly affected by the weather, while microwave remote sensing can be the alternative choices for all-weather observation. The multi-source remote sensing data fusion should be attribute more attention to soil moisture retrieval model construction.

mots-clés

soil moisture;the Shandian river;extreme random trees;MODIS;STR

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