Research Article
BibTex RIS Cite

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

Year 2024, Volume: 6 Issue: 2, 63 - 71, 31.12.2024

Fizyon Sönmez Erdoğan , Mehmet Akif Erdoğan

https://doi.org/10.51534/tiha.1493413

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.

Keywords

Precision agriculture soil moisture remote sensing vegetation indices unmanned aerial vehicle

References

  • Adegoke, J. O., & Carleton, A. M. (2002). Relations between soil moisture and satellite vegetation indices in the US Corn Belt. Journal of Hydrometeorology, 3(4), 395-405.
  • Ahmad, S., Kalra, A., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69-80.
  • Ainiwaer, M., Ding, J., Kasim, N., Wang, J., & Wang, J. (2020). Regional scale soil moisture content estimation based on multi-source remote sensing parameters. International Journal of Remote Sensing, 41(9), 3346-3367.
  • Akkamış, M., & Çalışkan, S. (2020). İnsansız Hava Araçları ve Tarımsal Uygulamalarda Kullanımı. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 8-16.
  • Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., & Siebert, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317-337.
  • Barzin, R., Pathak, R., Lotfi, H., Varco, J., & Bora, G. C. (2020). Use of UAS multispectral imagery at different physiological stages for yield prediction and input resource optimization in corn. Remote Sensing, 12(15), 2392.
  • Boretti, A., & Rosa, L. (2019). Reassessing the projections of the world water development report. NPJ Clean Water, 2(1), 15.
  • Çakmak, B., & Gökalp, Z. (2011). İklim değişikliği ve etkin su kullanımı. Tarım Bilimleri Araştırma Dergisi, (1), 87-95.
  • Cassman, K. G., Grassini, P., & van Wart, J. (2010). Crop yield potential, yield trends, and global food security in a changing climate. In Handbook of Climate Change and Agroecosystems (pp. 37-51). London: Imperial College Press.
  • Çetin, Ö. (2003). Toprak-su ilişkileri ve toprak suyu ölçüm yöntemleri. Köy Hizmetleri Genel Müdürlüğü, Eskişehir Araştırma Enstitüsü Müdürlüğü, Genel Yayın (258), 100.
  • Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., & Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the-art and future directions. Remote Sensing of Environment, 203, 185-215.
  • Entekhabi, D., Njoku, E. G., O'neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., & Van Zyl, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704-716.
  • Foster, T., Brozović, N., & Butler, A. P. (2015). Why well yield matters for managing agricultural drought risk. Weather and Climate Extremes, 10, 11-19.
  • Gaikwad, P., Devendrachari, M. C., Thimmappa, R., Paswan, B., Kottaichamy, A. J., Kotresh, H. M. N., & Hotiyl, M. O. (2015). Galvanic cell type sensor for soil moisture analysis. Analytical Chemistry, 87(14), 7439-7445.
  • García-Martínez, H., et al. (2020). Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture, 10(7), 277.
  • Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science, 327(5967), 812-818.
  • Gosling, S. N., & Arnell, N. W. (2016). A global assessment of the impact of climate change on water scarcity. Climatic Change, 134, 371-385.
  • Gracia-Romero, A., Kefauver, S. C., Vergara-Díaz, O., Zaman-Allah, M. A., Prasanna, B. M., Cairns, J. E., & Araus, J. L. (2017). Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Frontiers in Plant Science, 8, 2004.
  • Groten, S. M. E. (1993). NDVI—crop monitoring and early yield assessment of Burkina Faso. TitleREMOTE SENSING, 14(8), 1495-1515.
  • Gu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X., & Xu, J. (2020). Irrigation scheduling approaches and applications: A review. Journal of Irrigation and Drainage Engineering, 146(6), 04020007.
  • Gül, S., Güzey, Y. Z., Yıldırım, H., & Keskin, M. (2021). Eye of the farmer in the sky: Drones. Türkiye İnsansız Hava Araçları Dergisi, 3(2), 69-77. https://doi.org/10.51534/tiha.943842
  • Hajnsek, I., Jagdhuber, T., Schon, H., & Papathanassiou, K. P. (2009). Potential of estimating soil moisture under vegetation cover by means of PolSAR. IEEE Transactions on Geoscience and Remote Sensing, 47, 442-454.
  • Han, Y., Qiao, D., & Lu, H. (2023). Spatial-temporal coupling pattern between irrigation demand and soil moisture dynamics throughout wheat-maize rotation system in the North China Plain. European Journal of Agronomy, 151, 126970.
  • Hoss, D. F., Luz, G. L. D., Lajús, C. R., Moretto, M. A., & Tremea, G. A. (2020). Multispectral aerial images for the evaluation of maize crops. Ciência e Agrotecnologia, 44, e004920.
  • Hosseini, M., & Saradjian, M. R. (2011). Multi-index-based soil moisture estimation using MODIS images. International Journal of Remote Sensing, 32(21), 6799-6809.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  • Huete, A. R., Liu, H. Q., Batchily, K. V., & Van Leeuwen, W. J. D. A. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440-451.
  • Hunt Jr, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S., & McCarty, G. W. (2010). Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2(1), 290-305.
  • Jiang, G., Grafton, M., Pearson, D., Bretherton, M., & Holmes, A. (2019). Integration of precision farming data and spatial statistical modelling to interpret field-scale maize productivity. Agriculture, 9(11), 237.
  • Karaman, S., & Gökalp, Z. (2010). Küresel Isınma ve İklim Değişikliğinin Su Kaynakları Üzerine Etkileri. Tarım Bilimleri Araştırma Dergisi, 3(1), 59-66.
  • Kaufman, Y. J., & Tanré, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 261-270.
  • Li, N., Skaggs, T. H., Ellegaard, P., Bernal, A., & Scudiero, E. (2024). Relationships among soil moisture at various depths under diverse climate, land cover, and soil texture. Science of The Total Environment, 174583.
  • Liang, M., Pause, M., Prechtel, N., & Schramm, M. (2020). Regionalization of coarse scale soil moisture products using fine-scale vegetation indices—Prospects and case study. Remote Sensing, 12(3), 551.
  • Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457-465.
  • Liu, K., Li, X., Wang, S., & Zhang, H. (2023). A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning. Hydrology and Earth System Sciences, 27(2), 577-598.
  • Liu, Q., Wu, Z., Cui, N., Jin, X., Zhu, S., Jiang, S., & Gong, D. (2023). Estimation of soil moisture using multi-source remote sensing and machine learning algorithms in farming land of Northern China. Remote Sensing, 15(17), 4214.
  • Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors, 7(11), 2636-2651.
  • Molden, D., Oweis, T., Steduto, P., Bindraban, P., Hanjra, M. A., & Kijne, J. (2010). Improving agricultural water productivity: Between optimism and caution. Agricultural Water Management, 97(4), 528-535.
  • Mouazen, A. M., & Shi, Z. (2021). Estimation and mapping of soil properties based on multi-source data fusion. Remote Sensing, 13(5), 978.
  • Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490(7419), 254-257.
  • Mulla, D. J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371.
  • Myers, E., Kerekes, J., Daughtry, C., & Russ, A. (2019). Assessing the impact of satellite revisit rate on estimation of corn phenological transition timing through shape model fitting. Remote Sensing, 11(21), 2558.
  • Ozdogan, M., Yang, Y., Allez, G., & Cervantes, C. (2010). Remote sensing of irrigated agriculture: Opportunities and challenges. Remote Sensing, 2(9), 2274-2304.
  • Pimentel, D., Houser, J., Preiss, E., White, O., Fang, H., Mesnick, L., & Alpert, S. (1997). Water resources: agriculture, the environment, and society. BioScience, 47(2), 97-106.
  • Pinter Jr, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647-664.
  • Pretty, J., Benton, T. G., Bharucha, Z. P., Dicks, L. V., Flora, C. B., Godfray, H. C. J., & Wratten, S. (2018). Global assessment of agricultural system redesign for sustainable intensification. Nature Sustainability, 1(8), 441-446.
  • Qiu, J., Crow, W. T., Wagner, W., & Zhao, T. (2019). Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation, 80, 47-57.
  • Radočaj, D., Šiljeg, A., Marinović, R., & Jurišić, M. (2023). State of major vegetation indices in precision agriculture studies indexed in Web of Science: A review. Agriculture, 13(3), 707.
  • Reichle, R. H., Ardizzone, J. V., Kim, G. K., Lucchesi, R. A., Smith, E. B., & Weiss, B. H. (2022). Soil Moisture Active Passive (SMAP) mission level 4 surface and root zone soil moisture (L4_SM) product specification document.
  • Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107.
  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ, 351(1), 309.
  • Saha, A., Patil, M., Goyal, V. C., & Rathore, D. S. (2018). Assessment and impact of soil moisture index in agricultural drought estimation using remote sensing and GIS techniques. In Proceedings (Vol. 7, No. 1, p. 2). MDPI.
  • Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B., & Kabat, P. (2014). Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences, 111(9), 3245-3250.
  • Schwalbert, R. A., Amado, T. J., Nieto, L., Varela, S., Corassa, G. M., Horbe, T. A., & Ciampitti, I. A. (2018). Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystems Engineering, 171, 179-192.
  • Şenol, C. (2021). Innovation, support, sustainability: Turkish economy and agriculture. International Journal of Geography and Geography Education (IGGE), 44, 475-788.
  • Siraç, M., & Acar, E. (2017). Toprak nemi tahmini için Radarsat-2 verisinden çoklu saçılma katsayılarının elde edilmesi. Dicle Üniversitesi Mühendislik Fakültesi Dergisi, 8(4), 759-766.
  • Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart Sensors and Smart Data for Precision Agriculture: A Review. Sensors, 24(8), 2647.
  • Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water (Vol. 1028, p. 99). Rome, Italy: FAO.
  • Teke, M., Deveci, S., Öztoprak, F., Efendioğlu, M., Küpçü, R., Demirkesen, C., Şimşek, F. F., & Bayramin, İ. (2016). Akıllı tarım fizibilite projesi: hassas tarım uygulamaları için havadan ve yerden veri toplanması, işlenmesi ve analizi. 6. Uzaktan Algılama-CBS Sempozyumu (UZALCBS 2016), 5-7 Ekim 2016, Adana.
  • Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260-20264.
  • Toscano, F., Fiorentino, C., Capece, N., Erra, U., Travascia, D., Scopa, A., & D’Antonio, P. (2024). Unmanned Aerial Vehicle for Precision Agriculture: A Review. IEEE Access.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  • UNESCO. (2020). The United Nations world water development report 2020: Water and climate change. UN.
  • Uslu, H., & Apaydın, F. (2021). Türkiye’de tarımsal verimlilik ve alan bazlı desteklemeler üzerine ampirik bir uygulama. Hitit Sosyal Bilimler Dergisi, 14(2), 477-499.
  • Wada, Y., Gleeson, T., & Esnault, L. (2014). Wedge approach to water stress. Nature Geoscience, 7(9), 615-617.
  • Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2), 191-207.
  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017(1), 1353691.
  • Yetkin, A. K., & Aşık, M. (2021). Toprak Nem İçeriğinin İzlenmesi ve Tayininde Kullanılan Yöntemler. BŞEÜ Fen Bilimleri Dergisi, 8(1), 484-496.
  • Yüksel, D., & İnanç, A. L. (2022). Geleneksel Yöntem ve Direkt Fermantasyon ile Üretilen Maraş Tarhanalarında Biyoaktif Peptitlerin Belirlenmesi. KSÜ Tarım ve Doğa Dergisi, 25(2), 357-366.
  • Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693-712.
  • Zhang, X., Zhang, K., Sun, Y., Zhao, Y., Zhuang, H., Ban, W., & Hao, Y. (2022). Combining spectral and texture features of UAS-based multispectral images for maize leaf area index estimation. Remote Sensing, 14(2), 331.

İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması

Year 2024, Volume: 6 Issue: 2, 63 - 71, 31.12.2024

Fizyon Sönmez Erdoğan , Mehmet Akif Erdoğan

https://doi.org/10.51534/tiha.1493413

Abstract

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.

Keywords

Hassas tarım toprak nemi uzaktan algılama vejetasyon indeksleri insansız hava aracı

Ethical Statement

Çalışmada etik beyanına gerek duyulmamaktadır.

Thanks

Bu çalışmanın gerçekleştirilmesinde kullanılan veri setleri TÜBİTAK 1512 - BİGG Teknogirişim Sermaye Desteği Programı Aşama 2 kapsamında desteklenen 2190170 numaralı ve “AGRONE: Tarımsal İzleme Bilgi Paketi Geliştirilmesi” başlıklı proje kapsamında üretilmiş olup ilgili proje kapsamında kurulan Geodynamic Coğrafi Bilgi Sistemleri ve Danışmanlık Ltd. Şti.’nin izniyle kullanılmıştır.

References

  • Adegoke, J. O., & Carleton, A. M. (2002). Relations between soil moisture and satellite vegetation indices in the US Corn Belt. Journal of Hydrometeorology, 3(4), 395-405.
  • Ahmad, S., Kalra, A., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69-80.
  • Ainiwaer, M., Ding, J., Kasim, N., Wang, J., & Wang, J. (2020). Regional scale soil moisture content estimation based on multi-source remote sensing parameters. International Journal of Remote Sensing, 41(9), 3346-3367.
  • Akkamış, M., & Çalışkan, S. (2020). İnsansız Hava Araçları ve Tarımsal Uygulamalarda Kullanımı. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 8-16.
  • Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., & Siebert, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317-337.
  • Barzin, R., Pathak, R., Lotfi, H., Varco, J., & Bora, G. C. (2020). Use of UAS multispectral imagery at different physiological stages for yield prediction and input resource optimization in corn. Remote Sensing, 12(15), 2392.
  • Boretti, A., & Rosa, L. (2019). Reassessing the projections of the world water development report. NPJ Clean Water, 2(1), 15.
  • Çakmak, B., & Gökalp, Z. (2011). İklim değişikliği ve etkin su kullanımı. Tarım Bilimleri Araştırma Dergisi, (1), 87-95.
  • Cassman, K. G., Grassini, P., & van Wart, J. (2010). Crop yield potential, yield trends, and global food security in a changing climate. In Handbook of Climate Change and Agroecosystems (pp. 37-51). London: Imperial College Press.
  • Çetin, Ö. (2003). Toprak-su ilişkileri ve toprak suyu ölçüm yöntemleri. Köy Hizmetleri Genel Müdürlüğü, Eskişehir Araştırma Enstitüsü Müdürlüğü, Genel Yayın (258), 100.
  • Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., & Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the-art and future directions. Remote Sensing of Environment, 203, 185-215.
  • Entekhabi, D., Njoku, E. G., O'neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., & Van Zyl, J. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704-716.
  • Foster, T., Brozović, N., & Butler, A. P. (2015). Why well yield matters for managing agricultural drought risk. Weather and Climate Extremes, 10, 11-19.
  • Gaikwad, P., Devendrachari, M. C., Thimmappa, R., Paswan, B., Kottaichamy, A. J., Kotresh, H. M. N., & Hotiyl, M. O. (2015). Galvanic cell type sensor for soil moisture analysis. Analytical Chemistry, 87(14), 7439-7445.
  • García-Martínez, H., et al. (2020). Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture, 10(7), 277.
  • Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science, 327(5967), 812-818.
  • Gosling, S. N., & Arnell, N. W. (2016). A global assessment of the impact of climate change on water scarcity. Climatic Change, 134, 371-385.
  • Gracia-Romero, A., Kefauver, S. C., Vergara-Díaz, O., Zaman-Allah, M. A., Prasanna, B. M., Cairns, J. E., & Araus, J. L. (2017). Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Frontiers in Plant Science, 8, 2004.
  • Groten, S. M. E. (1993). NDVI—crop monitoring and early yield assessment of Burkina Faso. TitleREMOTE SENSING, 14(8), 1495-1515.
