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Filters: Sites: US-Ne1

Publications Found: 28

Monitoring Maize (Zea Mays L.) Phenology With Remote Sensing
Viña, A., Gitelson, A. A., Rundquist, D. C., Keydan, G., Leavitt, B., Schepers, J.

Monitoring crop phenology is required for understanding intra- and interannual variations of agroecosystems, as well as for improving yield prediction models. The objective of this paper is to remotely evaluate the phenological development of maize (Zea mays L.) in terms of both biomass accumulation and reproductive organ …

Journal: Agronomy Journal, Volume 96 (4): 1139-1147 (2004), ISBN . DOI: 10.2134/agronj2004.1139 Sites: US-Ne1, US-Ne2, US-Ne3

Satellite Monitoring Of Vegetation Dynamics: Sensitivity Enhancement By The Wide Dynamic Range Vegetation Index
Viña, A., Genebry, G.M., Gitelson, A. A.

Synoptic monitoring of vegetation dynamics relies on satellite observations of the distinctive spectral contrast between red and near infrared reflectance that photosynthetically active green vegetation exhibits. It has long been recognized that the Normalized Difference Vegetation Index (NDVI) suffers a rapid decrease of sensitivity …

Journal: Geophysical Research Letters, Volume 31 (4): 1-4 (2004), ISBN . DOI: 10.1029/2003gl019034 Sites: US-Ne1, US-Ne2, US-Ne3

Wide Dynamic Range Vegetation Index For Remote Quantification Of Biophysical Characteristics Of Vegetation
Gitelson, A. A.

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring, analyzing, and mapping temporal and spatial distributions of physiological and biophysical characteristics of vegetation. It is well documented that the NDVI approaches saturation asymptotically under conditions of moderate-to-high aboveground biomass. …

Journal: Journal Of Plant Physiology, Volume 161 (2): 165-173 (2004), ISBN . DOI: 10.1078/0176-1617-01176 Sites: US-Ne1, US-Ne2, US-Ne3

Geostatistical Integration Of Yield Monitor Data And Remote Sensing Improves Yield Maps
Dobermann, A., Ping, J. L.

Grain yield maps must accurately display general yield patterns as well as details of local yield variation. Different geostatistical procedures for creating interpolated yield maps by integrating yield data with remotely sensed vegetation indices (VI) were evaluated. Yield monitor data and a multispectral satellite image at 4-m …

Journal: Agronomy Journal, Volume 96 (1): 285-297 (2004), ISBN . DOI: 10.2134/agronj2004.0285 Sites: US-Ne1, US-Ne2

Classification Of Crop Yield Variability In Irrigated Production Fields
Dobermann, A., Ping, J. L., Adamchuk, V. I., Simbahan, G. C., Ferguson, R. B.

Crop yield maps reflect stable yield patterns and annual random yield variation. Procedures for classifying a sequence of yield maps to delineate yield zones were evaluated in two irrigated maize (Zea mays L.) fields. Yield classes were created using empirically defined yield categories or through hierarchical or nonhierarchical …

Journal: Agronomy Journal, Volume 95 (5): 1105-1120 (2003), ISBN . DOI: 10.2134/agronj2003.1105 Sites: US-Ne1, US-Ne2, US-Ne3

Novel Technique For Remote Estimation Of CO2 Flux In Maize
Gitelson, A. A., Verma, S. B, Rundquist, D. C., Keydan, G., Leavitt, B., Arkebauer, T. J., Burba, G. G., Suyker, A. E.

[1] There is considerable interest in assessing the magnitude of carbon sources and sinks for agricultural lands, grasslands, and forests. In this paper, we propose a novel …

Journal: Geophysical Research Letters, Volume 30 (9): 1486-n/a (2003), ISBN . DOI: 10.1029/2002GL016543 Sites: US-Ne1, US-Ne2

Creating Spatially Contiguous Yield Classes For Site-Specific Management
Ping, J. L., Dobermann, A.

Annual yield maps are spatially fragmented because of random variation caused by crop management as well as measurement errors. Two approaches for creating maps of spatially contiguous yield classes were evaluated at two irrigated sites. In the first approach, prior-classification interpolation (PCI), grid size was increased from …

Journal: Agronomy Journal, Volume 95 (5): 1121-1131 (2003), ISBN . DOI: 10.2134/agronj2003.1121 Sites: US-Ne1, US-Ne2, US-Ne3

Remote Estimation Of Leaf Area Index And Green Leaf Biomass In Maize Canopies
Gitelson, A. A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., Leavitt, B.

Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but …

Journal: Geophysical Research Letters, Volume 30 (5): n/a-n/a (2003), ISBN . DOI: 10.1029/2002GL016450 Sites: US-Ne1

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