Research Article
BibTex RIS Cite

Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini

Year 2020, Volume: 35 Issue: 1, 8 - 25, 14.02.2020
https://doi.org/10.7161/omuanajas.551781

Abstract

Toprak
neminin konumsal ve zamansal olarak dağılımı, kurak ve yarı kurak bölgelerde
kuraklık izlemesi, ürün sulama planlaması, ürün tahmini gibi havza seviyesindeki
tarımsal uygulamalarda anahtar bir parametredir. Ayrıca, radar uydu görüntüleri
çeşitli bölgeler için toprak ve bitki örtüsü dağılımının mekânsal ve zamansal
olarak ortaya konulmasını sağlamak için kullanılmaktadır. Aktif mikrodalga
sensör sistemleri kullanarak yüzey toprağı neminin tahmini araştırmacılar,
koruma planlamacıları ve doğal kaynakların sürdürülebilir kullanımını izleyen
karar vericiler için yararlı bilgilerden biridir. Bu çalışma, yarı kurak iklime
sahip Altınova Tarım İşletmesi arazisinde seçilen altmış dört kilometrekarelik
test alanı topraklarında yürütülmüştür. Dört farklı zamanda elde edilen
Sentetik Açıklıklı Radar (SAR) görüntülerinin gerisaçılım değerleri
(Radarsat-2) ve yüzey toprağı nemi arasındaki ilişki belirlenmeye
çalışılmıştır. Bu amaçla, Altınova Tarım İşletmesine ait dört SAR görüntüsü (4
tane Radarsat-2 görüntüsü) kullanılmıştır. Eş zamanlı olarak, 730 farklı
noktada 250 m aralıklarla yüzey toprak örnekleri 0-20 cm’den alınmış ve çalışma
alanı boyunca gravimetrik yöntem kullanılarak yüzey toprağının nemi
belirlenmiştir. Her örnekleme periyodu için yüzey toprağı nem dağılım
haritaları ordinary kriging kullanılarak üretilmiştir. Toprak nem dağılım
haritalarına göre Ağustos verileri, çalışma alanı boyunca diğer örnekleme
dönemlerine kıyasla yüzey toprağı neminde en fazla değişiklikleri göstermiştir.
Bu nedenle çalışma alanı boyunca gerisaçılma (Ağustos 2012 Radarsat-2
verilerinden elde edilen) ile toprak nemi içeriği arasındaki ilişkinin diğer
SAR veri sonuçlarından daha iyi olduğu bulunmuştur (r=0.506, p<0.05). 

