RESEARCH PAPER
Landsat and Sentinel-2 images as a tool for the effective estimation of winter
and spring cultivar growth and yield prediction in the Czech Republic
1
Department of Machinery Utilization, Faculty of Engineering, Czech University of Life Sciences, Kamýcká 129, 165 21 Prague 6 – Suchdol, Czech Republic
2
Department of Agricultural Machines, Faculty of Engineering, Czech University of Life Sciences, Kamýcká 129, 165 21 Prague 6 – Suchdol, Czech Republic
3
Department of Mathematics, Faculty of Engineering, Czech University of Life Sciences, Kamýcká 129, 165 21 Prague 6 – Suchdol, Czech Republic
4
Division of Crop Management Systems, Crop Research Institute, Drnovská 507, 161 00 Prague 6 – Ruzyně, Czech Republic
Final revision date: 2020-08-14
Acceptance date: 2020-08-20
Publication date: 2020-09-28
Corresponding author
Zdeněk Jelínek
Department of Machinery Utilization, Czech University of Life Sciences, Kamýcká 129, 16521, Prague, Czech Republic
Department of Machinery Utilization, Czech University of Life Sciences, Kamýcká 129, 16521, Prague, Czech Republic
Int. Agrophys. 2020, 34(3): 391-406
KEYWORDS
TOPICS
ABSTRACT
The influence of climate and topography on crop condition and yield estimates is most effectively monitored by non-invasive satellite imagery. This paper evaluates the efficiency of free-access Sentinel 2 and Landsat 5, 7 and 8 satellite images scanned by different sensors on wheat growth and yield prediction. Five winter and spring wheat cultivars were grown between 2005 and 2017 in a relatively small 11.5 ha field with a 6% slope. The normalized difference vegetation index was derived from the satellite images acquired for later growth phases of the wheat crops (Biologische Bundesanstalt, Bundessorenamt and Chemical industry 55 – 70) and then compared with the topography wetness index, crop yields and yield frequency maps. The results showed a better correlation of data obtained over one day (R2 = 0.876) than data with a one-day delay (R2 = 0.689) using the Sentinel 2 B8 band instead of the B8A band for the near-infrared part of electromagnetic spectrum in the normalized difference vegetation index calculation.
REFERENCES (58)
1.
Agrometeorological station, 2018. Crop Research Institute Prague. https://www.vurv.cz/meteo/mete....
2.
Andarzian B., Bakhshandeh A.M., Bannayan M., Emam Y., Fathib G., and Alami Saeedb K., 2008. WheatPot: A simple model for spring wheat yield potential using monthly weather data. Biosys. Eng., 99, 487-495. doi: 10.1016/j.biosystemseng.2007.12.008.
3.
Benedetti R. and Rossini P., 1993. On the use of NDVI profiles as a tool for agricultural statistics. The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing Environ., 45, 311-326. doi: 10.1016/0034-4257(93)90113-C.
4.
Cattani C.E.V., Garcia M.R., Mercante E., Johann J.A., Correa M.M., and Oldoni L.V., 2017. Spectral-temporal characterization of wheat cultivars through NDVI obtained by terrestrial sensors. Revista Brasileira de Engenharia Agrícola e Ambiental, 21, 769-773.
5.
Chemura A., Mutanga O., Odindi J., and Kutywayo D., 2018. Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data. ISPRS. J. Photogrammetry Remote Sensing, 138, 1-11. doi: 10.1016/j.isprsjprs.2018.02.004.
6.
Clevers J.G.P.W., Kooistra L., and van den Brande M.M.M, 2017. Using Sentinel-2 data for retrieving LAI and leaf canopy chlorophyll content of a potato crop. Remote Sensing, 9: 405. doi: 10.3390/rs9050405.
8.
Doerge T.A., 1999. Yield map interpretation. J. Production Agric., 12, 54-61. doi: 10.2134/jpa1999.0054.
9.
Domínguez J.A., Kumhálová J., and Novák P., 2015. Winter oilseed rape and winter wheat growth prediction using remote sensing methods. Plant Soil Environ., 61, 410-416. doi: 10.17221/412/2015-PSE.
10.
Domínguez J.A., Kumhálová J., and Novák P., 2017. Assessment of the relationship between spectral indices from satellite remote sensing and winter oilseed rape yield. Agronomy Res., 15, 55-68.
11.
Du M. and Noguchi N., 2017. Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sensing, 9, 289. doi: 10.3390/rs9030289.
12.
Ehleringer J., and Forseth I., 1980. Solar tracking by plants. Science, 210, 1094. doi: 10.1126/science.210.4474.1094.
13.
Evans L.T., 1993. Crop Evolution, Adaptation and Yield. Cambridge University Press, Cambridge, Great Britain: 500.
14.
