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

Authors
S. Zhao 1
Q. Wang 2
Y. Yao 3
S. Du 4
C. Zhang 5
J. Li 6
J. Zhao 7
1 Satellite Environmental Center, Ministry of Environmental Protection, Beijing, 100094, Peoples Republic’s of China.
2 State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing, 100101, Peoples Republic’s of China.
3 School of Geography, Beijing Normal University. Beijing, 100875, Peoples Republic’s of China.
4 Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, 100871, Peoples Republic’s of China.
5 College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, 271018, Peoples Republic’s of China.
6 Ningxia Provincial Institute of Meteorology, Yinchuan, 750002, Peoples Republic’s of China.
7 Xuchang 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

1. Bowyer, P. and Danson, F. M. 2004. Sensitivity of Spectral Reflectance to Variation in Live Fuel Moisture Content at Leaf and Canopy Level. Remote Sens. Environ., 92(3): 297–308.
2. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S. and Grégoire, J. M. 2001. Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain. Remote Sens. Environ., 77: 22-33.
3. Chen, D., Huang, J. and Jackson, T. J. 2005. Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near- and Short-wave Infrared Bands. Remote Sens. Environ., 98:225-236.
4. Cheng, Y. B., Ustin, S. L., Riaño, D. and Vanderbilt, V. C. 2008. Water Content Estimation from Hyperspectral Images and MODIS Indexes in Southeastern Arizona. Remote Sens. Environ., 112(2): 363-374.
5. Cheng, Y. B., Zarco-Tejada, P. J., Riaño, D., Rueda, C. A. and Ustin, S. L. 2005. Estimating Vegetation Water Content with Hyperspectral data for Different Canopy Scenarios: Relationships between AVIRIS and MODIS Indexes. Remote Sens. Environ., 105(4): 354-366.
6. Darvishsefat, A. A., Abbasi, M. and Schaepman, M. 2011. Evaluation of Spectral Reflectance of Seven Iranian Rice Varieties Canopies. J. Agr. Sci. Tech., 13: 1091-1104.
7. Eslam, B. P. 2009. Evaluation of Physiological Indices, Yield and Its Components as Screening Techniques for Water Deficit Tolerance in Oilseed Rape Cultivars. J. Agr. Sci. Tech., 11: 413-422.
8. Fensholt, R. and Sandholt, I. 2003. Derivation of a Shortwave Infrared Water Stress Index from MODIS Near- and Shortwave Infrared Data in a Semiarid Environment. Remote Sens. Environ., 87: 111-121.
9. Gao, B. 1996. NDWI: A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ., 58: 257-266.
10. Ghasemloo, N., Mobasheri, M. R. and Rezaei, Y. 2011. Vegetation Species Determination Using Spectral Characteristics and Artificial Neural Network (SCANN). J. Agr. Sci. Tech., 13: 1223-1232.
11. Ghulam, A., Li, Z.L., Qin, Q. M., Tong, Q. X., Wang, J.H., Kasimu, A. and Zhu, L. 2007. A Method for Canopy Water Content Estimation for Highly Vegetated Surfaces–shortwave Infrared Perpendicular Water Stress Index. Sci. China Series D: Earth Sci., 50(9): 1359-1368.
12. Ghulam, A., Li, Z., Qin, Q., Yimit, H. and Wang, J. H. 2008. Estimating Crop Water Stress with ETM+ NIR and SWIR Data. Agr. Forest Meteorol., 148: 1679-1695.
13. Hardisky, M. A., Klemas, V. and Smart, R. M. 1983.The Influence of Soft Salinity, Growth Form, and Leaf Moisture on the Spectral Radiance of Spartina alterniflora Canopies. Photogr. Eng. Remote Sen., 49: 77-83.
14. Hunt, E. R., Li, L., Yilmaz, M. T. and Jackson, T. J. 2011. Comparison of Vegetation Water Contents Derived from Shortwave-infrared and Passive-microwave Sensors over Central Iowa. Remote Sens. Environ., 115: 2376–2383.