References

  • Alexander, L., Ralf L., Wolfram, M., 2006. Deviation of surface soil moisture from ENNVISAT ASAR wide swath and image mode Data in agricultural area, IEEE Trans. Geosci. Remote Sensing, 44 (4): 889-99.
  • Alvarez-Mazos, J., Casali, J., Gonzalez-Audicana, M., Verhoest, N. E. C., 2005. “Correlation between ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricultural catchments of Navvare (North of Spain). Biosystems Engineering 92 : 119-133.
  • Baghdadi, N., King, C., Chanzy, A., Wigneron, J. P., 2002. An empirical calibration of the integral equation model based on SAR Data, soil moisture and surface roughness measured over bare soils. International Journal of Remote Sensing 23: 4325-4340.
  • Baghdadı, N., Holah, N., Zribi, M., 2006. Soil moisture estimation using multi-incident and multi-polarization ASAR data. Int. J. Remote Sens., 27 (10): 1907–20.
  • Bayramin, İ., Kılıç, Ş., Dengiz, O., Başkan, O., Tunçay, T., Yıldırım, A., Koç, A., Öğütmen, Ç., 2013. Radar görüntülerinin toprak etüt ve haritalama çalışmalarında kullanımı. TUBİTAK TOVAG 110 O 729 nolu TOVAG Projesi Sonuç Raporu.
  • Beaudoin, A., Le Toan, T., Gwyn, Q. H. J., 1990. SAR observations and modeling of the C-Band backscatter variability due to the multiscale geometry and soil moisture. IEEE Transactions on Geoscience and Remote Sensing 28: 886-895.
  • Boisvert, J. B., Gwyn, Q.H., Chanzy, A., Major, D. J., Brisco, B., Brown, R. J., 1997. Effects of soil moisture gradients on modeling radar backscattering from bare soils. International Journal of Remote Sensing 18: 153-170.
  • Chen, J. S., Lın, H., Pei, Z. Y., 2007. Application of ENVISAT ASAR data in mapping rice crop growth in Southern China. IEEE Trans. Geosci. Remote Sens., 4 (3): 431–35.
  • De Lannoy, G.J.M., Verhoest, N.E.C., Houser, P.R., Gish, T.J., van Meirvenne, M., 2006. Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3). Journal of Hydrology 331, 719–730.
  • Eliason, E.M., McEwen, A.S., 1990. Adaptive box filters for removal of random noise from digital images. Photogrammetric Engineering & Remote Sensing, vol. 56, no. 4: 453, 1990. Fung, A. K., Chen, K. S., 1992. Backscattering from a randomly rough dielectric surface. IEEE Trans. on Geosci. and Remote Sensing, 30(2): 356-69.
  • Gish, T.J., Walthall, C.L., Daughtry, C.S.T., Kung, K.J.S., 2005. Using soil moisture and spatial yield patterns to identify subsurface flow pathways. Journal of Environmental Quality 34: 274–286.
  • Gish, T.J., Prueger, J.H., Daughtry, C.S.T., Kustas, W.P., McKee, L.G., Russ, A.L., Hatfield, J.L., 2011. Comparison of field-scale herbicide runoff and volatilization losses: an eight-year field investigation. Journal of Environmental Quality 40, 1432–1442.
  • Hegarat-mascle, S.L., Zribi, M., Alem, F., Weisse, A., Loumagne, C., 2000. Soil moisture estimation from ERS/SAR data: toward an operational methodology. Remote Sensing Environmental, 72: 290 – 303.
  • Huangg, Y. and Genderen, J.L., 1996. Evaluation of several filtering techniques for ERS-1&2 Imagery. International Archives of Photogrammetry and Remote Sensing Vol. XXXI Part B2. Vienna 1996.
  • Holah, N., Baghdadı, N., Zribi, M., Brund, A., King, C., 2005. Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields. Remote Sens. Environ, 96 (1): 78–86.
  • Jackson, T., Schmugge, J., Engman, E., 1996. Remote sensing applications to hydrology: soil moisture. Hydrol. Sci. J. 41: 517–530.
  • Kelly, R.E.J., Davie, T.J.A., Atkinson, P. M., 2003. Explaining temporal and spatial variation in soil moisture in a bare field using SAR imagery. International Journal of Remote Sensing 24, 3059-3074.
  • Kutilek, M., Nielsen, D.R., 1994. Soil hydrology. Catena Verlag: Cremlingen-Destedt, Germany.
  • Li, Z., 2004. Soil moisture measurement and retrieval using Envisat ASAR imagery. In: IEEE Geoscience Remote Sensing Proceedings, 5 (20–24),pp: 3539–42.
  • Lin, H.S., Kogelmann, W., Walker C, Bruns, M.A., 2006a. Soil moisture patterns in a forested catchment: a hydropedological perspective. Geoderma 131, 345–368.
  • Lin, H.S., Bouma, J., Pachepsky, Y., Western, A.W., Thompson, J.A., van Genuchten, M.T., Vogel, H., Lilly, A., 2006b. Hydropedology: synergistic integration of pedology and hydrology. Water Resources Research 42, W05301.
  • Lin, H.S., Zhou, X.B., 2008. Evidence of subsurface preferential flow using soil hydrologic monitoring in the Shale Hills catchment. European Journal of Soil Science 59, 34–49.
  • Löw, H., Ludwig, R., Mauser, W., 2005. Use of microwave remote sensing data to montor spatio temporal characteristics of surface soil moisture at local and regional scales. Advanges in Geosicences 5, 49-56.
  • Moran, M.S., Hymer, D.c., Qi, J., Sano, E.E., 2000. Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR)in semiarid rangeland. Agricultural and Forest Meteorology (105): 69-90.
  • Oldak, A., Jackson, T.J., Starks, P., Elliott, R., 2003. Mapping near surface soil moisture on regional scale using ERS-2 SAR data. International Journal of Remote Sensing 24: 579-4598.
  • Paloscia, S., Pettinato, S., 2008. A Comparison of algorithms for retrieving soil moisture from ENVISAT/SAR images. IEEE Trans. on Geoscience and Remote Sensing, vol. 46, No. 10.
  • Shakil, A. Romshoo, T. O., Katumi, M., 2000. A Multi- polarized and multi- angle C- band radar system for soil mositure determination under bare soil condition. Asian Association on Remote Sensing, Proceeding ACRS, Section:14, SAR/InSAR.
  • Shao, Y., Hu, Q., Hu, H., Guo, Y., Lu, Q., Dong, Chunming Han., 2003. Effect of dielectric properties of moist salinized soils on backscattering coefficients extracted from RADARSAT image. IEEE Transactions on Geoscience and Remote Sensing, No: 41: 1879–1888.
  • Siegert, F., Ruecker, G., 2000. Use of multitemporal ERS-2 SAR images for identification of burned scars in South-East Asian tropical forest. International Journal of Remote Sensing 21: 831-837.
  • Soil Survey Staff., 2015. Illustrated guide to Soil Taxonomy. Version 1.1. U.S. Department of Agriculture. National Resources Conservation Service, Lincoln, Nebraska.
  • Trangmar, B.B., Yost, R.J., Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Advance in Agronomy, 38, 65-91.
  • Trangmar, B.B., Yost, R.J., Wade, M.K., Uehara, G. and Sudjadi, M., 1987. Spatial variation of soil properties and rice yield on recently cleared land. Soil Sci. Soc. Am. J., 51, 668-674.
  • Walker, J.P., Houser, P.R., Willgoose, G.R., 2004. Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS-2. Hydrol. Process., 18: 1975–1997.
  • Wang, C., Qi, J., Moran, S., Marsett, R., 2003. Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery. Remote Sensing of Environment 90: 178-189.
  • Western, A.W., Grayson, R.B., Blöschl, G., Willgoose, G.R., McMahon, T.A., 1999. Observed spatial organization of soil moisture and its relation to terrain indices. Water Resources Research 35, 797–810.
  • Western, A.W., Zhou, S.L., Grayson, R.B., McMahon, T.A., Blöschl, G., Wilson, D.J., 2004. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology 286, 113–134.
  • Zribi, M., O. N., Holah N., Fafin, O., Guerin, C. 2005a. Evaluation of rough surface soil description with ASAR-ENVISAT radar data. Remote Sensing of Environment 95: 67-76.Zribi, M., Baghdadi, N., Holah, N., Fafin, O., 2005b. New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR Multiincidence data inversion. Remote Sensing of Environment 96: 485-496.

