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Estimating maize grain yield from cr...

Guindin-Garcia, Noemi.

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Estimating maize grain yield from crop biophysical parameters using remote sensing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Estimating maize grain yield from crop biophysical parameters using remote sensing./
Author:	Guindin-Garcia, Noemi.
Description:	120 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: .
Contained By:	Dissertation Abstracts International72-02B.
Subject:	Agriculture, Agronomy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3433051
ISBN:	9781124382913

Estimating maize grain yield from crop biophysical parameters using remote sensing.
Guindin-Garcia, Noemi.

Estimating maize grain yield from crop biophysical parameters using remote sensing. - 120 p.

Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: .

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2011.

The overall objective of this investigation was to develop a robust technique to predict maize (Zea mays L.) grain yield that could be applied at a regional level using remote sensing with or without a simple crop growth simulation model. This study evaluated capabilities and limitations of the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index 250-m and MODIS surface reflectance 500-m products to track and retrieve information over maize fields. Results demonstrated the feasibility of using MODIS data to estimate maize green leaf area index (LAIg). Estimates of maize LAIg obtained from Wide Dynamic Range Vegetation Index using data retrieved from MODIS 250-m products (e.g. MOD13Q1) can be incorporated in crop simulation models to improve LAIg simulations by the Muchow-Sinclair-Bennet (MSB) model reducing the RMSE of LAIg simulations for all years of study under irrigation. However, more accurate estimates of LAIg did not necessarily imply better final yield (FY) predictions in the MSB maize model. The approach of incorporating better LAIg estimates into crop simulation models may not offer a panacea for problem solving; this approach is limited in its ability to simulate other factors influencing crop yields. On the other hand, the approach of relating key crop biophysical parameters at the optimum stage with maize grain final yields is a robust technique to early FY estimation over large areas. Results suggest that estimates of LAI g obtained during the mid-grain filling period can used to detect variability of maize grain yield and this technique offers a rapid and accurate (RMSE < 900 kg ha-1) method to detect FY at county level using MODIS 250-m products.

ISBN: 9781124382913Subjects--Topical Terms:

1018679
Agriculture, Agronomy.

Estimating maize grain yield from crop biophysical parameters using remote sensing.
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020 $a 9781124382913
035 $a (UMI)AAI3433051
035 $a AAI3433051
040 $a UMI $c UMI
100 1 $a Guindin-Garcia, Noemi. $3 1676551
245 1 0 $a Estimating maize grain yield from crop biophysical parameters using remote sensing.
300 $a 120 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-02, Section: B, page: .
500 $a Adviser: Timothy J. Arkebauer.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2011.
520 $a The overall objective of this investigation was to develop a robust technique to predict maize (Zea mays L.) grain yield that could be applied at a regional level using remote sensing with or without a simple crop growth simulation model. This study evaluated capabilities and limitations of the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index 250-m and MODIS surface reflectance 500-m products to track and retrieve information over maize fields. Results demonstrated the feasibility of using MODIS data to estimate maize green leaf area index (LAIg). Estimates of maize LAIg obtained from Wide Dynamic Range Vegetation Index using data retrieved from MODIS 250-m products (e.g. MOD13Q1) can be incorporated in crop simulation models to improve LAIg simulations by the Muchow-Sinclair-Bennet (MSB) model reducing the RMSE of LAIg simulations for all years of study under irrigation. However, more accurate estimates of LAIg did not necessarily imply better final yield (FY) predictions in the MSB maize model. The approach of incorporating better LAIg estimates into crop simulation models may not offer a panacea for problem solving; this approach is limited in its ability to simulate other factors influencing crop yields. On the other hand, the approach of relating key crop biophysical parameters at the optimum stage with maize grain final yields is a robust technique to early FY estimation over large areas. Results suggest that estimates of LAI g obtained during the mid-grain filling period can used to detect variability of maize grain yield and this technique offers a rapid and accurate (RMSE < 900 kg ha-1) method to detect FY at county level using MODIS 250-m products.
590 $a School code: 0138.
650 4 $a Agriculture, Agronomy. $3 1018679
650 4 $a Remote Sensing. $3 1018559
690 $a 0285
690 $a 0799
710 2 $a The University of Nebraska - Lincoln. $b Agronomy. $3 1029981
773 0 $t Dissertation Abstracts International $g 72-02B.
790 1 0 $a Arkebauer, Timothy J., $e advisor
790 1 0 $a Weiss, Albert $e committee member
790 1 0 $a Gitelson, Anatoly $e committee member
790 1 0 $a Shanahan, John $e committee member
790 $a 0138
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3433051