15. Jackson, T. J., Chen, D., Cosh, M., Li, F. Q., Anderson, M., Walthall, C., Doriaswamy, P. and Hunt, E. R. 2004. Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans. Remote Sens. Environ., 92: 475-484.
16. Jacquemoud, S., Bacour, C., Poilvé, H. and Frangi, J. -P. 2000. Comparison of Four Radiative Transfer Models to Simulate Plant Canopies Reflectance: Direct and Inverse Mode. Remote Sens. Environ., 74(3): 471–481.
17. Jordan, C.F. 1969. Derivation of Leaf Area Index from Quality of Light on the Forest Floor. Ecology, 50:663–666.
18. Kramer, P. J. 1983. Water Relations of Plants. Academic Press, New York.
19. Li, Y. X., Yang, W. N., Tong, L., Jian, J. and Gu, X. F.2009. Remote Sensing Quantitative Monitoring and Analysis of Fuel Moisture Content Based on Spectral Index. Acta Optica Sinica., 29(5): 1403-1407. (in Chinese with English Abstract)
20. Maki, M., Ishiahra, M. and Tamura, M. 2004. Estimation of Leaf Water Status to Monitor the Risk of Forest Fires by Using Remotely Sensed Data. Remote Sens. Environ., 90: 441-450.
21. Mamnouie, E., Ghazvini, R. F., Esfahani, M. and Nakhoda, B. 2006. The Effects of Water Deficit on Crop Yield and the Physiological Characteristics of Barley (Hordeum vulgare L.) Varieties. J. Agr. Sci. Tech., 8: 211-219.
22. Mei, A. X., Peng, W. L., Qin, Q. M. and Liu, H. P. 2001. Remote Sensing Introduction. Higher Education Press, Beijing, 240 PP. (in Chinese)
23. Mirzaie, M., Darvishzadeh, R., Shakiba, A., Matkan, A. A., Atzberger, C. and Skidmore, A. 2014. Comparative Analysis of Different Uni- and Multi-variate Methods for Estimation of Vegetation Water Content Using Hyper-spectral Measurements. Int. J. Appl. Earth Obs., 26:1–11.
24. Mousivand, A., Menenti, M., Gorte, B. and Verhoef, W. 2014. Global Sensitivity Analysis of the Spectral Radiance of a Soil–vegetation System. Remote Sens. Environ., 145: 131–144.
25. Pan, R. C. 2004. Plant physiology. 5th Edition, Higher Education Press, Beijing.
26. Paul, B. and Danson, F. M. 2004. Sensitivity of Spectral Reflectance to Variation in Live Fuel Moisture Content at Leaf and Canopy Level. Remote Sens. Environ., 92(3): 297–308.
27. Peñuelas, J., Filella, .I, Biel, C., Serrano, L. and Save, R. 1993. The Reflectance at the 950–970 nm Region as an Indicator of Plant Water Status. Int. J. Remote Sens., 14: 1887–1905.
28. Ping, X. Y., Jia, B. R., Yuan, W. P., Wang, F. Y., Wang, Y. H., Zhou, L., Xu, Z. Z. and Zhou, G. S. 2007. Biomass Allocation of Leymus chinensis Population: A Dynamic Simulation Study. Chin .J. Appl. Ecol., 18(12): 2699- 2704. (in Chinese with English Abstract)
29. Qi, S. H., Zhang, Y. P., Niu, Z., Wang, C. Y. and Zheng, L.2005. Application of Water Deficit Index in Drought Monitoring in China with Remote Sensing. Acta Pedologica Sinica., 42(3):367-372 (in Chinese with English Abstract)
30. Tadesse, T., Brown, J. F. and Hayes, M. J. 2005. A New Approach for Predicting Drought-related Vegetation Stress: Integrating Satellite, Climate, and Biophysical Data over the US Central Plains. ISPRS J. Photogramm. Remote Sens., 59(4): 244–253.