Estimation of soil moisture by synthetic aparture radar (microwave) images in semi arid regions

Year 2020, Volume: 35 Issue: 1, 8 - 25, 14.02.2020
https://doi.org/10.7161/omuanajas.551781

Abstract

Spatial and temporal distribution of soil moisture
is a key parameter for agricultural applications at watershed level such as
drought monitoring, crop irrigation scheduling, and yield estimations in arid
and semi-arid regions. Moreover, radar satellite imagery systems have been used
to figure out soil and vegetation distributions spatially and temporally for
various regions. Estimation of surface soil moisture using active microwave
sensor systems is among useful information for researchers, conservation
planners, and decision makers pursuing sustainable use of natural resources.
This study was carried out at the soils of selected sixty-four square
kilometers test site in Altınova State Farm. It was aimed to determine the
relationship between the surface soil moisture and the backscatter values of
SAR images (Radarsat-2) obtained four different times. To that end, four SAR
images (4 Radarsat-2 images) from Altınova State Farm were used. Surface soil
samples were collected simultaneously from 0-20 cm depth at 730 different
points with 250 m-intervals, and soil moisture was determined using gravimetric
method throughout the study area. In regards to each sampling period, surface
soil moisture distribution maps were produced using ordinary kriging method.
Considering the soil moisture distribution maps the data obtained in August
indicated the most alterations in the surface soil moisture throughout the
study area in comparison to the other sampling periods. Therefore, it was
revealed that the relationship between backscattering (obtained from Radarsat-2
data in August, 2012) and soil moisture content was better than the other SAR
data results (r=0.506, p<0.05). 

