Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index

Xin Lu; Hongli Zhao; Yanyan Huang; Shuangmei Liu; Zelong Ma; Yunzhong Jiang; Wei Zhang; Chuan Zhao

doi:10.3390/s22145366

Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index

Sensors (Basel). 2022 Jul 19;22(14):5366. doi: 10.3390/s22145366.

Authors

Xin Lu¹, Hongli Zhao², Yanyan Huang³, Shuangmei Liu¹, Zelong Ma¹, Yunzhong Jiang², Wei Zhang^{4

5}, Chuan Zhao¹

Affiliations

¹ Sichuan Research Institute of Water Conservancy, Chengdu 610072, China.
² Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.
³ School of Software Engineering, Chengdu University of Information Technology, Chengdu 610200, China.
⁴ China Electronics Technology Group Corporation (CETC), Big Data Research Institute Chengdu Branch Co., Ltd., Chengdu 610093, China.
⁵ National Engineering Laboratory for Big Data Application on Improving Government Governance Capabilities, Guiyang 550081, China.

Abstract

Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3/cm3 versus 0.027 to 0.032 cm3/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications.

Keywords: European Space Agency’s Climate Change Initiative (ESA CCI); modified perpendicular drought index (MPDI); physical model; remote sensing; soil moisture (SM); spatial downscaling; water resources management.

MeSH terms

Climate Change
Droughts*
Environmental Monitoring / methods
Remote Sensing Technology / methods
Soil*

Substances

Soil

Grants and funding

This research was funded by the National Key Research and Development Program of China (Grant No. 2021YFC3000201, No. 2018YFC0407705); Research on Key Technologies of shoreline extraction based on machine learning (Grant No. 45-33-2069999); Research on Key Technologies of landslide Dam management and comprehensive utilization of barrier lake in Mahu reservoir; Special Project of Basic Scientific Research of China Institute of Water Resources and Hydropower Research (No. WR0145B272016, WR0145B012017).