Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index

Georgios Ovakoglou, Thomas K. Alexandridis*, Jan G.P.W. Clevers, Ines Cherif, Dimitrios A. Kasampalis, Ioannis Navrozidis, Charalampos Iordanidis, Dimitrios Moshou, Giovanni Laneve, Juan Suarez Beltran

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

2 Citations (Scopus)

Abstract

The Leaf Area Index (LAI) is an important indicator of vegetation development which can be used as an input parameter in hydrological and biochemical models (e.g. crop models for yield prediction and forecast) and is, thus, relevant information to monitor food production and to feed an early warning system for famine crisis. Satellite LAI data is available on a regular basis (high temporal resolution) with maps at regional or global scales (low spatial resolution). This study aimed at enhancing the spatial resolution of the MODIS LAI product to bring it to the Landsat resolution. The proposed method was applied in four sites with different climate and vegetation conditions. Regression analysis between MODIS EVI (Enhanced Vegetation Index) and LAI data was applied across time and the estimated regression equations were input in a downscaling model using Landsat EVI images and land cover maps. Comparison between the downscaled LAI values and LAI field measurements showed high correlation, with correlation coefficient values ranging from moderate (0.5 - 0.7 in two cases) to high (0.7 - 0.96 in five cases). The results show that it is possible to use this methodology to reliably estimate LAI at a 30m spatial resolution across various climates and ecosystems, thus supporting a food security early warning system.

Original languageEnglish
Title of host publicationIGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8232-8235
VolumeJuly
ISBN (Electronic)9781538671504
DOIs
Publication statusPublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18

Fingerprint

vegetation index
Regression analysis
leaf area index
MODIS
Landsat
regression analysis
Alarm systems
spatial resolution
early warning system
Ecosystems
Crops
Satellites
famine
vegetation
downscaling
climate
food production
food security
land cover
crop

Keywords

  • Downscaling model
  • EVI
  • High resolution LAI
  • Regression analysis

Cite this

Ovakoglou, G., Alexandridis, T. K., Clevers, J. G. P. W., Cherif, I., Kasampalis, D. A., Navrozidis, I., ... Beltran, J. S. (2018). Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index. In IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium (Vol. July, pp. 8232-8235). [8519387] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8519387
Ovakoglou, Georgios ; Alexandridis, Thomas K. ; Clevers, Jan G.P.W. ; Cherif, Ines ; Kasampalis, Dimitrios A. ; Navrozidis, Ioannis ; Iordanidis, Charalampos ; Moshou, Dimitrios ; Laneve, Giovanni ; Beltran, Juan Suarez. / Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index. IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium. Vol. July Institute of Electrical and Electronics Engineers Inc., 2018. pp. 8232-8235
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title = "Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index",
abstract = "The Leaf Area Index (LAI) is an important indicator of vegetation development which can be used as an input parameter in hydrological and biochemical models (e.g. crop models for yield prediction and forecast) and is, thus, relevant information to monitor food production and to feed an early warning system for famine crisis. Satellite LAI data is available on a regular basis (high temporal resolution) with maps at regional or global scales (low spatial resolution). This study aimed at enhancing the spatial resolution of the MODIS LAI product to bring it to the Landsat resolution. The proposed method was applied in four sites with different climate and vegetation conditions. Regression analysis between MODIS EVI (Enhanced Vegetation Index) and LAI data was applied across time and the estimated regression equations were input in a downscaling model using Landsat EVI images and land cover maps. Comparison between the downscaled LAI values and LAI field measurements showed high correlation, with correlation coefficient values ranging from moderate (0.5 - 0.7 in two cases) to high (0.7 - 0.96 in five cases). The results show that it is possible to use this methodology to reliably estimate LAI at a 30m spatial resolution across various climates and ecosystems, thus supporting a food security early warning system.",
keywords = "Downscaling model, EVI, High resolution LAI, Regression analysis",
author = "Georgios Ovakoglou and Alexandridis, {Thomas K.} and Clevers, {Jan G.P.W.} and Ines Cherif and Kasampalis, {Dimitrios A.} and Ioannis Navrozidis and Charalampos Iordanidis and Dimitrios Moshou and Giovanni Laneve and Beltran, {Juan Suarez}",
year = "2018",
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doi = "10.1109/IGARSS.2018.8519387",
language = "English",
volume = "July",
pages = "8232--8235",
booktitle = "IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium",
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Ovakoglou, G, Alexandridis, TK, Clevers, JGPW, Cherif, I, Kasampalis, DA, Navrozidis, I, Iordanidis, C, Moshou, D, Laneve, G & Beltran, JS 2018, Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index. in IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium. vol. July, 8519387, Institute of Electrical and Electronics Engineers Inc., pp. 8232-8235, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8519387

Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index. / Ovakoglou, Georgios; Alexandridis, Thomas K.; Clevers, Jan G.P.W.; Cherif, Ines; Kasampalis, Dimitrios A.; Navrozidis, Ioannis; Iordanidis, Charalampos; Moshou, Dimitrios; Laneve, Giovanni; Beltran, Juan Suarez.

IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium. Vol. July Institute of Electrical and Electronics Engineers Inc., 2018. p. 8232-8235 8519387.

Research output: Chapter in Book/Report/Conference proceedingConference paper

TY - GEN

T1 - Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index

AU - Ovakoglou, Georgios

AU - Alexandridis, Thomas K.

AU - Clevers, Jan G.P.W.

AU - Cherif, Ines

AU - Kasampalis, Dimitrios A.

AU - Navrozidis, Ioannis

AU - Iordanidis, Charalampos

AU - Moshou, Dimitrios

AU - Laneve, Giovanni

AU - Beltran, Juan Suarez

PY - 2018/10/31

Y1 - 2018/10/31

N2 - The Leaf Area Index (LAI) is an important indicator of vegetation development which can be used as an input parameter in hydrological and biochemical models (e.g. crop models for yield prediction and forecast) and is, thus, relevant information to monitor food production and to feed an early warning system for famine crisis. Satellite LAI data is available on a regular basis (high temporal resolution) with maps at regional or global scales (low spatial resolution). This study aimed at enhancing the spatial resolution of the MODIS LAI product to bring it to the Landsat resolution. The proposed method was applied in four sites with different climate and vegetation conditions. Regression analysis between MODIS EVI (Enhanced Vegetation Index) and LAI data was applied across time and the estimated regression equations were input in a downscaling model using Landsat EVI images and land cover maps. Comparison between the downscaled LAI values and LAI field measurements showed high correlation, with correlation coefficient values ranging from moderate (0.5 - 0.7 in two cases) to high (0.7 - 0.96 in five cases). The results show that it is possible to use this methodology to reliably estimate LAI at a 30m spatial resolution across various climates and ecosystems, thus supporting a food security early warning system.

AB - The Leaf Area Index (LAI) is an important indicator of vegetation development which can be used as an input parameter in hydrological and biochemical models (e.g. crop models for yield prediction and forecast) and is, thus, relevant information to monitor food production and to feed an early warning system for famine crisis. Satellite LAI data is available on a regular basis (high temporal resolution) with maps at regional or global scales (low spatial resolution). This study aimed at enhancing the spatial resolution of the MODIS LAI product to bring it to the Landsat resolution. The proposed method was applied in four sites with different climate and vegetation conditions. Regression analysis between MODIS EVI (Enhanced Vegetation Index) and LAI data was applied across time and the estimated regression equations were input in a downscaling model using Landsat EVI images and land cover maps. Comparison between the downscaled LAI values and LAI field measurements showed high correlation, with correlation coefficient values ranging from moderate (0.5 - 0.7 in two cases) to high (0.7 - 0.96 in five cases). The results show that it is possible to use this methodology to reliably estimate LAI at a 30m spatial resolution across various climates and ecosystems, thus supporting a food security early warning system.

KW - Downscaling model

KW - EVI

KW - High resolution LAI

KW - Regression analysis

U2 - 10.1109/IGARSS.2018.8519387

DO - 10.1109/IGARSS.2018.8519387

M3 - Conference paper

VL - July

SP - 8232

EP - 8235

BT - IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Ovakoglou G, Alexandridis TK, Clevers JGPW, Cherif I, Kasampalis DA, Navrozidis I et al. Spatial enhancement of MODIS leaf area index using regression analysis with Landsat vegetation index. In IGARSS 2018 Proceedings - 2018 IEEE International Geoscience and Remote Sensing Symposium. Vol. July. Institute of Electrical and Electronics Engineers Inc. 2018. p. 8232-8235. 8519387 https://doi.org/10.1109/IGARSS.2018.8519387