References

  • Alexander, L., Ralf L., Wolfram, M., 2006. Deviation of surface soil moisture from ENNVISAT ASAR wide swath and image mode Data in agricultural area, IEEE Trans. Geosci. Remote Sensing, 44 (4): 889-99.
  • Alvarez-Mazos, J., Casali, J., Gonzalez-Audicana, M., Verhoest, N. E. C., 2005. “Correlation between ground measured soil moisture and RADARSAT-1 derived backscattering coefficient over an agricultural catchments of Navvare (North of Spain). Biosystems Engineering 92 : 119-133.
  • Baghdadi, N., King, C., Chanzy, A., Wigneron, J. P., 2002. An empirical calibration of the integral equation model based on SAR Data, soil moisture and surface roughness measured over bare soils. International Journal of Remote Sensing 23: 4325-4340.
  • Baghdadı, N., Holah, N., Zribi, M., 2006. Soil moisture estimation using multi-incident and multi-polarization ASAR data. Int. J. Remote Sens., 27 (10): 1907–20.
  • Bayramin, İ., Kılıç, Ş., Dengiz, O., Başkan, O., Tunçay, T., Yıldırım, A., Koç, A., Öğütmen, Ç., 2013. Radar görüntülerinin toprak etüt ve haritalama çalışmalarında kullanımı. TUBİTAK TOVAG 110 O 729 nolu TOVAG Projesi Sonuç Raporu.
  • Beaudoin, A., Le Toan, T., Gwyn, Q. H. J., 1990. SAR observations and modeling of the C-Band backscatter variability due to the multiscale geometry and soil moisture. IEEE Transactions on Geoscience and Remote Sensing 28: 886-895.
  • Boisvert, J. B., Gwyn, Q.H., Chanzy, A., Major, D. J., Brisco, B., Brown, R. J., 1997. Effects of soil moisture gradients on modeling radar backscattering from bare soils. International Journal of Remote Sensing 18: 153-170.
  • Chen, J. S., Lın, H., Pei, Z. Y., 2007. Application of ENVISAT ASAR data in mapping rice crop growth in Southern China. IEEE Trans. Geosci. Remote Sens., 4 (3): 431–35.
  • De Lannoy, G.J.M., Verhoest, N.E.C., Houser, P.R., Gish, T.J., van Meirvenne, M., 2006. Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3). Journal of Hydrology 331, 719–730.
  • Eliason, E.M., McEwen, A.S., 1990. Adaptive box filters for removal of random noise from digital images. Photogrammetric Engineering & Remote Sensing, vol. 56, no. 4: 453, 1990. Fung, A. K., Chen, K. S., 1992. Backscattering from a randomly rough dielectric surface. IEEE Trans. on Geosci. and Remote Sensing, 30(2): 356-69.
  • Gish, T.J., Walthall, C.L., Daughtry, C.S.T., Kung, K.J.S., 2005. Using soil moisture and spatial yield patterns to identify subsurface flow pathways. Journal of Environmental Quality 34: 274–286.
  • Gish, T.J., Prueger, J.H., Daughtry, C.S.T., Kustas, W.P., McKee, L.G., Russ, A.L., Hatfield, J.L., 2011. Comparison of field-scale herbicide runoff and volatilization losses: an eight-year field investigation. Journal of Environmental Quality 40, 1432–1442.
  • Hegarat-mascle, S.L., Zribi, M., Alem, F., Weisse, A., Loumagne, C., 2000. Soil moisture estimation from ERS/SAR data: toward an operational methodology. Remote Sensing Environmental, 72: 290 – 303.
  • Huangg, Y. and Genderen, J.L., 1996. Evaluation of several filtering techniques for ERS-1&2 Imagery. International Archives of Photogrammetry and Remote Sensing Vol. XXXI Part B2. Vienna 1996.
  • Holah, N., Baghdadı, N., Zribi, M., Brund, A., King, C., 2005. Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields. Remote Sens. Environ, 96 (1): 78–86.
  • Jackson, T., Schmugge, J., Engman, E., 1996. Remote sensing applications to hydrology: soil moisture. Hydrol. Sci. J. 41: 517–530.
  • Kelly, R.E.J., Davie, T.J.A., Atkinson, P. M., 2003. Explaining temporal and spatial variation in soil moisture in a bare field using SAR imagery. International Journal of Remote Sensing 24, 3059-3074.
  • Kutilek, M., Nielsen, D.R., 1994. Soil hydrology. Catena Verlag: Cremlingen-Destedt, Germany.
