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

1 Scopus citations

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
EditorsJohn V. Stafford
PublisherWageningen Academic Publishers
Pages183-189
Number of pages7
ISBN (Electronic)9789086862672
DOIs
StatePublished - 2015
Event10th European Conference on Precision Agriculture, ECPA 2015 - Tel-Aviv, Israel
Duration: Jul 12 2015Jul 16 2015

Publication series

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

Other

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

Keywords

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

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Remote estimation of gross primary productivity in maize and soybean'. Together they form a unique fingerprint.

Cite this