Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1739-1746, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1739-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1739-1746, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1739-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

INVERSION OF FARMLAND SOIL MOISTURE IN LARGE REGION BASED ON MODIFIED VEGETATION INDEX

J. X. Wang1, B. S. Yu1, G. Z. Zhang2,3, G. C. Zhao1, S. D. He1, W. R. Luo1, and C. C. Zhang1 J. X. Wang et al.
  • 1School of Water Conservancy & Environment of Zhengzhou University, Zhengzhou 450001, China
  • 2CMA • Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou, 450003, China
  • 3Henan Institute of Meteorological Sciences, Zhengzhou 450003, China

Keywords: Soil Moisture, Normalized Difference Vegetation Index, Modified Vegetation Index, Henan Province China, Winter Wheat

Abstract. Soil moisture is an important parameter for agricultural production. Efficient and accurate monitoring of soil moisture is an important link to ensure the safety of agricultural production. Remote sensing technology has been widely used in agricultural moisture monitoring because of its timeliness, cyclicality, dynamic tracking of changes in things, easy access to data, and extensive monitoring. Vegetation index and surface temperature are important parameters for moisture monitoring. Based on NDVI, this paper introduces land surface temperature and average temperature for optimization. This article takes the soil moisture in winter wheat growing area in Henan Province as the research object, dividing Henan Province into three main regions producing winter wheat and dividing the growth period of winter wheat into the early, middle and late stages on the basis of phenological characteristics and regional characteristics. Introducing appropriate correction factor during the corresponding growth period of winter wheat, correcting the vegetation index in the corresponding area, this paper establishes regression models of soil moisture on NDVI and soil moisture on modified NDVI based on correlation analysis and compare models. It shows that modified NDVI is more suitable as a indicator of soil moisture because of the better correlation between soil moisture and modified NDVI and the higher prediction accuracy of the regression model of soil moisture on modified NDVI. The research in this paper has certain reference value for winter wheat farmland management and decision-making.