Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

J. R. Marques da Silva, C. V. Damasio, A. M. O. Sousa, L. Bugalho, L. Pessanha, P. Quaresma

Research output: Contribution to journalArticle

Abstract

Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 x 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: "thermal integral over air temperature (accumulated degree-days)". The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations. (C) 2015 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)40-50
Number of pages11
JournalInternational journal of applied earth observation and geoinformation
Volume38
DOIs
Publication statusPublished - Jun 2015

Keywords

  • Land surface temperature
  • LST
  • Satellite application facility
  • SAF
  • EUMETSAT
  • MSG
  • Pest management
  • Pest risk maps
  • URBAN HEAT ISLANDS
  • POTATO LATE BLIGHT
  • MODIS LST DATA
  • AIR TEMPERATURES
  • DEPENDENT DEVELOPMENT
  • POPULATION-DYNAMICS
  • TEMPORAL VARIATIONS
  • CLIMATE-CHANGE
  • RICE DISEASES
  • PHENOLOGY

Cite this

@article{e1d2474a95b74efda1bbb98191b6295d,
title = "Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature",
abstract = "Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 x 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: {"}thermal integral over air temperature (accumulated degree-days){"}. The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations. (C) 2015 Elsevier B.V. All rights reserved.",
keywords = "Land surface temperature, LST, Satellite application facility, SAF, EUMETSAT, MSG, Pest management, Pest risk maps, URBAN HEAT ISLANDS, POTATO LATE BLIGHT, MODIS LST DATA, AIR TEMPERATURES, DEPENDENT DEVELOPMENT, POPULATION-DYNAMICS, TEMPORAL VARIATIONS, CLIMATE-CHANGE, RICE DISEASES, PHENOLOGY",
author = "{Marques da Silva}, {J. R.} and Damasio, {C. V.} and Sousa, {A. M. O.} and L. Bugalho and L. Pessanha and P. Quaresma",
note = "Sem PDF. FEDER Funds, through the Operational Programme for Competitiveness Factors - COMPETE; National Funds through FCT - Foundation for Science and Technology under the Strategic Project (PEst-C/AGR/UI0115/2011); Initiative of the Agriculture, Sea Environment and Land Planning Ministry; FEADER, in the scope of PRODER; EUMETSAT/LSASAF through IPMA (Portuguese Sea and Atmosphere Institute)",
year = "2015",
month = "6",
doi = "10.1016/j.jag.2014.12.016",
language = "English",
volume = "38",
pages = "40--50",
journal = "International journal of applied earth observation and geoinformation",
issn = "0303-2434",
publisher = "Elsevier Science B.V., Inc",

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Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature. / Marques da Silva, J. R.; Damasio, C. V.; Sousa, A. M. O.; Bugalho, L.; Pessanha, L.; Quaresma, P.

In: International journal of applied earth observation and geoinformation, Vol. 38, 06.2015, p. 40-50.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

AU - Marques da Silva, J. R.

AU - Damasio, C. V.

AU - Sousa, A. M. O.

AU - Bugalho, L.

AU - Pessanha, L.

AU - Quaresma, P.

N1 - Sem PDF. FEDER Funds, through the Operational Programme for Competitiveness Factors - COMPETE; National Funds through FCT - Foundation for Science and Technology under the Strategic Project (PEst-C/AGR/UI0115/2011); Initiative of the Agriculture, Sea Environment and Land Planning Ministry; FEADER, in the scope of PRODER; EUMETSAT/LSASAF through IPMA (Portuguese Sea and Atmosphere Institute)

PY - 2015/6

Y1 - 2015/6

N2 - Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 x 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: "thermal integral over air temperature (accumulated degree-days)". The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations. (C) 2015 Elsevier B.V. All rights reserved.

AB - Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 x 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: "thermal integral over air temperature (accumulated degree-days)". The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations. (C) 2015 Elsevier B.V. All rights reserved.

KW - Land surface temperature

KW - LST

KW - Satellite application facility

KW - SAF

KW - EUMETSAT

KW - MSG

KW - Pest management

KW - Pest risk maps

KW - URBAN HEAT ISLANDS

KW - POTATO LATE BLIGHT

KW - MODIS LST DATA

KW - AIR TEMPERATURES

KW - DEPENDENT DEVELOPMENT

KW - POPULATION-DYNAMICS

KW - TEMPORAL VARIATIONS

KW - CLIMATE-CHANGE

KW - RICE DISEASES

KW - PHENOLOGY

U2 - 10.1016/j.jag.2014.12.016

DO - 10.1016/j.jag.2014.12.016

M3 - Article

VL - 38

SP - 40

EP - 50

JO - International journal of applied earth observation and geoinformation

JF - International journal of applied earth observation and geoinformation

SN - 0303-2434

ER -