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Remote sensing drought factor integration based on machine learning can improve the estimation of drought in arid and semi-arid regions

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A Correction to this article was published on 31 January 2023

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Abstract

Drought usually occurs as a result of an imbalance between water supply and demand. Drought indices, an important tool for studying drought, are commonly used for drought monitoring and drought risk assessment. The ground-based drought indices, with high accuracy, are limited in the area monitored. In contrast, the remotely sensed drought index covers a large area but with poor accuracy. Data-driven data fusion–based estimation of ground indices helps to fill this gap. The overall objective is to determine whether various remotely sensed drought factors can effectively monitor drought in arid and semi-arid northern China. In this study, the ground-based drought index SPEI was reconstructed by using remotely sensed drought factors, divided from the Global Precipitation Measuring Mission GPM, GLDAS, and MODIS satellite sensors as well as environmental covariates. Based on climate elements, soil elements, vegetation, and environmental covariate elements, a composite drought index CDIS was established in this study. In this study, the single drought index, multivariate linear integrated drought index, and bias-corrected random forest method were used as the prediction model. Based on ground truth historical climate data as reference data, the performance of the model-predicted composite drought index CDIS in drought monitoring was evaluated. Our results indicate that the drought index on 1-month time scale has no significant advantage over the maximum single drought index. The bias-corrected random forest model with drought index predictions outperformed multiple linear regression and single drought index, with higher prediction accuracy in semi-arid and semi-humid regions than in arid regions. Compared with the ground station drought indices, the drought map based on bias-corrected random forest shows visual and statistical consistency. Under the background of a non-homogeneous and complex surface environment, the model prediction results integrating environmental covariates performed best, and the method used in the study was effective and could be extended and applied. For areas where information is scarce, remote sensing data can be easily extended to regional scale monitoring, which can better achieve high-precision drought monitoring at the regional scale.

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Data availability

All the datasets are available in the repository of Google Earth Engine (GEE). The links to each, along with their titles, are mentioned below:

MOD09A1.061, Terra Surface Reflectance 8-Day Global 500 m, https://code.earthengine.google.com/.

MOD11A2.061, Terra Land Surface Temperature and Emissivity 8-Day Global 1 km,

https://code.earthengine.google.com/.

MOD13A3.v006, MODIS/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid,

https://lpdaac.usgs.gov/products/mod13a3v006/.

GLDAS-2.1, Global Land Data Assimilation System, https://code.earthengine.google.com/.

GPM, Global Precipitation Measurement (GPM) v6, https://code.earthengine.google.com/.

MCD12Q1v006, MODIS/Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid, https://lpdaac.usgs.gov/products/mcd12q1v006/.

Ground in situ climate datasets, http://data.cma.cn/.

Code availability

Not applicable.

Change history

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Funding

This research was funded by the key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region (No. 2021D01D06) and the National Natural Science Foundation of China (No. 41961059).

Author information

Authors and Affiliations

  1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China

    Junyong Zhang, Jianli Ding, Jinjie Wang, Lijing Han, Xiaohang Li & Jie Liu

  2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830017, China

    Junyong Zhang, Jianli Ding, Jinjie Wang, Lijing Han, Xiaohang Li & Jie Liu

  3. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, 830017, China

    Junyong Zhang, Jianli Ding, Jinjie Wang, Lijing Han, Xiaohang Li & Jie Liu

  4. Technology Innovation Center for Central Asia Geo-Information Exploitation and Utilization, Ministry of Natural Resources, Xinjiang University, Urumqi, 830046, China

    Jianli Ding

  5. School of Economics and Management, University of Chinese Academy of Sciences, Zhongguancun Nansantiao, Haidian District, Beijing, 100190, China

    Hua Lin

Authors
  1. Junyong Zhang
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  6. Xiaohang Li
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Contributions

Conceptualization, Junyong Zhang and Jianli Ding; writing—review, editing, and formal analysis, Junyong Zhang; writing—original draft preparation, Junyong Zhang; visualization, Junyong Zhang.; project administration, Junyong Zhang; meteorological datasets, Jinjie Wang; language modification, Hua Lin; statistical software, Lijing Han; pre-processing of images, Xiaohang L; acquiring images, Jie Liu.

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Correspondence to Jianli Ding.

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The original online version of this article was revised: Fig. 13 is wrong due to an oversight.

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Zhang, J., Ding, J., Wang, J. et al. Remote sensing drought factor integration based on machine learning can improve the estimation of drought in arid and semi-arid regions. Theor Appl Climatol 151, 1753–1770 (2023). https://doi.org/10.1007/s00704-022-04305-z

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