  • Gu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X., & Xu, J. (2020). Irrigation scheduling approaches and applications: A review. Journal of Irrigation and Drainage Engineering, 146(6), 04020007.
  • Gül, S., Güzey, Y. Z., Yıldırım, H., & Keskin, M. (2021). Eye of the farmer in the sky: Drones. Türkiye İnsansız Hava Araçları Dergisi, 3(2), 69-77. https://doi.org/10.51534/tiha.943842
  • Hajnsek, I., Jagdhuber, T., Schon, H., & Papathanassiou, K. P. (2009). Potential of estimating soil moisture under vegetation cover by means of PolSAR. IEEE Transactions on Geoscience and Remote Sensing, 47, 442-454.
  • Han, Y., Qiao, D., & Lu, H. (2023). Spatial-temporal coupling pattern between irrigation demand and soil moisture dynamics throughout wheat-maize rotation system in the North China Plain. European Journal of Agronomy, 151, 126970.
  • Hoss, D. F., Luz, G. L. D., Lajús, C. R., Moretto, M. A., & Tremea, G. A. (2020). Multispectral aerial images for the evaluation of maize crops. Ciência e Agrotecnologia, 44, e004920.
  • Hosseini, M., & Saradjian, M. R. (2011). Multi-index-based soil moisture estimation using MODIS images. International Journal of Remote Sensing, 32(21), 6799-6809.
  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309.
  • Huete, A. R., Liu, H. Q., Batchily, K. V., & Van Leeuwen, W. J. D. A. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440-451.
  • Hunt Jr, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S., & McCarty, G. W. (2010). Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2(1), 290-305.
  • Jiang, G., Grafton, M., Pearson, D., Bretherton, M., & Holmes, A. (2019). Integration of precision farming data and spatial statistical modelling to interpret field-scale maize productivity. Agriculture, 9(11), 237.
  • Karaman, S., & Gökalp, Z. (2010). Küresel Isınma ve İklim Değişikliğinin Su Kaynakları Üzerine Etkileri. Tarım Bilimleri Araştırma Dergisi, 3(1), 59-66.
  • Kaufman, Y. J., & Tanré, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 261-270.
  • Li, N., Skaggs, T. H., Ellegaard, P., Bernal, A., & Scudiero, E. (2024). Relationships among soil moisture at various depths under diverse climate, land cover, and soil texture. Science of The Total Environment, 174583.
  • Liang, M., Pause, M., Prechtel, N., & Schramm, M. (2020). Regionalization of coarse scale soil moisture products using fine-scale vegetation indices—Prospects and case study. Remote Sensing, 12(3), 551.
  • Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457-465.
  • Liu, K., Li, X., Wang, S., & Zhang, H. (2023). A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning. Hydrology and Earth System Sciences, 27(2), 577-598.
  • Liu, Q., Wu, Z., Cui, N., Jin, X., Zhu, S., Jiang, S., & Gong, D. (2023). Estimation of soil moisture using multi-source remote sensing and machine learning algorithms in farming land of Northern China. Remote Sensing, 15(17), 4214.
  • Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors, 7(11), 2636-2651.
  • Molden, D., Oweis, T., Steduto, P., Bindraban, P., Hanjra, M. A., & Kijne, J. (2010). Improving agricultural water productivity: Between optimism and caution. Agricultural Water Management, 97(4), 528-535.
  • Mouazen, A. M., & Shi, Z. (2021). Estimation and mapping of soil properties based on multi-source data fusion. Remote Sensing, 13(5), 978.
  • Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490(7419), 254-257.
  • Mulla, D. J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371.
  • Myers, E., Kerekes, J., Daughtry, C., & Russ, A. (2019). Assessing the impact of satellite revisit rate on estimation of corn phenological transition timing through shape model fitting. Remote Sensing, 11(21), 2558.
  • Ozdogan, M., Yang, Y., Allez, G., & Cervantes, C. (2010). Remote sensing of irrigated agriculture: Opportunities and challenges. Remote Sensing, 2(9), 2274-2304.
  • Pimentel, D., Houser, J., Preiss, E., White, O., Fang, H., Mesnick, L., & Alpert, S. (1997). Water resources: agriculture, the environment, and society. BioScience, 47(2), 97-106.
