TY - JOUR
T1 - Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content
AU - Peng, Yi
AU - Gitelson, Anatoly A.
N1 - Funding Information:
This research was supported by NASA NACP grant No. NNX08AI75G and partially by the U.S. Department of Energy : (a) EPSCoR program, Grant No. DE-FG-02-00ER45827 and (b) Office of Science (BER), Grant No. DE-FG03-00ER62996 . We gratefully acknowledge the support and the use of facilities and equipment provided by the Center for Advanced Land Management Information Technologies (CALMIT) and Carbon Sequestration Program, University of Nebraska-Lincoln. We are sincerely grateful to Donald. C. Rundquist, Shashi B. Verma and Andy E. Suyker for their help and support, and Anthony Lawrence Nguy-Robertson for fruitful discussion and suggestions.
PY - 2012/2/15
Y1 - 2012/2/15
N2 - The synoptic quantification of crop gross primary productivity (GPP) is essential for studying carbon budgets in croplands and monitoring crop status. In this study, we applied a recently developed model, which relates crop GPP to a product of total crop chlorophyll content and incoming photosynthetically active radiation, for the remote estimation of GPP in two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. The objective of this study was to evaluate performances of twelve vegetation indices used for detecting different vegetation biophysical characteristics, in estimating GPP of rainfed and irrigated crops over a period from 2001 through 2008. Indices tested in the model exhibited strong and significant relationships with widely variable GPP in each crop (GPP ranged from 0 to 19gC/m 2/d for soybean and 0 to 35gC/m 2/d for maize), however, they were species-specific. Only three indices, which use MERIS red edge and NIR spectral bands (i.e. red edge chlorophyll index, MERIS Terrestrial Chlorophyll Index and red edge NDVI), were found to be able to estimate GPP accurately in both crops combined, with root mean square errors (RMSE) below 3.2gC/m 2/d. It was also shown that two indices, red edge chlorophyll index and red edge NDVI with a red edge band around 720nm, were non-species-specific and yielded a very accurate estimation of GPP in maize and soybean combined, with RMSEs below 2.9gC/m 2/d and coefficients of variation below 21%.
AB - The synoptic quantification of crop gross primary productivity (GPP) is essential for studying carbon budgets in croplands and monitoring crop status. In this study, we applied a recently developed model, which relates crop GPP to a product of total crop chlorophyll content and incoming photosynthetically active radiation, for the remote estimation of GPP in two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. The objective of this study was to evaluate performances of twelve vegetation indices used for detecting different vegetation biophysical characteristics, in estimating GPP of rainfed and irrigated crops over a period from 2001 through 2008. Indices tested in the model exhibited strong and significant relationships with widely variable GPP in each crop (GPP ranged from 0 to 19gC/m 2/d for soybean and 0 to 35gC/m 2/d for maize), however, they were species-specific. Only three indices, which use MERIS red edge and NIR spectral bands (i.e. red edge chlorophyll index, MERIS Terrestrial Chlorophyll Index and red edge NDVI), were found to be able to estimate GPP accurately in both crops combined, with root mean square errors (RMSE) below 3.2gC/m 2/d. It was also shown that two indices, red edge chlorophyll index and red edge NDVI with a red edge band around 720nm, were non-species-specific and yielded a very accurate estimation of GPP in maize and soybean combined, with RMSEs below 2.9gC/m 2/d and coefficients of variation below 21%.
KW - Gross primary production
KW - Total chlorophyll content
KW - Unified algorithm
KW - Vegetation index
UR - http://www.scopus.com/inward/record.url?scp=84855446205&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84855446205&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2011.10.021
DO - 10.1016/j.rse.2011.10.021
M3 - Article
AN - SCOPUS:84855446205
SN - 0034-4257
VL - 117
SP - 440
EP - 448
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -