Satellite-Based Monitoring of Vegetation and Soil Moisture Indices for Stress Detection and Irrigation Planning in a Semi-Arid Apple Orchard

Yıl 2025, Cilt: 21 Sayı: 2, 139 - 161, 31.08.2025

Alperay Altıkat

Öz

Sustainable irrigation management is crucial for fruit production in semi-arid regions. This study utilized satellite data collected between 2020 and 2025 to evaluate plant growth and changes in soil moisture in a 15.59-acre, drip-irrigated apple orchard located in eastern Türkiye. Spectral indices, such as NDVI, SAVI, and NDRE, along with radar-based indices like RVI and RSM, were combined to analyze the temporal and spatial patterns of plant stress. The results revealed significant stress conditions in 2020, 2021, and 2023. In contrast, 2024 saw 44% of the orchard reach the medium-high vegetation cover class. High correlations (r ≈ 0.99) were found between RSM, NDVI, and RVI indices, indicating that plant health is directly related to soil moisture. The SAVI and NDRE indices, on the other hand, enabled the sensitive monitoring of both early- and late-stage stress. These data defined area-based productivity maps and stress zones, and a field-level decision support framework was established to support irrigation decisions. The study proposes a repeatable method for precision agriculture applications, contributing to the mitigation of the effects of climate variability on yield.

Anahtar Kelimeler

Remote sensing indices , Soil moisture interaction , Phenological stress detection

Etik Beyan

The author gratefully acknowledges the use of ChatGPT for grammatical and linguistic enhancement during the preparation of this manuscript. After utilizing these tools, they reviewed and edited the content as needed and accepted full responsibility for the content of the publication.

Destekleyen Kurum

Yok

Teşekkür

Yok

Yarı kurak iklim koşullarında bir elma bahçesinde stres tespiti ve sulama planlaması için uydu tabanlı bitki örtüsü ve toprak nemi endekslerinin izlenmesi

Öz

Sürdürülebilir sulama yönetimi, yarı kurak bölgelerde meyve üretimi için kritik öneme sahiptir. Bu çalışmada, 2020 ile 2025 yılları arasında toplanan uydu verileri, Türkiye'nin doğusunda bulunan 63 dekar büyüklüğünde, damla sulama sistemine sahip bir elma bahçesindeki bitki büyümesi ve toprak nemi değişikliklerini değerlendirmek için kullanılmıştır. NDVI, SAVI ve NDRE gibi spektral indeksler ile RVI ve RSM gibi radar tabanlı indeksler birleştirilerek bitki stresinin zamansal ve mekansal örüntüleri analiz edilmiştir. Sonuçlar, 2020, 2021 ve 2023 yıllarında önemli stres koşulları ortaya çıkarken, 2024 yılında bahçenin %44'ünün orta-yüksek bitki örtüsü sınıfına ulaştığını ortaya koymuştur. RSM, NDVI ve RVI indeksleri arasında yüksek korelasyonlar (r ≈ 0,99) bulunmuş olup, bu da bitki sağlığının toprak nemi ile doğrudan ilişkili olduğunu göstermektedir. SAVI ve NDRE indeksleri ise erken ve geç aşama stresin hassas bir şekilde izlenmesini sağlamıştır. Bu veriler, alan bazlı verimlilik haritaları ve stres bölgeleri tanımlamış ve sulama kararlarını desteklemek için tarla düzeyinde bir karar destek çerçevesi oluşturulmuştur. Çalışma, iklim değişkenliğinin verim üzerindeki etkilerini azaltmaya katkıda bulunan, hassas tarım uygulamaları için tekrarlanabilir bir yöntem önermektedir.

Anahtar Kelimeler

Uzaktan algılama indeksleri , Toprak nemi etkileşimi , Fenolojik stres tespiti

Etik Beyan

The author gratefully acknowledges the use of ChatGPT for grammatical and linguistic enhancement during the preparation of this manuscript. After utilizing these tools, they reviewed and edited the content as needed and accepted full responsibility for the content of the publication.

