Pamuk Bitkisi Üretim Alanı Ortam Nem ve Sıcaklık Değerlerinin, SAR ve Optik Uydu Görüntüleri ile Tahmin Edebilirliğinin Araştırılması

Year 2023, , 2217 - 2228, 01.09.2023

Serkan Kılıçaslan , Remzi Ekinci , Mehmet Cengiz Arslanoglu

https://doi.org/10.21597/jist.1265099

Cited By: 1

Abstract

Mardin İli Artuklu ve Kızıltepe İlçelerine bağlı 8 adet köy ve 27 adet pamuk parselinde yürütülen çalışmada, arazilerde ortam nem ve sıcaklık değerleri, 6 saat aralıklar ile datalogger cihazları ile ölçülmüş ve kayıt altına alınmıştır. Data loggerlardan elde edilen veriler, Google Earth Engine (GEE) ortamında hazırlanan Sentinel-1 ve Landsat-8 uydu verileriyle analiz edilerek aralarındaki ilişki irdelenmiştir. Ortam nemi (ON) değerleri ile VV (R2=0.63), VV-VH (R2=0.68), Toprak Nem İndeksi (SMI) (R2=0.84) arasında yüksek ilişki saptanırken, VH (R2=0.05), LEE_VH (R2=0.07), LEE_VV (R2=0.56), GAMMA_VH (R2=0.09), GAMMA_VV (R2=0.50), MALIK_VH (R2=0.07), MALIK_VV (R2=0.57) ve Arazi Yüzey Sıcaklığı (LST) (R2=0.35) arasında düşük ilişki saptanmıştır. Ortam sıcaklığı (OS) değerleri ile LST (R2=0.80**) arasında yüksek ilişki saptanırken, VV (R2=0.51), VH (R2=0.06), VV-VH (R2=0.49), LEE_VH (R2=0.09), LEE_VV (R2=0.49), GAMMA_VH (R2=0.11, GAMMA_VV (R2=0.08), MALIK_VH (R2=0.08), MALIK_VV (R2=0.49) ve SMI (R2=0.50) arasında düşük ilişki saptanmıştır. Geniş ölçekli arazi çalışmalarında ortam nemi değerlerinin tahmin edilmesinde VV, VV-VH ve SMI indisi; ortam sıcaklığı değerlerinin tahmin edilmesinde LST bandı yüksek doğruluk ile kullanılabileceği sonucuna varıldığından tavsiye edilmiştir.

Keywords

Pamuk, Sıcaklık, Nem, SAR, Google Earth Engine

Supporting Institution

Dicle Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

FBE.21.009

Thanks

Bu çalışma, Serkan KILIÇASLAN tarafından Dicle Üniversitesi Fen Bilimleri Enstitüsü Tarla Bitkileri ana bilim dalında yürütülen “Pamukta (G. hirsutum L.) Bitki-Su İlişkisinin Saptanmasında Uydu Görüntülerinin Kullanma Olanaklarının Araştırılması” başlıklı Doktora tez çalışmasından üretilmiş olup, Dicle Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından FBE.21.009 nolu proje numarası ile desteklenmiştir. Bilimsel Araştırma Koordinasyon Birimi’ne desteğinden ötürü teşekkür ederiz.

Investigation of Predictability of Cotton Plant Production Area Ambient Humidity and Temperature Values with SAR and Optical Satellite Images

Abstract

In the study carried out in 8 villages and 27 cotton parcels of Mardin Province Artuklu and Kızıltepe Districts, ambient humidity and temperature values in the field were measured and recorded with data logger devices at 6-hour intervals. The data obtained from the data loggers were analyzed with the Sentinel-1 and Landsat-8 satellite data prepared in the Google Earth Engine (GEE) environment and the relationship between them was examined. While a high correlation was found between ambient humidity (ON) values and VV (R2=0.63), VV-VH (R2=0.68), Soil Moisture Index (SMI) (R2=0.84), low correlation was found between VV (R2=0.51), VH (R2=0.06), VV-VH (R2=0.49), LEE_VH (R2=0.09), LEE_VV (R2=0.49), GAMMA_VH (R2=0.11, GAMMA_VV (R2=0.08), MALIK_VH (R2=0.08), MALIK_VV (R2=0.49), SMI (R2=0.50). A high correlation was found between ambient temperature (OS) values and Land Surface Temperature (LST) (R2=0.80**). However, a low correlation was found between ambient temperature (OS) and VV (R2=0.51), VH (R2=0.06), VV-VH (R2=0.49), LEE_VH (R2=0.09), LEE_VV (R2=0.49), GAMMA_VH (R2=0.11, GAMMA_VV (R2=0.08), MALIK_VH (R2=0.08), MALIK_VV (R2=0.49), SMI (R2=0.50). In large-scale field studies; VV, VV-VH and SMI index in estimating ambient humidity values; as it was concluded that the LST band can be used with high accuracy in estimating ambient temperature values, it has been recommended.

Keywords

Cotton, Temperature, Moisture, SAR, Google Earth Engine

Project Number

FBE.21.009

References

• Acar. E. ve Özerdem. M.S. (2020). On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach. Turkish Journal of Electrical Engineering & Computer Sciences (2020) 28: 2316 – 2330.
• Ahlmer. A.K.. Cavalli. M.. Hansson. K.. Koutsouris. A.J.. Crema. S. and Kalantari. Z. (2018). Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure. Environ Earth Sci 77. 533 (2018). https://doi.org/10.1007/s12665-018-7704-z.
• Aktaş. F.A. and Üstündağ. B.B. (2020). Soil moisture monitoring of the plant root zone by using phenology as context in remote sensing. Ieee Journal of Selected Topics In Applied Earth Observations And Remote Sensing. Vol. 13. 6051– 6063 04 September 2020.
• Amazirh. A. Merlin. O. Er-Raki. S. Gao. Q. Rivalland. V. Malbeteau. Y. ... & Escorihuela. M.J. (2018). Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil. Remote sensing of environment. 211. 321-337.
• Anonim. (2017). Türkiye’de sulanan bitkilerin bitki su tüketimleri. TAGEM
• Anonim. (2023). https://icac.org/DataPortal/DataPortal?Year=2020/21%20est Erişim: 10.01.2023
• Anonim. (2023a). http://www.nik.com.tr/content_sistem_uydu.asp?id=101 Erişim: 31.01.2023
• Anonim. (2023b). https://ntrs.nasa.gov/api/citations/20200001565/downloads/20200001565.pdf
• Anonim. (2023c). https://earthengine.google.com/faq/ Erişim: 31.12.2022
• Avdan. U. & Jovanovska. G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of sensors. 2016. 1-8.
• Bulut. B. Yilmaz. M.T. Afshar. M.H. Şorman. A.Ü. Yücel. İ. Cosh. M.H. and Şimşek. O. (2019). Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type. Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey. Remote Sens. 2019. 11. 1875. https://doi.org/10.3390/rs11161875.
• Chander. G. Markham. B.L. & Helder. D.L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS. TM. ETM+. and EO-1 ALI sensors. Remote sensing of environment. 113(5). 893-903.
• Cresson. R. Grizonnet. M. & Michel. J. (2018). Orfeo ToolBox Applications. QGIS and generic tools. 1. 151-242.
• El Ghandour. F.-E. Alfieri. S.M. Houali. Y. Habib. A. Akdim. N. Labbassi. K. and Menenti. M. (2019). Detecting the Response of Irrigation Water Management to Climate by Remote Sensing Monitoring of Evapotranspiration. Water 2019. 11. 2045. https://doi.org/10.3390/w11102045.
• Ghafarian H R. (2015). Reconstruction of Gap-Free Time Series Satellite Observations of Land Surface Temperature to Model Spectral Soil Thermal Admittance.The Netherlands:Delft University of Technology. DOI:10.4233/uuid:63dc3402-9fd6-4594-a00e-7aa5ae2501aa
• Hoskera. A.K. Nico. G. Irshad Ahmed. M. and Whitbread. A. (2020). Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data. Remote Sens. 2020. 12. 1664. https://doi.org/10.3390/rs12101664.
• Khabbazan. S. Vermunt. P. Steele-Dunne. S. Ratering Arntz. L. Marinetti. C. Van Der Valk. D. Iannini. L. Molijn. R. Westerdijk. K. and Van Der Sande. C. (2019). Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands. Remote Sens. 2019. 11. 1887. https://doi.org/10.3390/rs11161887.
• Khan, N. U. (2013). Diallel analysis of cotton leaf curl virus (CLCuV) disease, earliness, yield and fiber traits under CLCuV infestation in upland cotton. Australian journal of crop science, 7 (12), 1955-1966.
• Koçak. M. (2002). Elektriksel yöntemlerle algılanan toprak neminin sulama otomasyonunda kullanılması Ankara Üniv. Fen Bilimleri Enstitüsü Tarım Makinaları Anabilim Dalı Doktora Tezi.
• Makoei. E.B. (2015). Evaluation of three semi-empirical soıl moisture estimation models in agriculture areas with Radarsat-2 imagery processing in The Southeast Of Turkey. İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü Elektronik Ve Haberlesme Mühendisliği Anabilim Dalı Yüksek Lisans Tezi.
• Mansourpour. M. Rajabi. M. A. & Blais. J. A. R. (2006). Effects and performance of speckle noise reduction filters on active radar and SAR images. In Proc. Isprs (Vol. 36. No. 1. p. W41).
