Spatial downscaling of gridded soil moisture products using optical and thermal satellite data: the effects of using different vegetation indices
(2023) In Student thesis series INES NGEM01 20231Dept of Physical Geography and Ecosystem Science
- Abstract
- Soil moisture (SM) plays an important role in the exchange of heat and water between the surface and atmosphere, impacting water and energy cycles and the climate. Satellite remote sensing offers a global-scale estimation of SM; however, the coarse resolutions of satellite SM products, typically ranging from 25-50 km, are unsuitable for regional analysis. To overcome this limitation, various spatial downscaling methods have been developed to disaggregate SM products at coarse resolution to estimates at higher resolution. One commonly used approach is the optical and thermal-based method, which utilizes higher resolution ancillary data, such as land surface temperatures (LST) and vegetation indices (VI), within a triangular feature space.... (More)
- Soil moisture (SM) plays an important role in the exchange of heat and water between the surface and atmosphere, impacting water and energy cycles and the climate. Satellite remote sensing offers a global-scale estimation of SM; however, the coarse resolutions of satellite SM products, typically ranging from 25-50 km, are unsuitable for regional analysis. To overcome this limitation, various spatial downscaling methods have been developed to disaggregate SM products at coarse resolution to estimates at higher resolution. One commonly used approach is the optical and thermal-based method, which utilizes higher resolution ancillary data, such as land surface temperatures (LST) and vegetation indices (VI), within a triangular feature space. Previous studies have primarily relied on the use of NDVI (Normalised difference Vegetation Index) or EVI (Enhanced Vegetation Index) as VIs, neglecting the potential benefits of newly proposed VIs for spatial downscaling. Consequently, few studies have investigated the influence of different VIs on the downscaling of gridded soil moisture.
This study aims to investigate the influences of using different VIs on spatial downscaling of the coarse resolution SM product. Two study areas are focused on in this study (1) an area around the SMOSMANIA network in southern France with 18 SM measurement stations and (2) an area surrounding the REMEDHUS network in northern Spain with 17 measurement stations.
The daily ESA CCI SM product at 0.25° resolution was spatially downscaled using four different VIs including the NDVI, EVI, the kernel NDVI (kNDVI) and Plant Phenology Index (PPI) to produce a higher resolution SM product at 1 km and 16-day resolutions. All four VIs and the LST were obtained from MODIS products. The Vegetation Temperature Condition Index (VTCI) based downscaling approach was used for this study, in which wet and dry edges of the triangular feature space were determined by fitting a linear line to the maximum and minimum temperatures, respectively, for each VI interval.
Evaluation showed that using PPI showed better consistency between two study areas, having the good correlation and ubRMSD against the in-situ measurements, whilst the performance of using other VIs particularly EVI and kNDVI varied in the study area. Using NDVI generally yielded the poorest overall performance in terms of ubRMSD and correlation, but it outperformed kNDVI in areas with generally sparser vegetation within the SMOSMANIA network. Comparison of SM product at the original coarse resolution and spatially downscaled SM, the ESA CCI SM product generally outperformed the downscaled SM products, with only 12 out of 35 stations showing superior performance for the downscaled products in terms of correlation and 10 out of 35 stations in terms of ubRMSD. (Less) - Popular Abstract
- Soil moisture plays an important role in the exchange of heat and water between the surface and atmosphere, impacting water and energy cycles and the climate. Satellite remote sensing offers a global-scale estimation of soil moisture but is generally produced at low pixel resolutions, typically ranging from 25-50 km, which is unsuitable for regional analysis. To overcome this limitation, various downscaling methods have been developed to convert low resolution soil moisture products to higher resolutions. One commonly used approach uses higher resolution data, such as land surface temperatures and vegetation indices which are related to soil moisture to scale down the lower resolution data.
Previous studies have primarily relied on... (More) - Soil moisture plays an important role in the exchange of heat and water between the surface and atmosphere, impacting water and energy cycles and the climate. Satellite remote sensing offers a global-scale estimation of soil moisture but is generally produced at low pixel resolutions, typically ranging from 25-50 km, which is unsuitable for regional analysis. To overcome this limitation, various downscaling methods have been developed to convert low resolution soil moisture products to higher resolutions. One commonly used approach uses higher resolution data, such as land surface temperatures and vegetation indices which are related to soil moisture to scale down the lower resolution data.
Previous studies have primarily relied on vegetation indices such as the Normalised Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), neglecting the potential benefits of newly proposed vegetation indices which have been further developed to tackle the sensitivity of vegetation indices to various factors such as dense vegetation or atmospheric effects. Consequently, few studies have investigated the influences of the different vegetation indices on the downscaling of soil moisture products.
This study aims to investigate the influences of using different vegetation indices to estimate the soil moisture at high resolutions. The daily soil moisture product from the European Space Agency’s Climate Change Initiative (ESA CCI) at 28 km resolution was downscaled using four different vegetation indices, the NDVI, EVI, the kernel NDVI (kNDVI) and Plant Phenology Index (PPI) to produce a higher resolution soil moisture product at 1 km and 16-day resolutions. Two study areas were focused on in this study, one in southern France and another in northern Spain.
This study showed that the choice of vegetation index is important in the downscaling of soil moisture products and using alternative vegetation indices can lead to improved results. In comparison with ground measurements provided by soil moisture networks within each study area, PPI showed consistency between the two study areas, producing good results in both areas. The performance of other vegetation indices such as EVI and kNDVI varied between the study areas, where they seemed to produce good results in one area but underperform in the other. NDVI generally yielded the poorest overall performance but did outperform kNDVI in areas with generally sparser vegetation within the study area in southern France. In comparison to the original low resolution ESA CCI soil moisture product, the downscaled soil moisture products generally did not outperform the ESA CCI product, indicating that the accuracy of the ESA CCI soil moisture product is not conserved in the downscaling process. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9128503
- author
- Halldórsson Alexander, Tómas LU
- supervisor
-
- Zheng Duan LU
- Hongxiao Jin LU
- Haijun Luan LU
- organization
- alternative title
- The effects of using different vegetation indices in the downscaling of satellite soil moisture
- course
- NGEM01 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography and Ecosystem Analysis, Remote Sensing, Soil moisture, Downscaling, Vegetation index
- publication/series
- Student thesis series INES
- report number
- 610
- language
- English
- id
- 9128503
- date added to LUP
- 2023-06-21 12:15:20
- date last changed
- 2023-06-21 12:15:20
@misc{9128503,
abstract = {{Soil moisture (SM) plays an important role in the exchange of heat and water between the surface and atmosphere, impacting water and energy cycles and the climate. Satellite remote sensing offers a global-scale estimation of SM; however, the coarse resolutions of satellite SM products, typically ranging from 25-50 km, are unsuitable for regional analysis. To overcome this limitation, various spatial downscaling methods have been developed to disaggregate SM products at coarse resolution to estimates at higher resolution. One commonly used approach is the optical and thermal-based method, which utilizes higher resolution ancillary data, such as land surface temperatures (LST) and vegetation indices (VI), within a triangular feature space. Previous studies have primarily relied on the use of NDVI (Normalised difference Vegetation Index) or EVI (Enhanced Vegetation Index) as VIs, neglecting the potential benefits of newly proposed VIs for spatial downscaling. Consequently, few studies have investigated the influence of different VIs on the downscaling of gridded soil moisture.
This study aims to investigate the influences of using different VIs on spatial downscaling of the coarse resolution SM product. Two study areas are focused on in this study (1) an area around the SMOSMANIA network in southern France with 18 SM measurement stations and (2) an area surrounding the REMEDHUS network in northern Spain with 17 measurement stations.
The daily ESA CCI SM product at 0.25° resolution was spatially downscaled using four different VIs including the NDVI, EVI, the kernel NDVI (kNDVI) and Plant Phenology Index (PPI) to produce a higher resolution SM product at 1 km and 16-day resolutions. All four VIs and the LST were obtained from MODIS products. The Vegetation Temperature Condition Index (VTCI) based downscaling approach was used for this study, in which wet and dry edges of the triangular feature space were determined by fitting a linear line to the maximum and minimum temperatures, respectively, for each VI interval.
Evaluation showed that using PPI showed better consistency between two study areas, having the good correlation and ubRMSD against the in-situ measurements, whilst the performance of using other VIs particularly EVI and kNDVI varied in the study area. Using NDVI generally yielded the poorest overall performance in terms of ubRMSD and correlation, but it outperformed kNDVI in areas with generally sparser vegetation within the SMOSMANIA network. Comparison of SM product at the original coarse resolution and spatially downscaled SM, the ESA CCI SM product generally outperformed the downscaled SM products, with only 12 out of 35 stations showing superior performance for the downscaled products in terms of correlation and 10 out of 35 stations in terms of ubRMSD.}},
author = {{Halldórsson Alexander, Tómas}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Spatial downscaling of gridded soil moisture products using optical and thermal satellite data: the effects of using different vegetation indices}},
year = {{2023}},
}