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Validation of the LAI biophysical product derived from Sentinel-2 and Proba-V images for winter wheat in western Poland

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Warianty tytułu
PL
Walidacja produktu biofizycznego LAI generowanego na podstawie obrazów satelitarnych Sentinel-2 i Proba-V dla pszenicy ozimej w zachodniej Polsce
Języki publikacji
EN
Abstrakty
EN
The main objective of the work presented is to assess applicability of new-generation satellite data for deriving Leaf Area Index (LAI) information. Two types of data were used in the study: Sentinel-2 and Proba-V images. Various vegetation indices were derived from these data and correlated with ground measured LAI values. The results of the study revealed that there is a good relationship between satellite based indices – Normalized Difference Vegetation Index (NDVI), Normalized Difference Infrared Index (NDII), and Disease Water Stress Index (DSWI) – and ground based LAI, but the strength of this relationship depends on the phase of crop development. It was also found that the accuracy of LAI determination with the use of the vegetation index derived from Sentinel-2 and Proba-V data is similar when applying the image acquisition at the proper date – the heading stage for winter wheat.
PL
Głównym celem prezentowanej pracy jest ocena możliwości wykorzystania obrazów satelitarnych nowej generacji dla określania wskaźnika pokrycia liśćmi LAI. W badaniach wykorzystano dwa typy danych satelitarnych: Sentinel-2 oraz Proba-V. Na ich podstawie wygenerowano różne wskaźniki roślinności i skorelowano je z wartościami LAI pomierzonymi w terenie. W wyniku przeprowadzonych analiz stwierdzono dobre zależności pomiędzy wskaźnikami NDVI, NDII i DSWI a naziemnymi wartościami LAI; okazało się również, że siła tych zależności zależy od fazy rozwojowej roślin. Stwierdzono także, że dokładność wyznaczania wartości LAI za pomocą wskaźników roślinnych generowanych na podstawie obrazów satelitarnych Sentinel-2 oraz Proba-V jest zbliżona, pod warunkiem wykorzystania danych z odpowiedniego okresu wegetacji roślin – fazy kłoszenia dla pszenicy ozimej.
Rocznik
Strony
15--26
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Geodesy and Cartography, 27 Jacka Kaczmarskiego St., 02-679, Warsaw, Poland, Tel.: +48 22 3291977, Fax: +48 22 3291950
  • Institute of Geodesy and Cartography, 27 Jacka Kaczmarskiego St., 02-679, Warsaw, Poland, Tel.: +48 22 3291974, Fax: +48 22 3291950
autor
  • University of Warsaw, Faculty of Geography and Regional Studies, Department of Geoinformatics, Cartography and Remote Sensing, 30 Krakowskie Przedmieście St., 00-927 Warsaw, Poland; Institute of Geodesy and Cartography, 27 Jacka Kaczmarskiego St., 02-679, Warsaw, Poland, Tel.: +48 22 3291978, Fax: +48 22 3291950
autor
  • ESA-ESRIN, Via Galileo Galilei, 00044 Frascati, Italy, Tel: +39 06 941-88387
autor
  • University of Warsaw, Faculty of Geography and Regional Studies, Department of Geoinformatics, Cartography and Remote Sensing; Institute of Geodesy and Cartography, 27 Jacka Kaczmarskiego St., 02-679, Warsaw, Poland, Tel.: +48 22 3291978, Fax: +48 22 3291950
  • Institute of Geodesy and Cartography, 27 Jacka Kaczmarskiego St., 02-679, Warsaw, Poland, Tel.: +48 22 3291989, Fax: +48 22 3291950
Bibliografia
  • [1] Aboelghar M., Arafat S., Saleh A., Naeem S., Shirbeny M., Belal A., (2013): Retrieving Leaf Area Index from SPOT4 satellite data, The Egyptian Journal of Remote Sensing and Space Science (2010), 13, pp. 121–127.
  • [2] Baret F., Hagolle O., Geiger B., Bicheron P., Miras B. et al. (2007): LAI, fAPAR and fCOVER CYCLOPES global products derived from VEGETATION Part 1: Principles of the algorithm, Remote Sensing of Environment, 2007, 110, pp. 275–286.
  • [3] Berterretche M., Hudak A.T., Cohen W.B., Maierspenger T.K., Gower S.T., Dungan J., (2005): Comparison of regression and geostatistical methods for mapping Leaf Area Index with Landsat ETM+ data over a boreal forest, Remote Sensing of Environment, 96, pp. 49–61.
  • [4] Broge N.H., Leblanc E., (2000): Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density, Remote Sensing of Environment, Vol. 76, Issue 2, pp. 156–172.
  • [5] Carlson T.N., Ripley D.A., (1997): On the relationbetween NDVI, Fractional Vegetation Cover and Leaf Area Index, Remote Sensing of Environment 62; pp. 241–252.
  • [6] Dabrowska-Zielinska K., Kogan F., Ciolkosz A., Gruszczynska M., Kowalik W., (2002): Modelling crop growth conditions and crop yield in Poland using AVHRR-based indices, International Journal of Remote Sensing, Vol. 23, No. 6, pp. 1109–1123.
