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Open AccessArticle

A Cuboid Model for Assessing Surface Soil Moisture

by Xiufang Zhu 1,2, Yaozhong Pan 3,4,*, Junxia Wang 5 and Ying Liu 5
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
School of Geographical Sciences, Qinghai Normal University, Xining 810016, China
Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3034;
Received: 12 November 2019 / Revised: 11 December 2019 / Accepted: 12 December 2019 / Published: 16 December 2019
This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (ΔLST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2/1/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0–0.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters. View Full-Text
Keywords: feature parameter; soil moisture; inversion; remote sensing feature parameter; soil moisture; inversion; remote sensing
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Zhu, X.; Pan, Y.; Wang, J.; Liu, Y. A Cuboid Model for Assessing Surface Soil Moisture. Remote Sens. 2019, 11, 3034.

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