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Evaluating Multispectral and Hyperspectral Satellite Remote Sensing Data for Estimating Winter Wheat Growth Parameters at Regional Scale in the North China Plain Anwendung Multispektralerund Hyperspektraler Fernerkundung zur Ableitung von Bestandsparametern des Winterweizens in der Nordchinesischen Tiefebene

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Timely monitoring of crop growth status at large scale is crucial for improving regional crop management decisions. The main objective of the recent study is a model development to predict and estimate crop parameters, here biomass, plant N concentration and plant height, based on multi- and hyperspectral satellite data. In this contribution, the focus is on relating orbital multispectral (EO-1 ALI) and hyperspectral (EO-1 Hyperion) measurements to winter wheat parameters for regional level applications. The study was conducted in Huimin County, Shandong Province of China in the growing season of 2005/2006 involving three big winter wheat fields managed by different farmers. Winter wheat growth parameters including aboveground biomass, plant N concentration and plant height were collected at different growth stages. Three different predicting models were investigated: traditional vegetation indices calculated from broad and narrow bands, and Normalized Ratio Indices (NRI) calculated from all possible two-band combinations of Hyperion between 400 and 2,500 nm. The results indicated that TVI performed best among the tested vegetation indices using either broad (R2=0.69, 0.32 and 0.64 for biomass, N concentration and plant height, respectively) or narrow (R2=0.71, 0.33 and 0.65 for biomass, N concentration and plant height, respectively) bands. The best performing Normalized Ratio Index (NRI) selected through band combination analysis were significantly better than TVI, achieving R2 of 0.83, 0.81 and 0.79 for biomass, plant N concentration and plant height, respectively. The different NRI models use wavebands from the near infrared (NIR) (centered at 874, 732, and 763 nm) and short wave infrared (SWIR) (centered at 1,225 and 1,305 nm) spectrum with varying bandwidth between 10 and 190 nm. The result of this study suggest that vegetation indices derived from NIR- and SWIR-Hyperion spectrum are better predictors of plant aboveground biomass, nitrogen concentration and plant height than indices derived from only visible spectrum. More studies are needed to further evaluate the results using data from more diverse conditions.

Methoden und Techniken der Fernerkundung fungieren als ein wichtiges Hilfsmittel im regionalen Umweltmanagement. Ziel der vorliegenden Studie liegt dabei auf der Modelentwicklung zur Ableitung von Pflanzenparametern für Winterweizen aus multispektralen (EO-1 ALI) und hyperspektralen (EO-1 Hyperion) Bestandsmessungen. Hierfür wurde ein Feldversuch in der Nordchinesischen Tiefebene durchgeführt, wobei Pflanzenparameter zu verschiedenen Wachstumsstadien aufgenommen wurden. Um die aufgenommen Parameter mit den Fernerkundungsdaten in Beziehung zu setzen, wurden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalized Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2.500 nm.

Für traditionelle Vegetationsindices (SR, NDVI und SAVI), berechnet aus Multispektral- sowie aus Hyperspektraldaten, wurden geringe statistische Beziehungen zu Pflanzenparametern erzielt. Neben den Standardspektralbereichen (grün, rot, nahes Infrarot) bietet die hohe spektrale Auflösung des Hyperion Sensors jedoch die Möglichkeit, weitere Spektralbereiche mit Pflanzenparametern in Beziehung zu setzen. Aus der Untersuchung aller möglicher 2-Band Kombinationen konnten starke Korrelationen zwischen Pflanzenparametern und Fernerkundungsdaten bei der Kombination von Bändern aus dem nahen Infrarot (NIR) mit Bändern aus dem kurwelligen Infrarot (SWIR) festgestellt werden. Für die Pflanzenparameter Biomasse, Pflanzenstickstoffgehalt und Pflanzenhöhe wurden Korrela tionen (R2) von 0,83, 0,81 und 0,79 erzielt. Das Ergebnis der Studie zeigt, dass sich Anwendungsoptimierte Vegetationsindices, berechnet aus schmalen hyperspektralen Bändern des RE, NIR und SWIR, zur Ableitung von Pflanzenparametern eignen und gegenüber Standard Vegetationsindices deutlich bessere Ergebnisse liefern können.
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Document Type: Research Article

Publication date: 01 July 2010

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  • Photogrammetrie - Fernerkundung - Geoinformation (PFG) is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the intricately connected field of geoinformation processing.

    Papers published in PFG highlight new developments and applications of these technologies in practice. The journal hence addresses both researchers and student of these disciplines at academic institutions and universities and the downstream users in both the private sector and public administration.

    PFG places special editorial emphasis on the communication of new methodologies in data acquisition, new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general.

    PFG is the official journal of the German Society of Photogrammetry and Remote Sensing.
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