Flynn K.C., Frazier A.E., and Admas S., 2020. Performance of chlorophyll prediction indices for Eragrostis tef at Sentinel‑2 MSI and Landsat‑8 OLI spectral resolutions. Precision Agriculture, doi: 10.1007/s11119-020-09708-4.
15.
Gili A., Álvarez C., Bagnato R., and Noellemeyer E., 2017. Comparison of three methods for delineating management zones for site-specific crop management. Computers and Electronics Agric., 139, 213-223. doi: 10.1016/j.compag.2017.05.022.
16.
Goméz C., White J.C., and Wulder M.A., 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogrammetry Remote Sensing, 116, 55-72. doi: 10.1016/j.isprsjprs.2016.03.008.
17.
Grassini P., van Bussel L.G.J., van Wart J., Wolf J., Claessens L., Yang H., Boogaard H., de Groot H., van Ittersum M.K., and Cassman K.G., 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res., 117, 49-63. doi: 10.1016/j.fcr.2015.03.004.
18.
Grohs D.S., Bredemeier C., Mundstock C.M., and Poletto N., 2009. Modelo para estimativa do potencial produtivo em trigo e cevada por meio do sensor GreenSeeker. Engenharia Agrícola, 29, 101-112. doi: 10.1590/S0100-69162009000100011.
19.
Guilpart N., Grassini P., Sadras V.O., Timsina J., and Cassman K.G., 2017. Estimating yield gaps at the cropping system level. Field Crops Res., 206, 21-32. doi: 10.1016/j.fcr.2017.02.008.
20.
Heumann B.W., Seaquist J.W., Eklundh L., and Jönsson P., 2007. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005. Remote Sensing Environ., 108: 385-392. doi: 10.1016/j.rse.2006.11.025.
21.
Jackson R.D. and Ezra C.E., 1985. Spectral response of cotton to suddenly induced water stress. Int. J. Remote Sensing, 6, 177-185. doi: 10.1080/01431168508948433.
22.
Jamali S., Jönsson P., Eklundh L., Ardö J., and Seaquist J., 2015. Detecting changes in vegetation trends using time series segmentation. Remote Sensing Environ., 156, 182-195. doi: 10.1016/j.rse.2014.09.010.
23.
Jelínek Z., Starý K., and Kumhálová J., 2019. Assessment of production zones modelling accuracy based on satellite imaging and yield measurement of selected agriculture plot. Agronomy Res., 17(2), 447-455. doi: 10.15159/AR.19.102.
24.
Jin Z., Azzari G., Burke M., Aston S., and Lobell D.B., 2017. Mapping smallholder yield heterogeneity at multiple scales in Eastern Africa. Remote Sensing, 9, 931. doi: 10.3390/rs9090931.
25.
Julien Y., Sobrino J.A., and Jiménez-Muñoz J.C., 2011. Land use classification from multitemporal Landsat imagery using the yearly land cover dynamics (YLCD) method. Int. J. Applied Earth Observation and Geoinformation, 13, 711-720. doi: 10.1016/j.jag.2011.05.008.
26.
Kumhálová J., Kumhála F., Kroulík M., and Matějková Š., 2011. The impact of topography on soil properties and yield and the effects of weather conditions. Precision Agric., 12, 813-830. doi: 10.1007/s11119-011-9221-x.
27.
Kumhálová J. and Matějková Š., 2017. Yield variability prediction by the remote sensing sensors with different spatial resolution. Int. Agrophys., 31, 195-202. doi: 10.1515/intag-2016-0046.
28.
Kumhálová J., Matějková Š., Fifernová M., Lipavský J., and Kumhála F., 2008. Topography impact on nutrition content in soil and yield. Plant, Soil Environ., 54, 255-261. doi: 10.17221/257-PSE.
29.
Kumhálová J. and Moudrý V., 2014. Topographical characteristics for precision agriculture in conditions of the Czech Republic. Appl. Geography, 50, 90-98. doi: 10.1016/j.apgeog.2014.02.012.
30.
Kumhálová J., Zemek F., Novák P., Brovkina O., and Mayerová M., 2014. Use of Landsat images for yield evaluation within a small plot. Plant Soil Environ., 60, 501-506. doi: 10.17221/515/2014-PSE.
32.
Long D.S. and McCallum J.D., 2014. On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agric., 16, 492-504. doi: 10.5307/JBE.2016.41.4.408.
33.
Mandanici E., and Bitelli G., 2016. Preliminary comparison of Sentinel 2 and Landsat 8 imagery for a combined use. Remote Sensing, 8, 1014. doi: 10.3390/rs8121014.
34.
Maphanyane J.G., Mapeo R.B.M., and Akinola M.O., 2018. Handbook of research on geospatial science and technologies . IGI Global, Hershey, PA, USA: 457.
35.
Matějková Š., Kumhálová J., and Lipavský F., 2010. Evaluation of crop yield under different nitrogen doses of mineral fertilization. Plant, Soil Environ., 56, 163-167. doi: 10.17221/196/2009-PSE.