  • Li, Z., 2004. Soil moisture measurement and retrieval using Envisat ASAR imagery. In: IEEE Geoscience Remote Sensing Proceedings, 5 (20–24),pp: 3539–42.
  • Lin, H.S., Kogelmann, W., Walker C, Bruns, M.A., 2006a. Soil moisture patterns in a forested catchment: a hydropedological perspective. Geoderma 131, 345–368.
  • Lin, H.S., Bouma, J., Pachepsky, Y., Western, A.W., Thompson, J.A., van Genuchten, M.T., Vogel, H., Lilly, A., 2006b. Hydropedology: synergistic integration of pedology and hydrology. Water Resources Research 42, W05301.
  • Lin, H.S., Zhou, X.B., 2008. Evidence of subsurface preferential flow using soil hydrologic monitoring in the Shale Hills catchment. European Journal of Soil Science 59, 34–49.
  • Löw, H., Ludwig, R., Mauser, W., 2005. Use of microwave remote sensing data to montor spatio temporal characteristics of surface soil moisture at local and regional scales. Advanges in Geosicences 5, 49-56.
  • Moran, M.S., Hymer, D.c., Qi, J., Sano, E.E., 2000. Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR)in semiarid rangeland. Agricultural and Forest Meteorology (105): 69-90.
  • Oldak, A., Jackson, T.J., Starks, P., Elliott, R., 2003. Mapping near surface soil moisture on regional scale using ERS-2 SAR data. International Journal of Remote Sensing 24: 579-4598.
  • Paloscia, S., Pettinato, S., 2008. A Comparison of algorithms for retrieving soil moisture from ENVISAT/SAR images. IEEE Trans. on Geoscience and Remote Sensing, vol. 46, No. 10.
  • Shakil, A. Romshoo, T. O., Katumi, M., 2000. A Multi- polarized and multi- angle C- band radar system for soil mositure determination under bare soil condition. Asian Association on Remote Sensing, Proceeding ACRS, Section:14, SAR/InSAR.
  • Shao, Y., Hu, Q., Hu, H., Guo, Y., Lu, Q., Dong, Chunming Han., 2003. Effect of dielectric properties of moist salinized soils on backscattering coefficients extracted from RADARSAT image. IEEE Transactions on Geoscience and Remote Sensing, No: 41: 1879–1888.
  • Siegert, F., Ruecker, G., 2000. Use of multitemporal ERS-2 SAR images for identification of burned scars in South-East Asian tropical forest. International Journal of Remote Sensing 21: 831-837.
  • Soil Survey Staff., 2015. Illustrated guide to Soil Taxonomy. Version 1.1. U.S. Department of Agriculture. National Resources Conservation Service, Lincoln, Nebraska.
  • Trangmar, B.B., Yost, R.J., Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Advance in Agronomy, 38, 65-91.
  • Trangmar, B.B., Yost, R.J., Wade, M.K., Uehara, G. and Sudjadi, M., 1987. Spatial variation of soil properties and rice yield on recently cleared land. Soil Sci. Soc. Am. J., 51, 668-674.
  • Walker, J.P., Houser, P.R., Willgoose, G.R., 2004. Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS-2. Hydrol. Process., 18: 1975–1997.
  • Wang, C., Qi, J., Moran, S., Marsett, R., 2003. Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery. Remote Sensing of Environment 90: 178-189.
  • Western, A.W., Grayson, R.B., Blöschl, G., Willgoose, G.R., McMahon, T.A., 1999. Observed spatial organization of soil moisture and its relation to terrain indices. Water Resources Research 35, 797–810.
  • Western, A.W., Zhou, S.L., Grayson, R.B., McMahon, T.A., Blöschl, G., Wilson, D.J., 2004. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology 286, 113–134.
  • Zribi, M., O. N., Holah N., Fafin, O., Guerin, C. 2005a. Evaluation of rough surface soil description with ASAR-ENVISAT radar data. Remote Sensing of Environment 95: 67-76.Zribi, M., Baghdadi, N., Holah, N., Fafin, O., 2005b. New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR Multiincidence data inversion. Remote Sensing of Environment 96: 485-496.
There are 37 citations in total.