  • Pinter Jr, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647-664.
  • Pretty, J., Benton, T. G., Bharucha, Z. P., Dicks, L. V., Flora, C. B., Godfray, H. C. J., & Wratten, S. (2018). Global assessment of agricultural system redesign for sustainable intensification. Nature Sustainability, 1(8), 441-446.
  • Qiu, J., Crow, W. T., Wagner, W., & Zhao, T. (2019). Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation, 80, 47-57.
  • Radočaj, D., Šiljeg, A., Marinović, R., & Jurišić, M. (2023). State of major vegetation indices in precision agriculture studies indexed in Web of Science: A review. Agriculture, 13(3), 707.
  • Reichle, R. H., Ardizzone, J. V., Kim, G. K., Lucchesi, R. A., Smith, E. B., & Weiss, B. H. (2022). Soil Moisture Active Passive (SMAP) mission level 4 surface and root zone soil moisture (L4_SM) product specification document.
  • Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107.
  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ, 351(1), 309.
  • Saha, A., Patil, M., Goyal, V. C., & Rathore, D. S. (2018). Assessment and impact of soil moisture index in agricultural drought estimation using remote sensing and GIS techniques. In Proceedings (Vol. 7, No. 1, p. 2). MDPI.
  • Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B., & Kabat, P. (2014). Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences, 111(9), 3245-3250.
  • Schwalbert, R. A., Amado, T. J., Nieto, L., Varela, S., Corassa, G. M., Horbe, T. A., & Ciampitti, I. A. (2018). Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystems Engineering, 171, 179-192.
  • Şenol, C. (2021). Innovation, support, sustainability: Turkish economy and agriculture. International Journal of Geography and Geography Education (IGGE), 44, 475-788.
  • Siraç, M., & Acar, E. (2017). Toprak nemi tahmini için Radarsat-2 verisinden çoklu saçılma katsayılarının elde edilmesi. Dicle Üniversitesi Mühendislik Fakültesi Dergisi, 8(4), 759-766.
  • Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart Sensors and Smart Data for Precision Agriculture: A Review. Sensors, 24(8), 2647.
  • Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water (Vol. 1028, p. 99). Rome, Italy: FAO.
  • Teke, M., Deveci, S., Öztoprak, F., Efendioğlu, M., Küpçü, R., Demirkesen, C., Şimşek, F. F., & Bayramin, İ. (2016). Akıllı tarım fizibilite projesi: hassas tarım uygulamaları için havadan ve yerden veri toplanması, işlenmesi ve analizi. 6. Uzaktan Algılama-CBS Sempozyumu (UZALCBS 2016), 5-7 Ekim 2016, Adana.
  • Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260-20264.
  • Toscano, F., Fiorentino, C., Capece, N., Erra, U., Travascia, D., Scopa, A., & D’Antonio, P. (2024). Unmanned Aerial Vehicle for Precision Agriculture: A Review. IEEE Access.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150.
  • UNESCO. (2020). The United Nations world water development report 2020: Water and climate change. UN.
  • Uslu, H., & Apaydın, F. (2021). Türkiye’de tarımsal verimlilik ve alan bazlı desteklemeler üzerine ampirik bir uygulama. Hitit Sosyal Bilimler Dergisi, 14(2), 477-499.
  • Wada, Y., Gleeson, T., & Esnault, L. (2014). Wedge approach to water stress. Nature Geoscience, 7(9), 615-617.
  • Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2), 191-207.
  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017(1), 1353691.
  • Yetkin, A. K., & Aşık, M. (2021). Toprak Nem İçeriğinin İzlenmesi ve Tayininde Kullanılan Yöntemler. BŞEÜ Fen Bilimleri Dergisi, 8(1), 484-496.
  • Yüksel, D., & İnanç, A. L. (2022). Geleneksel Yöntem ve Direkt Fermantasyon ile Üretilen Maraş Tarhanalarında Biyoaktif Peptitlerin Belirlenmesi. KSÜ Tarım ve Doğa Dergisi, 25(2), 357-366.
  • Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693-712.
  • Zhang, X., Zhang, K., Sun, Y., Zhao, Y., Zhuang, H., Ban, W., & Hao, Y. (2022). Combining spectral and texture features of UAS-based multispectral images for maize leaf area index estimation. Remote Sensing, 14(2), 331.
There are 71 citations in total.

