scholarly journals A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index

2008 ◽  
Vol 23 (7) ◽  
pp. 876-892 ◽  
Author(s):  
Benoît Duchemin ◽  
Philippe Maisongrande ◽  
Gilles Boulet ◽  
Iskander Benhadj
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Simple Algorithm ◽  
Green Leaf ◽  
Area Index ◽  
Semi Arid ◽  
Green Leaf Area

Prediction of Daily Green Leaf Area Index for Corn 1

1980 ◽  
Vol 72 (6) ◽  
pp. 999-1005 ◽  
Author(s):  
Robert F. Dale ◽  
Dirceu T. Coelho ◽  
Kevin P. Gallo
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Green Leaf ◽  
Area Index ◽  
Green Leaf Area

Using hyperspectral radiometry to predict the green leaf area index of turfgrass

2015 ◽  
Vol 36 (5) ◽  
pp. 1470-1483 ◽  
Author(s):  
Nan An ◽  
Anthony L. Goldsby ◽  
Kevin P. Price ◽  
Dale J. Bremer
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Green Leaf ◽  
Area Index ◽  
Green Leaf Area

scholarly journals Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat

2020 ◽  
Vol 12 (15) ◽  
pp. 2378
Author(s):  
Yang Song ◽  
Jinfei Wang ◽  
Jiali Shang ◽  
Chunhua Liao
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Low Cost ◽  
Simple Algorithm ◽  
Yield Potential ◽  
Yield Estimation ◽  
Area Index ◽  
Cloud Data

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.

Comparison of different vegetation indices for the remote assessment of green leaf area index of crops

2011 ◽  
Vol 115 (12) ◽  
pp. 3468-3478 ◽  
Author(s):  
Andrés Viña ◽  
Anatoly A. Gitelson ◽  
Anthony L. Nguy-Robertson ◽  
Yi Peng
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Green Leaf ◽  
Area Index ◽  
Remote Assessment ◽  
Green Leaf Area

scholarly journals An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index

2012 ◽  
Vol 161 ◽  
pp. 15-25 ◽  
Author(s):  
Noemi Guindin-Garcia ◽  
Anatoly A. Gitelson ◽  
Timothy J. Arkebauer ◽  
John Shanahan ◽  
Albert Weiss
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Green Leaf ◽  
Area Index ◽  
Green Leaf Area
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