  • [7] Diercks W., Stercks S., Benhadj I., Livens S., Duhoux G., Van Achteren T., Francois M., Mellab K., Saint G., (2014): PROBA_V mission for global vegetation monitoring: standard products and image quality, International Journal of Remote Sensing, Vol. 35, Issue 7, pp. 2589–2614.
  • [8] Drusch M., Del Bello U., Carkier S., Colin O., Gascon F.F., Hoersch B., Isola C., Laberinti P., Martimort P., Meygret A., Spoto F., Sy O., Marchese F., Bargellini P., (2012): Sentinel-2 ESA’s optical high-resolution mission for GMES operational services, Remote Sensing of Environment, Vol. 120, 15 May 2012, pp. 25–36.
  • [9] Frampton W.J. Dash J., Watmough G., Milton E.J., (2013): Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation, ISPRS Journal of Photogrammetry and Remote Sensing 82 (2013), pp. 83–92.
  • [10] Friedl M.A., Schimel D.S., Michaelsen J., Davis F.W., Walker H., (1994): Estimating grassland biomass and Leaf Area Index using ground and satellite data, International Journal of Remote Sensing, Vol. 15, Issue 7, pp. 1401–1420.
  • [11] Galvão L.S., Formaggio A.R., Tisot D.A., (2005): Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data, Remote Sensing of Environment, Vol. 94, Issue 4, pp. 523–534.
  • [12] GCOS, (2011): Systematic observation requirements for satellite-based products for climate, 2011 update, WMO GCOS Rep. 154, 127 pp.
  • [13] Hardinsky M.A.; Lemas V., (1983): The influence of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alternifolia canopies, Photogrammetric Engineering and Remote Sensing, Vol. 49, pp.77–83.
  • [14] Heiskanen J., (2006): Estimating above ground tree biomass and Leaf Area Index in a mountain birch forest using satellite data, International Journal of remote Sensing, Vol. 27, No. 6, March 2006, pp. 1135–1158.
  • [15] Huete A.R., (1988): A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, vol. 25, issue 3, pp. 259–309. DOI: 10.1016/0034-4257(88)90106-X.
  • [16] Huete A.R., Liu H., Batchily K., Leeuwen W., (1997): A comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sensing of Environment, No 59(3), pp. 440–451.
  • [17] Jiang J., Xiao Z., Wang J., Song J., (2016): Multiscale estimation of Leaf Area Index from satellite observations based on an ensemble multiscale filter, Remote Sensing 2016, 8, pp. 229; doi:10.3390/rs8030229.
  • [18] Kowalik W., Dabrowska-Zielinska K., Meroni M., Raczka T.U., de Wit A., (2014): Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries, International Journal of Applied Earth Observation and Geoinformation 32, pp. 228–239.
  • [19] Lee K.S., Cohen W.B., Kennedy R.E., Maiersperger T.K., Gower S.T., (2004): Hyperspectral versus multispectral data for estimating Leaf Area Index in four different biomes, Remote Sensing of Environment 91, pp. 508–520.
  • [20] Li Z., Guo X., (2011): A suitable vegetation index for quantifying temporal variation of Leaf Area Index (LAI) in semiarid mixed grassland, Canadian Journal of Remote Sensing, 36, pp. 709–721.
  • [21] Price J.C., (1993): Estimating Leaf Area Index from satellite data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 31, Issue 3, May 1993, pp. 727–734.
  • [22] Roumenina E., Kazandijev V., Dimitrow P., Filchev L., Jelev G., (2013): Validation of LAI and assessment of winter wheat status using spectral data and vegetation indices from SPOT VEGETATION and simulated PROBA-V images, International Journal of Remote Sensing, Vol. 34, Issue 8, pp. 2888–2904, doi.org/10.1080/01431161.2012.755276.
  • [23] Rousse J.W., Haas R.H., Schell J.A., Deering D.W., (1973): Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351 I, pp. 309–317.
  • [24] Shen L., Li Z., Guo X., (2014): Remote Sensing of Leaf Area Index (LAI) and a spatiotemporally parameterized model for mixed grasslands, International Journal of Applied Science and Technology, Vol. 4, No. 1, January 2014, pp. 46–61.
  • [25] Song X., Ciu B., Yang G., Feng H., (2014): Comparison of winter wheat growth with multi-temporal remote sensing imagery. 35th International Symposium on Remote Sensing of Environment, IOP Conf. Series: Earth and Environmental Science 17 (2014) 012044 doi:10.1088/1755-1315/17/1/012044.
  • [26] Turner D.P.; Cohen W.B.; Kennedy R.E., Fassnacht K.S.; Briggs J.M., (1999): Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites, Remote Sensing of Environment 1999, 70, pp. 52–68.
  • [27] Zheng G., Moskal L.M., (2009): Retrieving Leaf Area Index (LAI) using remote sensing; theories, methods and sensors, Sensors 2009, 9, pp. 2719–2745.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3aab860b-0e72-4cd1-a01d-41f2c5577cc6
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