Zhao, S., Wang, Q., Yao, Y., Du, S., Zhang, C., Li, J., Zhao, J. (2016). Estimating and Validating Wheat Leaf Water Content with Three MODIS Spectral Indexes: A Case Study in Ningxia Plain, China. , 18(2), 387-398.
S. Zhao; Q. Wang; Y. Yao; S. Du; C. Zhang; J. Li; J. Zhao. "Estimating and Validating Wheat Leaf Water Content with Three MODIS Spectral Indexes: A Case Study in Ningxia Plain, China". , 18, 2, 2016, 387-398.
Zhao, S., Wang, Q., Yao, Y., Du, S., Zhang, C., Li, J., Zhao, J. (2016). 'Estimating and Validating Wheat Leaf Water Content with Three MODIS Spectral Indexes: A Case Study in Ningxia Plain, China', , 18(2), pp. 387-398.
Zhao, S., Wang, Q., Yao, Y., Du, S., Zhang, C., Li, J., Zhao, J. Estimating and Validating Wheat Leaf Water Content with Three MODIS Spectral Indexes: A Case Study in Ningxia Plain, China. , 2016; 18(2): 387-398.

Estimating and Validating Wheat Leaf Water Content with Three MODIS Spectral Indexes: A Case Study in Ningxia Plain, China

Journal of Agricultural Science and Technology
Article 9, Volume 18, Issue 2, 2016, Pages 387-398    PDF (274.69 K)
Authors
S. Zhao* 1; Q. Wang2; Y. Yao3; S. Du4; C. Zhang5; J. Li6; J. Zhao7
1Satellite Environmental Center, Ministry of Environmental Protection, Beijing, 100094, Peoples Republic’s of China.
2State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing, 100101, Peoples Republic’s of China.
3School of Geography, Beijing Normal University. Beijing, 100875, Peoples Republic’s of China.
4Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, 100871, Peoples Republic’s of China.
5College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, 271018, Peoples Republic’s of China.
6Ningxia Provincial Institute of Meteorology, Yinchuan, 750002, Peoples Republic’s of China.
7Xuchang Environmental Monitoring Center, Henan Province, Xuchang, 461000, Peoples Republic’s of China.
Abstract
Water content plays an important role in the process of plant photosynthesis and biomass accumulation. Many methods have been developed to retrieve canopy leaf water content from remote sensing data. However, the validity of these methods has not been verified, which limits their applications. This study estimates the Leaf Water Content (LWC) of winter wheat with three most widely used indexes: Normalized Difference Water Index (NDWI), Simple Ratio (SR), and Shortwave Infrared Perpendicular Water Stress Index (SPSI), as well as MODIS short wave and near infrared data, and then compares remote sensing estimates of vegetation water content with field-measured values measured in concurrent dates. The results indicate that the three indexes are significantly correlated with the LWC of winter wheat at the 0.01 significance level. They all have good accuracy with higher than 90%. The indexes derived from MODIS bands 6 and 2 were better than those from bands 7 and 2 for measuring wheat leaf water content, and the correlations of the former two (NDWI and SR) were stronger than that of SPSI.
Keywords
Near infrared band; remote sensing; Short wave infrared band. Spectral characteristic index; validation
References
Statistics
Article View: 181
PDF Download: 84
Statistics
Number of Journals45
Number of Issues2,171
Number of Articles24,674
Article View24,395,640
PDF Download17,535,414