Machine learning based regression model for prediction of soil surface humidity over moderately vegetated fields

Emrullah Acar, Mehmet Sirac Ozerdem, Burak Berk Ustundag

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

16 Atıf (Scopus)

Özet

The soil surface humidity parameter over vegetated fields is of great importance for controlling water consumption; prevention of salinity caused by over-irrigation; efficient use of irrigation system and improving the yield and quality of the cultivated crop. However, determination of the soil surface humidity is very difficult on vegetated fields. In order to overcome this problem, polarimetric decomposition models and machine learning based regression model were implemented. The main purpose of this study is to predict soil surface humidity on moderately vegetated fields. Thus, the study is conducted in agricultural fields of Dicle University and it consists of several stages. In the first stage, a Radarsat-2 data was obtained in 3 March 2016 and the local humidity samples were measured simultaneously with the Radarsat-2 acquisition. In the second stage, 10 polarimetric features were obtained from each cell (2x2 pixels) of ground sample by utilizing standard intensity-phase technique as well as Freeman-Durden and H/A/α polarimetric decomposition models. This step is repeated for all ground samples and as a result, a dataset with 156x10 lengths is formed. In the next stage, Extreme Learning Machine based Regression (ELM-R) model was used for predicting the soil surface humidity with the aid of polarimetric SAR features. For the validation of the proposed system, leave-one-out cross-validation method was applied and finally, 2.19% Root Mean Square Error (RMSE) were computed.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728121161
DOI'lar
Yayın durumuYayınlandı - Tem 2019
Etkinlik8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019 - Istanbul, Turkey
Süre: 16 Tem 201919 Tem 2019

Yayın serisi

Adı2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019

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???event.eventtypes.event.conference???8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot16/07/1919/07/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Finansman

ACKNOWLEDGMENT The authors would like to thank ESA for providing the Sentinel-I software. This work was backed up by TUBITAK 1001 (No. 114E543), Dicle University Scientific Research Projects (DUBAP) and TARBIL project. The authors would like to thank ESA for providing the Sentinel-I software. This work was backed up by TUBITAK 1001 (No. 114E543), Dicle University Scientific Research Projects (DUBAP) and TARBIL project.

FinansörlerFinansör numarası
DUBAP
TUBITAK 10011001, 114E543
Ecological Society of America
Firat University Scientific Research Projects Management Unit

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