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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 Universität Berlin » Publications |
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