Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-15318
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Main Title: Suitability of satellite remote sensing data for yield estimation in northeast Germany
Author(s): Vallentin, Claudia
Harfenmeister, Katharina
Itzerott, Sibylle
Kleinschmit, Birgit
Conrad, Christopher
Spengler, Daniel
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/16541
http://dx.doi.org/10.14279/depositonce-15318
License: https://creativecommons.org/licenses/by/4.0/
Abstract: Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.
Subject(s): vegetation Index
yield estimation
multispectral remote sensing
precision farming
Issue Date: 17-Jun-2021
Date Available: 10-Mar-2022
Is Part Of: 10.14279/depositonce-11174
Language Code: en
DDC Class: 550 Geowissenschaften
Journal Title: Precision Agriculture
Publisher: Springer Nature
Volume: 23
Publisher DOI: 10.1007/s11119-021-09827-6
Page Start: 52
Page End: 82
EISSN: 1573-1618
ISSN: 1385-2256
TU Affiliation(s): Fak. 6 Planen Bauen Umwelt » Inst. Landschaftsarchitektur und Umweltplanung » FG Geoinformation in der Umweltplanung
Appears in Collections:Technische Universit├Ąt Berlin » Publications

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