Remote estimation of gross primary productivity in maize and soybean

A. A. Gitelson, Y. Peng, D. C. Rundquist, A. Suyeker, S. B. Verma

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

Abstract

In this study, a model for estimating crop gross primary production (GPP) using the product of a chlorophyll-related vegetation index (VI) and incident photosynthetically active radiation (PARin) was developed and tested. This model was tested using radiometric data taken 6 m above the top of the canopy and Landsat and MODIS satellites data for GPP estimation in both maize and soybean, crop types that differ in leaf structure and canopy architecture, under different crop management and climatic conditions. The model was capable of estimating GPP accurately in three Nebraska, USA sites during growing seasons 2001 through 2008. The developed model was successfully applied to measure GPP of vegetation (crops, grasslands and deciduous forests) where total chlorophyll content is closely tied to the seasonal dynamic of GPP. The techniques described open new possibilities for accurate estimation of crop GPP at different scales, from close-range (just above the canopy in the field) to satellite altitudes.

Original languageEnglish (US)
Title of host publicationPrecision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015
PublisherWageningen Academic Publishers
Pages183-189
Number of pages7
ISBN (Print)9789086862672
StatePublished - 2015
Event10th European Conference on Precision Agriculture, ECPA 2015 - Tel-Aviv, Israel
Duration: Jul 12 2015Jul 16 2015

Other

Other10th European Conference on Precision Agriculture, ECPA 2015
CountryIsrael
CityTel-Aviv
Period7/12/157/16/15

Fingerprint

primary productivity
Productivity
Crops
soybeans
corn
Chlorophyll
canopy
crops
Satellites
chlorophyll
moderate resolution imaging spectroradiometer
crop models
Landsat
crop management
photosynthetically active radiation
deciduous forests
remote sensing
grasslands
growing season
Radiation

Keywords

  • Crops remote sensing
  • Gross primary production
  • Landsat
  • MODIS

ASJC Scopus subject areas

  • Computer Science Applications
  • Agronomy and Crop Science

Cite this

Gitelson, A. A., Peng, Y., Rundquist, D. C., Suyeker, A., & Verma, S. B. (2015). Remote estimation of gross primary productivity in maize and soybean. In Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015 (pp. 183-189). Wageningen Academic Publishers.

Remote estimation of gross primary productivity in maize and soybean. / Gitelson, A. A.; Peng, Y.; Rundquist, D. C.; Suyeker, A.; Verma, S. B.

Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015. Wageningen Academic Publishers, 2015. p. 183-189.

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

Gitelson, AA, Peng, Y, Rundquist, DC, Suyeker, A & Verma, SB 2015, Remote estimation of gross primary productivity in maize and soybean. in Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015. Wageningen Academic Publishers, pp. 183-189, 10th European Conference on Precision Agriculture, ECPA 2015, Tel-Aviv, Israel, 7/12/15.
Gitelson AA, Peng Y, Rundquist DC, Suyeker A, Verma SB. Remote estimation of gross primary productivity in maize and soybean. In Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015. Wageningen Academic Publishers. 2015. p. 183-189
Gitelson, A. A. ; Peng, Y. ; Rundquist, D. C. ; Suyeker, A. ; Verma, S. B. / Remote estimation of gross primary productivity in maize and soybean. Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015. Wageningen Academic Publishers, 2015. pp. 183-189
@inproceedings{763db5edc931446ca29e38a5149caa4e,
title = "Remote estimation of gross primary productivity in maize and soybean",
abstract = "In this study, a model for estimating crop gross primary production (GPP) using the product of a chlorophyll-related vegetation index (VI) and incident photosynthetically active radiation (PARin) was developed and tested. This model was tested using radiometric data taken 6 m above the top of the canopy and Landsat and MODIS satellites data for GPP estimation in both maize and soybean, crop types that differ in leaf structure and canopy architecture, under different crop management and climatic conditions. The model was capable of estimating GPP accurately in three Nebraska, USA sites during growing seasons 2001 through 2008. The developed model was successfully applied to measure GPP of vegetation (crops, grasslands and deciduous forests) where total chlorophyll content is closely tied to the seasonal dynamic of GPP. The techniques described open new possibilities for accurate estimation of crop GPP at different scales, from close-range (just above the canopy in the field) to satellite altitudes.",
keywords = "Crops remote sensing, Gross primary production, Landsat, MODIS",
author = "Gitelson, {A. A.} and Y. Peng and Rundquist, {D. C.} and A. Suyeker and Verma, {S. B.}",
year = "2015",
language = "English (US)",
isbn = "9789086862672",
pages = "183--189",
booktitle = "Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015",
publisher = "Wageningen Academic Publishers",

}

TY - GEN

T1 - Remote estimation of gross primary productivity in maize and soybean

AU - Gitelson, A. A.

AU - Peng, Y.

AU - Rundquist, D. C.

AU - Suyeker, A.

AU - Verma, S. B.

PY - 2015

Y1 - 2015

N2 - In this study, a model for estimating crop gross primary production (GPP) using the product of a chlorophyll-related vegetation index (VI) and incident photosynthetically active radiation (PARin) was developed and tested. This model was tested using radiometric data taken 6 m above the top of the canopy and Landsat and MODIS satellites data for GPP estimation in both maize and soybean, crop types that differ in leaf structure and canopy architecture, under different crop management and climatic conditions. The model was capable of estimating GPP accurately in three Nebraska, USA sites during growing seasons 2001 through 2008. The developed model was successfully applied to measure GPP of vegetation (crops, grasslands and deciduous forests) where total chlorophyll content is closely tied to the seasonal dynamic of GPP. The techniques described open new possibilities for accurate estimation of crop GPP at different scales, from close-range (just above the canopy in the field) to satellite altitudes.

AB - In this study, a model for estimating crop gross primary production (GPP) using the product of a chlorophyll-related vegetation index (VI) and incident photosynthetically active radiation (PARin) was developed and tested. This model was tested using radiometric data taken 6 m above the top of the canopy and Landsat and MODIS satellites data for GPP estimation in both maize and soybean, crop types that differ in leaf structure and canopy architecture, under different crop management and climatic conditions. The model was capable of estimating GPP accurately in three Nebraska, USA sites during growing seasons 2001 through 2008. The developed model was successfully applied to measure GPP of vegetation (crops, grasslands and deciduous forests) where total chlorophyll content is closely tied to the seasonal dynamic of GPP. The techniques described open new possibilities for accurate estimation of crop GPP at different scales, from close-range (just above the canopy in the field) to satellite altitudes.

KW - Crops remote sensing

KW - Gross primary production

KW - Landsat

KW - MODIS

UR - http://www.scopus.com/inward/record.url?scp=84947224502&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947224502&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9789086862672

SP - 183

EP - 189

BT - Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015

PB - Wageningen Academic Publishers

ER -