Destekleyen Kurum

Yok

Teşekkür

Yok

Kaynakça

• Anderson, K. (2024). Detecting environmental stress in agriculture using satellite imagery and spectral indices. ResearchGate. https://www.researchgate.net/publication/378122204
• Berry, A., Vivier, M. A., & Poblete-Echeverría, C. (2024). Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map sensor status variability in a commercial vineyard. Irrigation Science. https://doi.org/10.1007/s00271-023-00907-1
• Carella, A., Bulacio Fischer, P. T., Massenti, R., & Lo Bianco, R. (2024). Continuous plant-based and remote sensing for the determination of fruit tree water status. Horticulturae, 10(5), 516. https://doi.org/10.3390/horticulturae10050516
• Crespo, N., Pádua, L., Santos, J. A., & Fraga, H. (2024). Satellite remote sensing tools for drought assessment in vineyards and olive orchards: A systematic review. Remote Sensing, 16(11), 2040. https://doi.org/10.3390/rs16112040
• Dong, H., Dong, J., Sun, S., Bai, T., Zhao, D., & Yin, Y. (2024). Crop water stress detection based on UAV remote sensing systems. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2024.108259
• Duarte, E., & Hernandez, A. (2024). A review on soil moisture dynamics monitoring in semi-arid ecosystems: Methods, techniques, and tools applied at different scales. Applied Sciences, 14(17), 7677. https://doi.org/10.3390/app14177677
• Gaznayee, H. A. A., Zaki, S. H., & Al-Quraishi, A. M. F. (2023). Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity. Water, 15(8), 1605. https://doi.org/10.3390/w15081605
• Guimarães, N., Sousa, J. J., Pádua, L., Bento, A., & Couto, P. (2024). Remote sensing applications in almond orchards: A comprehensive systematic review of current insights, research gaps, and prospects. Applied Sciences, 14(5), 1749. https://doi.org/10.3390/app14051749
• Hasan, S. S., Alharbi, O. A., Alqurashi, A. F., & Fahil, A. S. (2024). Assessment of desertification dynamics in arid coastal areas by integrating remote sensing data and statistical techniques. Sustainability, 16(11), 4527. https://www.mdpi.com/2071-1050/16/11/4527
• Hu, X., Li, L., Huang, J., Zeng, Y., Zhang, S., & Su, Y. (2024). Radar Vegetation Indices for Monitoring Surface Vegetation: Developments, Challenges, and Trends. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2024.170854
• Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
• Ippolito, M. (2023). Assessing crop water requirements and irrigation scheduling at different spatial scales in Mediterranean orchards using models, proximal and remotely sensed data [Doctoral dissertation]. Italian National Repository. https://tesidottorato.depositolegale.it/handle/20.500.14242/172965
• Kaya, P. D. S. N., & Öztürk, R. E. (n.d.). Soil quality and vegetation index assessments for rice and wheat cultivations: A review. ResearchGate Preprint. https://www.researchgate.net/publication/376851588
• Lakhiar, I. A., Yan, H., Zhang, C., Wang, G., He, B., & Hao, B. (2024). A review of precision irrigation water-saving technology under a changing climate for enhancing water use efficiency, crop yield, and environmental footprints. ResearchGate. https://www.researchgate.net/publication/377173663
• Mali, S. S., Scobie, M., Baillie, J., Plant, C., & Shammi, S. (2025). Integrating UAV-based multispectral and thermal infrared imagery with machine learning for predicting water stress in winter wheat. Precision Agriculture. https://link.springer.com/article/10.1007/s11119-025-10239-z
• Nalçaoğlu, Ç. Ğ., Nalçaoğlu, D. H., & Doğru, A. (2025). Predicting olive yield in Mediterranean climate zones of Türkiye using remote sensing and artificial neural networks: A case study of Muğla Province. Preprints. https://www.preprints.org/frontend/manuscript/7b9ac7d093830875cf265d810cec131a/download_pub
• Odi-Lara, M., Campos, I., Neale, C. M. U., & Ortega-Farías, S. (2016). Estimating evapotranspiration of an apple orchard using a remote sensing-based soil water balance. Remote Sensing, 8(3), 253. https://www.mdpi.com/2072-4292/8/3/253
• Peres, D. J., & Cancelliere, A. (2021). Analysis of multi-spectral images acquired by UAVs to monitor water stress of citrus orchards in Sicily, Italy. In World Environmental and Water Resources Congress 2021 (pp. 251–260). https://doi.org/10.1061/9780784483466.025
• Pradipta, A., Soupios, P., Kourgialas, N., Doula, M., & Dokou, Z. (2022). Remote sensing, geophysics, and modeling to support precision agriculture—Part 2: Irrigation management. Water, 14(7), 1157. https://doi.org/10.3390/w14071157
• Sellami, M. H., Albrizio, R., Čolović, M., & Hamze, M. (2022). Selection of hyperspectral vegetation indices for monitoring yield and physiological response in sweet maize under different water and nitrogen availability. Agronomy, 12(2), 489. https://doi.org/10.3390/agronomy12020489
• Sharma, H., Sidhu, H., & Bhowmik, A. (2025). Remote sensing using uncrewed aerial vehicles for water stress detection: A review focusing on specialty crops. Drones, 9(4), 241. https://doi.org/10.3390/drones9040241
• Sillero-Medina, J. A., & González-Pérez, J. (2025). Deficit irrigation regimens on soil moisture, production parameters of mango (Mangifera indica L.), and spectral vegetation indices in the Mediterranean region.
• Heliyon, 11, e2799. https://www.sciencedirect.com/science/article/pii/S2352938524002799
• Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://www.mdpi.com/2072-4292/12/19/3136
• Stagakis, S., González-Dugo, V., & Cid, P. (2012). Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 47–61. https://doi.org/10.1016/j.isprsjprs.2012.05.002
• Yang, X., Gao, F., Yuan, H., & Cao, X. (2024). Integrated UAV and satellite multi-spectral for agricultural drought monitoring of winter wheat in the seedling stage. Sensors, 24(17), 5715. https://doi.org/10.3390/s24175715