Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content Meisam Amani A presentation for 26th Annual Newfoundland Electrical and Computer Engineering Conference St. John’s, NL, Canada, Nov. 15, 2017
Outlines
• Introduction
• Study areas • Data
• Method • Results and discussion • Conclusion
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Introduction • Dryness Simply refers to the lack of Soil Moisture (SM) or water
Direct indicator: SM Indirect indicators: Land Surface Temperature (LST), vegetation status, evapotranspiration, rainfall, and ground water
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Introduction • Dryness estimation methods
- Field work: - Labor intensive - Expensive - Time consuming - Practical for relatively small areas
- Remote Sensing (RS)
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Introduction • RS - Remote sensing sensors measure electromagnetic radiation reflected, emitted, or backscattered from the terrain. • RS sensors: active (e.g. SAR) and passive (e.g. optical). • Why RS methods: Cost effective, Repetitivity, Accessibility, Large coverage, Time conservation
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Introduction • Current dryness indices (three categories): 1) Based on the direct indicator of dryness (i.e. SM), such as: - Perpendicular Drought Index (PDI, Ghulam et al., 2007a) - Modified Perpendicular Drought Index (MPDI, Ghulam et al., 2007b) - Second Modified Perpendicular Drought Index (MPDI1, Li and Tan, 2013) 2) Based on one indirect indicator of dryness (e.g. LST, vegetation), such as: - Normalised Difference Vegetation Index (NDVI, Rouse et al., 1974) - Perpendicular Vegetation Index (PVI, Richardson and Wiegand, 1977) - Temperature Condition Index (TCI, Kogan, 1995) 3) Based on a combination of indirect indicators of dryness, such as: - Vegetation Temperature Condition Index (VTCI, Wang et al., 2001) - Temperature-Vegetation Dryness Index (TVDI, Sandholt et al., 2002)
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Study areas
Australia and Iran
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Data • Field data - Field volumetric SM data (0-5 cm) and soil temperature data (2.5 cm) collected in the Yanco study area - In total 233 samples
• Satellite data - Landsat 8 (11 spectral bands, 30 and 100 m spatial resolution) - MODIS ( 36 spectral bands, 250, 500, and 1000 m spatial resolution)
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Method - The most important variables for dryness estimation are LST, vegetation (both indirect indicators), and SM (direct indicator)
• Purpose: - Develop a new 3D space, in a way that each axis refers to one of these three variables and propose a new dryness index - The proposed index was named the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Method • Red-NIR spectral space
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Method • PVI-SM space
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Method • 3D space of the LST, PVI, and SM -
To define a 3D, we only needed to add the LST axis in the 2D space of SM-PVI.
-
F irst, to make the LST, PVI, and SM comparable with one another, and to transform the dryness values between 0 and 1, each axis were normalized between 0 and
3 3
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Results and discussion • TVMDI vs. field data
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Results and discussion • TVMDI vs. other satellite-based dryness indices
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Results and discussion • Dryness maps by applying the TVMDI to Landsat 8 imagery The TVMDI map of the Yanco area using Landsat 8 imagery on (a) J anuary 17, 2015, and (c) J une 10, 2015. The false color composite images of the Yanco region (NIR, red, and green are displayed in red, green, and blue, respectively) on (b) J anuary 17, 2015, and (d) J une 10, 2015 (The spatial resolution of the imagery is 30 meters).
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Results and discussion • Dryness maps in Australia by applying the TVMDI to MODIS imagery (a) Spatial distribution of the TVMDI over Australia on October 16, 2015 using MODIS data (spatial resolution= 250 meters, areas represented in orange indicate extremely dry regions, green is predominantly neutral, and blue is wet surfaces), (b) The LST and (c) the NDVI maps of Australia obtained from MODIS products acquired on October 16, 2015, (d) The root soil moisture map, on October 16, 2015, produced by Australian Bureau of Meteorology (http://www.bom.gov.au/water/la ndscape/). Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Results and discussion • Dryness maps in Iran by applying the TVMDI to MODIS imagery
Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Conclusion • Novelty - Developing a new 3D space for proposing a new dryness index - Combining direct and indirect indicators of SM for dryness estimation
• Features of the proposed index - Simple - Effective - Operational - Flexible - Higher accuracy compared to the current dryness indices - Reliable dryness maps - Applicable for drought monitoring
• Future study - Improve the accuracy of the TVMDI
- Use the TVMDI for drought monitoring and predicting Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
References -
Amani, M., Salehi, B., Mahdavi, S., Masjedi, A., & Dehnavi, S. (2017). “Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)”. Remote Sensing of Environment, 197, 1-14.
-
Amani, M., Mobasheri, M. R., & Mahdavi, S. (2017). "Contemporaneous estimation of the Leaf Area Index and soil moisture using the red-NIR spectral space". Remote Sensing Letters.
-
Mobasheri, M. R., & Amani, M. (2016). "Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels". Journal of Applied Remote Sensing, 10(2), 026011- 026011.
-
Amani, M., Parsian, S., MirMazloumi, S. M., & Aieneh, O. (2016). "Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data". International Journal of Applied Earth Observation and Geoinformation, 50, 176186.
-
Amani, M., & Mobasheri, M. R. (2015). "A parametric method for estimation of leaf area index using Landsat ETM+ data". GIScience & Remote Sensing, 52(4), 478-497. Dryness Estimation using Three Variables of Land Surface Temperature, Perpendicular Vegetation Index, and Soil Moisture Content
Thank You Question? Email:
[email protected]