Details

Primary Language Turkish
Journal Section Anadolu Tarım Bilimleri Dergisi
Authors

Tülay Tunçay 0000-0001-5398-5497

Publication Date February 14, 2020
Acceptance Date January 7, 2020
Published in Issue Year 2020 Volume: 35 Issue: 1

Cite

APA Tunçay, T. (2020). Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini. Anadolu Tarım Bilimleri Dergisi, 35(1), 8-25. https://doi.org/10.7161/omuanajas.551781
AMA Tunçay T. Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini. ANAJAS. February 2020;35(1):8-25. doi:10.7161/omuanajas.551781
Chicago Tunçay, Tülay. “Yarı Kurak bölgelerde Sentetik açıklıklı Radar (mikrodalga) görüntüleri Ile Toprak Neminin Tahmini”. Anadolu Tarım Bilimleri Dergisi 35, no. 1 (February 2020): 8-25. https://doi.org/10.7161/omuanajas.551781.
EndNote Tunçay T (February 1, 2020) Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini. Anadolu Tarım Bilimleri Dergisi 35 1 8–25.
IEEE T. Tunçay, “Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini”, ANAJAS, vol. 35, no. 1, pp. 8–25, 2020, doi: 10.7161/omuanajas.551781.
ISNAD Tunçay, Tülay. “Yarı Kurak bölgelerde Sentetik açıklıklı Radar (mikrodalga) görüntüleri Ile Toprak Neminin Tahmini”. Anadolu Tarım Bilimleri Dergisi 35/1 (February 2020), 8-25. https://doi.org/10.7161/omuanajas.551781.
JAMA Tunçay T. Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini. ANAJAS. 2020;35:8–25.
MLA Tunçay, Tülay. “Yarı Kurak bölgelerde Sentetik açıklıklı Radar (mikrodalga) görüntüleri Ile Toprak Neminin Tahmini”. Anadolu Tarım Bilimleri Dergisi, vol. 35, no. 1, 2020, pp. 8-25, doi:10.7161/omuanajas.551781.
Vancouver Tunçay T. Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini. ANAJAS. 2020;35(1):8-25.
Online ISSN: 1308-8769