Details

Primary Language Turkish
Subjects Land Management
Journal Section Research Articles
Authors

Fizyon Sönmez Erdoğan 0000-0002-8648-0687

Mehmet Akif Erdoğan 0000-0002-8346-3590

Publication Date December 31, 2024
Submission Date May 31, 2024
Acceptance Date November 5, 2024
Published in Issue Year 2024 Volume: 6 Issue: 2

Cite

APA Sönmez Erdoğan, F., & Erdoğan, M. A. (2024). İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması. Türkiye İnsansız Hava Araçları Dergisi, 6(2), 63-71. https://doi.org/10.51534/tiha.1493413
AMA Sönmez Erdoğan F, Erdoğan MA. İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması. tiha. December 2024;6(2):63-71. doi:10.51534/tiha.1493413
Chicago Sönmez Erdoğan, Fizyon, and Mehmet Akif Erdoğan. “İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması”. Türkiye İnsansız Hava Araçları Dergisi 6, no. 2 (December 2024): 63-71. https://doi.org/10.51534/tiha.1493413.
EndNote Sönmez Erdoğan F, Erdoğan MA (December 1, 2024) İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması. Türkiye İnsansız Hava Araçları Dergisi 6 2 63–71.
IEEE F. Sönmez Erdoğan and M. A. Erdoğan, “İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması”, tiha, vol. 6, no. 2, pp. 63–71, 2024, doi: 10.51534/tiha.1493413.
ISNAD Sönmez Erdoğan, Fizyon - Erdoğan, Mehmet Akif. “İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması”. Türkiye İnsansız Hava Araçları Dergisi 6/2 (December 2024), 63-71. https://doi.org/10.51534/tiha.1493413.
JAMA Sönmez Erdoğan F, Erdoğan MA. İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması. tiha. 2024;6:63–71.
MLA Sönmez Erdoğan, Fizyon and Mehmet Akif Erdoğan. “İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması”. Türkiye İnsansız Hava Araçları Dergisi, vol. 6, no. 2, 2024, pp. 63-71, doi:10.51534/tiha.1493413.
Vancouver Sönmez Erdoğan F, Erdoğan MA. İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması. tiha. 2024;6(2):63-71.
 Download Cover Image