Sulu ve Kuru Tarım Alanlarında Buğday Verim Tahmininde Bitki Örtüsü İndekslerinin Kullanımı

Year 2021, Volume: 8 Issue: 3, 736 - 746, 26.07.2021

Yunus Kaya , Nizar Polat

https://doi.org/10.30910/turkjans.864231

Cited By: 1

Abstract

Dünya nüfusunun artmasına paralel olarak tarım alanlarına olan ihtiyaç da artmaktadır. Son yıllarda tarım alanlarının ve bitki gelişiminin izlenmesi için geleneksel ve modern yöntemler sıklıkla kullanılmaktadır. Bu çalışmada çok zamanlı uydu görüntülerinden elde edilen bitki örtüsü indeksleri yardımıyla buğday bitkisinin fenolojik evreleri incelenmiş ve bitki örtüsü indeksleri ile verim değerleri kullanılarak verim tahmin modeli geliştirilmiştir. Çalışmada Şanlıurfa ilinde bulunan Tarım İşletmeleri Genel Müdürlüğü (TİGEM) arazisinde bulunan buğday tarlalarından sulu tarım ve kuru tarım yapılan tarlalardan 5’er tane seçilmiştir. 2015-2016, 2016-2017 ve 2017-2018 sezonlarına ait ekim ile hasat tarihleri arasında belirli zaman aralıklarında alınan Landsat-8 ve Sentinel-2 uydu görüntülerinden üretilen NDVI ve SAVI indeksleri yardımıyla buğday bitkisinin gelişim süreçleri incelenmiştir. Ayrıca 3 yıla ilişkin NDVI, SAVI, GNDVI ve MSAVI değerleri ile TİGEM’den temin edilen verim değerleri birlikte değerlendirilerek verim tahmin modeli kurulmuştur. Oluşturulan model 2018-2019 sezonunda hem sulu hem de kuru tarlalarda uygulanarak doğruluk analizi yapılmıştır. Sonuçlar incelendiğinde elde edilen modelin sulu tarlalarda kuru tarlalara oranda daha yüksek başarı gösterdiği görülmüştür. Ayrıca hem sulu tarlada hem de kuru tarlada elde edilen model %80 in üzerinde bir doğruluk göstermiştir.

Keywords

Uzaktan algılama, Verim tahmin modeli, Kışlık buğday verim tahmini, Fenolojik evre, Bitki örtüsü indeksi

Use of Vegetation Indices in Wheat Yield Estimation in Irrigated and Dry Agricultural Lands

Abstract

The need for agricultural land is rising depending on the increasing e world population. Traditional and modern methods are frequently used to monitor agricultural land. In this study, the phenological stages of the wheat plant were examined with the help of vegetation indices obtained from multi-temporal and multispectral satellite images, and a yield estimation model was established by using vegetation indices and yield values. In the study, were selected five irrigated and five dry farming fields of the wheat in the land of the General Directorate of Agricultural Enterprises ( TİGEM) in Şanlıurfa province. The development processes of the wheat plant were examined with the help of NDVI and SAVI indices produced from Landsat-8 and Sentinel-2 satellite images taken at certain time intervals between sowing and harvesting for the seasons 2015-2016, 2016-2017, and 2017-2018. In addition, the yield estimation model was established by evaluating the NDVI, SAVI, Green NDVI (GNDVI), and Modified SAVI (MSAVI) values for 3 years together with the yield values obtained from the TİGEM. Accuracy analysis was performed by applying the model created in both irrigated and dry fields in the 2018-2019 season. When the results were examined, it was seen that the obtained model showed higher success in irrigated fields compared to dry fields. In addition, the model obtained in both irrigated and dry fields showed an accuracy of over 80%.

Keywords

Remote sensing, Yield estimation model, Yield estimation of winter wheat, Phenological stage, Vegetation index