31. Tanner, C. B. 1963. Plant Temperature. Agron. J., 50:210-211.
32. Tian, Q. J., Gong, P., Zhao, C. J. and Guo, X. W. 2000. The Feasibility Analysis of Estimating Wheat Water Content by Its Spectral Reflectance. Chin. Sci. Bull., 45(20): 2645-2650. (in Chinese with English Abstract)
33. Tian, Y. C., Zhu, Y., Cao, W. X. and Dai, T. B.2004. Relationship between Canopy Reflectance and Plant Water Status of Wheat. Chin. J. Appl. Eco., 15(11): 2072-2076. (in Chinese with English Abstract)
34. Ustin, S. L. and Gamon, J. A. 2010. Remote Sensing of Plant Functional Types. New Phytol., 186: 795–816.
35. Vermote, E. F. and Vermeulen, A. 1999. Atmospheric Correction Algorithm: Spectral Reflectances (MOD09). NASA MODIS ATBD version 4.0.
36. Wang, J. H., Zhao, C. H., Guo, X.W. and Tian, Q. J. 2001. Study on the Water Status of the Wheat Leaves Diagnosed by the Spectral Reflectance. Sci. Agr. Sinica., 34(1): 104-107. (in Chinese with English Abstract)
37. Wang, L. L., Qu, J. J., Hao, X. J. and Zhu, Q. P. 2008a. Sensitivity Studies of the Moisture Effects on MODIS SWIR Reflectance and Vegetation Water Indices. Int. J. Remote Sens., 29(24):7065–7075
38. Wang, J. H., Zhao, C. J. and Huang, W. J. 2008b. Base and Application of Agricultural Qualitative Remote Sensing. China Science Press, Beijing. (in Chinese)
39. Wolfe, R. E., Nishihama, M., Fleig, A. J., Kuyper, J.A., Roy, D. P., Storey, J. C. and Patt, F. S. 2002. Achieving Sub-pixel Geolocation Accuracy in Support of MODIS Land Science. Remote Sens. Environ., 83(1–2): 31–49.
40. Wu C. Y., Niu Z, Tang Q. and Huang, W. J. 2009. Predicting Vegetation Water Content in Wheat Using Normalized Difference Water Indices Derived from Ground Measurements. J. Plant Res., 122: 317-329.
41. Xue, L. H., Luo, W. H., Cao, W. X. and Tian, Y. C. 2003. Research Progress on the Water and Nitrogen Detection Using Spectral Reflectance. J. Remote Sens., 7（1）：73-80. (in Chinese with English Abstract)
42. Yilmaz, M. T., Jr Hunt. E. R.and Jackson T. J. 2008. Remote Sensing of Vegetation Water Content from Equivalent Water Thickness Using Satellite Imagery. Remote Sens. Environ., 112: 2514-2522.
43. Zarco-Tejada, P. J., Rueda C. A., and Ustin, S. L. 2003. Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods. Remote Sens. Environ., 85(1): 109–24.
44. Zhang, J. H., Guo, W. J. and Yao, F. M. 2007. The Study on Vegetation Water Content Estimating Model Based on Remote Sensing Technique. J. Basic Sci. Eng., 15(1): 45-53. (in Chinese with English Abstract)
45. Zhang, R. H.1987. The New Mode of Crop Water Stress Estimation Based on Thermal Radiation Technique. Sci. China. Ser. B., 7: 776-784. (in Chinese)
46. Zhao, S. H. 2010. Farmland Drought Monitoring by Multi-source Remote Sensing. Postdoctoral Research Report of Peking University, China. (in Chinese with English Abstract).
Journal of Agricultural Science and Technology
Volume 18, Issue 2
March and April 2016
Pages 387-398