36.
Mueller N.D., Gerber J.S., Johnston M., and Ray D.K., Ramankutty N., and Foley J.A., 2013. Closing yield gaps through nutrient and water management. Nature, 490, 254-257. doi: 10.1038/nature11420.
37.
Nearing G.S., Crow W.T., Thorp K.R., and Moran M.S., Reichle R.H., and Gupta H.V., 2010. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experi- ment. Water Res. Res., 48, W05525. doi: 10.1029/2011WR011420.
38.
Olesen J.E., Trnka M., Kersebaum K.C., Skjelvåg A.O., Seguin B., Peltonen-Sainio P., Rossi F., Kozyra J., and Micale F., 2011. Impacts and adaption of European crop production systems to climate change. Eur. J. Agron., 34, 96-112. doi: 10.1016/j.eja.2010.11.003.
39.
Povh F.P., Molin J.P., Gimenez L.M., Pauletti V., Molin R., and Salvi J.V., 2008. Properties of NDVI obtained by an active optical sensor in cereals. Pesquisa Agropecuária Brasileira, 43, 1075-1083. doi: 10.1590/S0100-204X2008000800018.
40.
Rezaei E.E., Siebert S., Hüging H., and Ewert F., 2018. Climate change effect on wheat phenology depends on cultivar change. Scientific Reports, 8, 4891. doi: 10.1038/s41598-018-23101-2.
41.
Schmidt F. and Persson A., 2003. Comparison of DEM data capture and topographic wetness indices. Precision Agric., 4, 179-192. doi: 10.1023/A:1024509322709.
42.
Scudiero E., Corwin D.L., Wienhold B.J., Bosley B., Shanahan J.F., and Johnson C.K., 2016. Downscaling Landsat 7 canopy reflectance employing a multi-soil sensor platform. Precision Agric., 17, 53-73. doi: 10.1007/s11119-015-9406-9.
43.
Scudiero E., Skaggs T.H., and Corwin D.L.C., 2014. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional, 2-3, 82-90. doi: doi.org/10.1016/j.geodrs.2014.10.004.
45.
Shanahan J.F., Schepers J.S., Francis D.D., Varvel G.E., Wilhelm W.W., and Tringe J.M., 2001. Use of remote-sensing imagery to estimate corn grain yield. Agronomy J., 93, 583-589. doi: 10.2134/agronj2001.933583x.
46.
Sørensen R., Zinko U., and Seibert J., 2006. On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology Earth System Sci., 10, 101-112. doi: 10.5194/hess-10-101-2006.
48.
Šíp V., Chrpová J., Žofajová A., and Milec Z., 2011. Evidence of selective changes in winter wheat in middle-European environments reflected by allelic diversity at loci affecting plant height and photoperiodic response. J. Agric. Sci., 149, 313-326. doi: 10.1017/S002185961000078X.
49.
Thorp K.R., Hunsaker D.J., and French A.N., 2001. Assimilating leaf area index estimates from remote sensing into the simulations of a cropping systems model. Trans. ASABE, 53, 251-262. doi: 10.3390/w10101367.
51.
Van Leeuwen W.J.D. and Huete A.R., 1996. Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices. Remote Sensing Environ., 55, 123-138. doi: 10.1016/0034-4257(95)00198-0.
52.
Vincini M., Calegari F., and Casa R., 2016. Sensitivity of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution for different canopy structures. Precision Agric., 17, 313-331. doi: 10.1007/s11119-015-9424-7.
53.
Viña A., Gitelson A.A., Nguy-Robertson A.L., and Peng Y., 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing Environ., 115, 3468-3478. doi: 10.1016/j.rse.2011.08.010.
54.
Wheeler T.R., Hong T.D., Ellis R.H., Batts G.R., Morison J.I.L., and Hadley P., 1996. The duration and rate of grain growth, and harvest index, of wheat (Triticum aestivum L.) in response to temperature and CO2. J. Experimental Botany, 47, 623-630. doi: 10.1093/jxb/47.5.623.
55.
Winter wheat cultivars Baletka and Brilliant, 2018. http://eagri.cz/public/web/fil... 404470/Psenice_ozima_2015.pdf.
57.
Wu Q., Wang C., Fang J.J., and Ji J.W., 2016. Field monitoring of wheat seedling stage with hyperspectral imaging. Int. J. Agric. Biological Eng., 9, 143-148. doi: 10.3965/j.ijabe.20160905.1707.
58.
Zhang F., Wu B., and Luo Z., 2004. Winter wheat yield predicting for America using remote sensing data. J. Remote Sensing, 8, 611-617. doi: 10.1371/journal.pone.0070816.
Share
RELATED ARTICLE
| eISSN: | 2300-8725 |
| ISSN: | 0236-8722 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