• Masoud. G. Mohammad. R.M. and Meisam. A. (2019). Soil moisture estimation using land surface temperature and soil temperature at 5 cm depth. International Journal of Remote Sensing. 40:1. 104-117. DOI: 10.1080/01431161.2018.1501167.
• Medasani. S. & Reddy. G.U. (2017). Analysis and evaluation of speckle filters for polarimetric synthetic aperture radar (PolSAR) data. International Journal of Applied Engineering Research. 12(15). 4916-4927.
• Mthandi. J. Kahimba. F. Tarimo. A. Salim. B. and Lowole. M. (2013). Root zone soil moisture redistribution in maize (Zea mays L.) under different water application regimes. Agricultural Sciences. 4. 521-528. doi: 10.4236/as.2013.410070.
• Navarro. A. Rolim. J. Miguel. I. Catalão. J. Silva. J. Painho. M. and Vekerdy. Z. (2016). Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements. Remote Sens. 2016. 8. 525. https://doi.org/10.3390/rs8060525.
• Özelkan. E. Bagis. S. Ozelkan. C.E. ve Üstündağ. B.B. (2014). Land surface temperature retrieval for climate analysis and association with climate data. European Journal of Remote Sensing – 2014.47: 655-669.
• Pablos. M. Martínez-Fernández. J. Piles. M. Sánchez. N. Vall-llossera. M. and Camps. A. (2016). Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations. Remote Sens. 2016. 8. 587. https://doi.org/10.3390/rs8070587.
• Qui. H. (2006). Rhermal remote sensing of soil moisture: validation of presumed linear relation between surface temperature gradient and soil moisture content. The University of Melbourne. Civil and Environmental Engineering Department. A final year research Project.
• Raper. T.B. (2014). In-season Drought Monitoring: Testing Instrumentation and Developing Methods of Measurement Analysis. Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2238.
• Rouse Jr. J.W. Haas. R.H. Schell. J.A. & Deering. D.W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. NASA-CR-132982).
• Saha. A. Patil. M. Goyal. V. and Rathore. D.S. (2018). Assessment and impact of soil moisture index in agricultural drought estimation using remote sensing and gis techniques. 3rd International Electronic Conference on Water Sciences (ECWS-3) Roorkee. Uttarakhand. India. 15–30 November 2018.
• Schmugge. T. Wilheit. T. Webster. W. and Gloersen. P. (1976). Remote sensing of soil moisture with microwave radiometers-II. Nasa Technical Note. Nasa TN D-8321. National Aeronautics and Space Administration Washington. D.C. September 1976.
• Şekertekin. A.İ. 2018. Aktif mikrodalga uydu görüntü verileri kullanılarak toprak neminin belirlenmesi. Bülent Ecevit Üniversitesi Fen Bilimleri Enstitüsü Geomatik Mühendisliği Anabilim Dalı Doktora Tezi.
• Sunar. F. Özkan. Ç. ve Osmanoğlu. B. (2016). Uzaktan Algılama. Eskişehir. Anadolu Üniversitesi.
• Yaşar, M. (2022). Evaluation of some new cotton genotypes against verticillum disease (Verticillum dahliae Kleb.). ISPEC Journal of Agricultural Sciences, 6 (1), 110-117. DOİ: https://doi.org/10.46291/ISPECJASvol6iss1pp110-117
• Yaşar, M. (2023). Yield and fiber quality traits of cotton (Gossypium hirsutum L.) cultivars analyzed by biplot method. Journal of King Saud University-Science, 35 (4), 102632. DOİ: https://doi.org/10.1016/j.jksus.2023.102632
• Yetbarek Acar. H. Özerdem. M.S. and Acar. E. (2020). Soil moisture inversion via semiempirical and machine learning methods with full-polarization Radarsat-2 and polarimetric target decomposition data: a comparative study. IEEE Access (Volume: 8) 197896- 197907 02 November 2020.
• Zeng. Y. Feng. Z. & Xiang. N. (2004). Assessment of soil moisture using Landsat ETM+ temperature/vegetation index in semiarid environment. In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium (Vol. 6. pp. 4306-4309).
• Zhang. D. and Zhou. G. (2016). Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review. Sensors (Basel. Switzerland). 16(8). 1308. https://doi.org/10.3390/s16081308.