References

• Anonim 2017. Türkiye İstatistik Kurumu. Bitkisel Üretim İstatistikleri. https://data.tuik.gov.tr /kategori/getkategori?p=tarim-111&dil=1
• Anonymous 2017. The Global Land Outlook. https://knowledge.unccd.int/glo/ GLO_first_edition#:~:text=One%20of%20the%20UNCCD's%20main,and%20energy%20security%20for%20all.
• Becker-Reshef I, Vermote E, Lindeman M, Justice C 2010. A Generalized Regression-Based Model for Forecasting Winter Wheat Yields in Kansas and Ukraine Using MODIS Data. Remote Sensing of Environment, 114(6), 1312–1323.
• Benek S 2006. Şanlıurfa İlinin Tarımsal Yapısı, Sorunları ve Çözüm Önerileri. Turkish Journal Of Geographical Sciences 4(1): 67-91.
• Brown JF, Wardlow B, Tadesse T, Hayes MJ, Reed BC 2008. The Vegetation Drought Response Index (VEGDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation. GIScience and Remote Sensing 45(1):16-46.
• Craig ME 2001. A Resource Sharing Approach to Crop Identification and Estimation. Bethesda, ABD.
• Çelik MA, Karabulut M 2017. Uydu Tabanlı Kuraklık İndisi (SVI) Kullanılarak Yarı Kurak Akdeniz İkliminde (Kilis) Buğday Bitkisinin Kurak Koşullara Verdiği Tepkinin İncelenmesi. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi 15(1):111-130.
• Demirpolat C, Leloğlu UM 2018. Barley Yield Estimation with Sentinel-2 Vegetation Indices. In 2018 26th Signal Processing and Communications Applications Conference (SIU) 2-5 Mayıs 2018, İzmir.
• Ferguson MC 1982. Evaluation of Trends in Yield Models: Agristars Supporting Research. ABD, 123 sy.
• Fernandez-Ordonez YM, Soria-Ruiz J 2017. Maize Crop Yield Estimation with Remote Sensing and Empirical Models. International Geoscience and Remote Sensing Symposium, 25-30 Haziran 2017, Aachen, Almanya.
• Gitelson AA, Kaufman YJ, Merzlyak MN 1996. Use of A Green Channel in Remote Sensing of Global Vegetation from EOS- MODIS. Remote Sensing of Environment 58(3): 289–298.
• Gontia NK, Tiwari KN 2011. Yield Estimation Model and Water Productivity of Wheat Crop (Triticum Aestivum) in an Irrigation Command Using Remote Sensing and GIS. Journal of The Indian Society of Remote Sensing 39(1):27–37.
• Huete A 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 25(3): 298-309.
• Kalfas J, Xıao X, Vanegas D, Verma S, Suyker AE 2011. Modeling Gross Primary Productionof Irrigated and Rainfed Maize Using MODIS Imagery and CO2 Flux Tower Data. Agricultural and Forest Meteorology 151:1514-1528.
• Kaya, Y , Polat, N. 2021. Bitki İndeksleri Kullanarak Buğday Bitkisinin Rekolte Tahmini . Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi , 12 (1) , 99-110 . DOI: 10.24012/dumf.860325
• Meier U 2018. Growth Stages of Mono- and Dicotyledonous Plants-BBCH Monograph, Quedlinburg, Almanya, 204 sy.
• Mkhabela MS, Bullock P, Raj S, Wang S, Yang Y 2011. Crop Yield Forecasting on The Canadian Prairies Using MODIS NDVI Data. Agricultural and Forest Meteorology 151(3):385–393.
• Pinter PJ, Hatfield JL, Schepers JS, Barnes EM, Moran MS, Daughtry CS, Upchurch DR 2003. Remote Sensing for Crop Management. Photogrammetric Engineering & Remote Sensing 69(6):647-664.
• Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S 1994. A Modified Soil Adjusted Vegetation Index. Remote Sensing of Environment 48(2): 119-126.
• Raun WR, Solie JB, Johnson GV, Stone ML, Lukina EV, Thomason WE, Schepers JS 2001. In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance. Agronomy Journal 93(1): 131-138.
• Ren JQ, Chen ZX, Zhou QB, Tang HJ 2010. LAI-Based Regional Winter Wheat Yield Estimation by Remote Sensing. Chinese Journal of Applied Ecology 21(11): 2883-2888.
• Richardson AJ, Wiegand C 1977. Distinguishing Vegetation from Soil Background Information, Photogrammetric Engineering and Remote Sensing 43(12):1541–1552.
• Rouse Jw, Haas RH, Schell JA, Deering DW 1974. Monitoring Vegetation Systems in The Great Plains with ERTS. Third ERTS-I Symposium, Washington DC, ABD.
• Salazar L, Kogan F, Roytman L 2007. Use of Remote Sensing Data for Estimation of Winter Wheat Yield in The United States. International Journal of Remote Sensing 28(17):3795-3811.
• Tanaka DL 1989. Spring Wheat Plant Parameters As Affected by Fallow Methods in The Northern Great Plains. Soil Science Society of America Journal 53(5):1506–1511.
• Tucker CJ, Holben BN, Elgin JH, Mcmurtrey JE 1981. Remote Sensing of Total Dry-Matter Accumulation in Winter Wheat. Remote Sensing of Environment 11:171-189.
• Tucker CJ, Townshend JRG, Goff TE 1985. African Land Cover Classification Using Satellite Data. Science 9227(4685):369-375.
• Viovy N, Arino O, Belward AS 1992. The Best Index Slope Extraction (BISE): A Method Forreducting Noise in NDVI Time Series. International Journal of Remote Sensing 13(8):1585-1590.
• Yiğit AY, Yunus, K 2020. Sentinel-2A Uydu Verileri Kullanılarak Sel Alanlarının İncelenmesi: Düzce Örneği. Türkiye Uzaktan Algılama Dergisi 2(1), 1-9.