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Journals / Türkiye İnsansız Hava Araçları Dergisi / 2024 / Cilt: 6 Sayı: 2

Mapping of Soil Moisture Using an Unmanned Aerial Vehicle in a Maize Field

İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması

Fizyon Sönmez Erdoğan(HATAY MUSTAFA KEMAL ÜNİVERSİTESİ, MİMARLIK FAKÜLTESİ)0000-0002-8648-0687
Mehmet Akif Erdoğan(HATAY MUSTAFA KEMAL ÜNİVERSİTESİ, MİMARLIK FAKÜLTESİ, PEYZAJ MİMARLIĞI BÖLÜMÜ)0000-0002-8346-3590
Pages
63–71
Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Additionally, this will result in more effective irrigation planning for agriculture.

📌 3 Key Takeaways

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Objective & Hypothesis:This study aimed to estimate soil moisture spatially by using unmanned aerial vehicle, remote sensing and geographical information systems in a maize-cultivated parcel.
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Key Findings & Data:The ortho-mosaic image created by a multispectral sensor integrated into the UAV system, the vegetation indices derived from this image, and the soil moisture measurements made using a digital moisture meter were utilized as inputs to predict soil moisture using a linear stepwise multiple regression method.
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Conclusion & Impact:Additionally, this will result in more effective irrigation planning for agriculture.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Hassas tarım · toprak nemi · uzaktan algılama

Abstract

This study aimed to estimate soil moisture spatially by using unmanned aerial vehicle, remote sensing and geographical information systems in a maize-cultivated parcel. The ortho-mosaic image created by a multispectral sensor integrated into the UAV system, the vegetation indices derived from this image, and the soil moisture measurements made using a digital moisture meter were utilized as inputs to predict soil moisture using a linear stepwise multiple regression method. A backward stepwise linear multiple regression at a 90% confidence interval among the eight vegetation indices that were produced led to the formation of the soil moisture prediction equation (R2: 0.81), which was derived from the red edge and near-infrared bands, ARVI, NDVI, red edge EVI, and red edge SAVI indices. Soil moisture was mapped for the entire field using the obtained prediction and the accuracy test revealed an R2 value of 0.74. The sensor characteristics, image capture dates, and combinations of vegetation indexes used vary, although the result is nearly identical to the accuracy rates of multiple comparable studies from various regions of the world for maize crop in the literature. These findings demonstrate that the integration of unmanned aerial vehicle (UAV) technologies, geographic information systems, and remote sensing has enabled faster and more cost-effective spatial estimation and mapping of soil moisture. Additionally, this will result in more effective irrigation planning for agriculture.

Özet

Bu çalışma mısır ekili bir parsel örneğinde insansız hava aracı kullanımı, uzaktan algılama ve coğrafi bilgi sistemleri yardımıyla toprak neminin konumsal olarak tahmini amaçlamıştır. Dijital nem ölçer ile toplanan toprak nemi ölçümleri ile İnsanız Hava Aracı (İHA) sistemine entegre bir multispektral sensör kullanılarak üretilen ortomozaik görüntüsü ve de bu görüntüden üretilen vejetasyon indislerinin girdi olarak kullanıldığı çoklu doğrusal regresyon yöntemi ile toprak nemi tahmini gerçekleştirilmiştir. Üretilen sekiz vejetasyon indisi içinden %90 güven aralığına gerçekleştirilen geriye adım çoklu doğrusal regresyon analizi sonucunda önem seviyesinde çıkan kızıl eşik ve yakın kızıl ötesi bantlar ile ARVI, NDVI, kızıl eşik EVI ve kızıl eşik SAVI katmanlarından toprak nemi tahmin denklemi (R2: 0,81) oluşturulmuştur. Elde edilen tahmin denklemi kullanılarak tüm tarla için toprak nemi haritalanmış ve yapılan doğruluk testine göre R2 değeri 0,74 olarak bulunmuştur. Elde edilen sonuç literatürde mısır ürünü için yapılan dünyanın farklı bölgelerinden benzer birkaç çalışma ile yakın doğruluk oranları sergilemekle beraber kullanılan sensör özellikleri, görüntü alım tarihleri ve vejetasyon indis kombinasyonları farklılık göstermektedir. Tüm bu sonuçlar göstermiştir ki uzaktan algılama, coğrafi bilgi sistemleri ve insansız hava aracı teknolojilerinin birlikte kullanılmasıyla çok daha ekonomik ve hızlı bir şekilde toprak neminin konumsal olarak tahmin edilmesi ve haritalanmasını olası hale getirmiştir. Bu durum aynı zamanda daha etkin tarımsal sulama planlamasına da yol açacaktır.

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Cite This Article

Erdoğan, F. S., Erdoğan, M. A. (2024). Mapping of Soil Moisture Using an Unmanned Aerial Vehicle in a Maize Field. Türkiye İnsansız Hava Araçları Dergisi, Cilt: 6 Sayı: 2, 63–71. https://doi.org/10.51534/tiha.1493413

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