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I am faced with the task, using the data of Sentinel-2 channels, to build a classifier based on time series (time series cassification), where the dependent variable is the wheat class (1) or another crop (0). I am using R language. For greater clarity, I will give a small example of data.

 exampl=structure(list(date = c("19.12.2020", "24.12.2020", "03.01.2021", 
    "08.01.2021", "13.01.2021", "18.01.2021", "23.01.2021", "28.01.2021", 
    "02.02.2021", "07.02.2021", "17.02.2021", "22.02.2021", "27.02.2021", 
    "04.03.2021", "09.03.2021", "14.03.2021", "24.03.2021", "29.03.2021", 
    "03.04.2021", "13.04.2021", "18.04.2021", "28.04.2021", "08.05.2021", 
    "01.10.2020", "06.10.2020", "11.10.2020", "16.10.2020", "21.10.2020", 
    "31.10.2020", "05.11.2020", "10.11.2020", "20.11.2020", "30.11.2020", 
    "05.12.2020", "10.12.2020", "20.12.2020", "25.12.2020", "04.01.2021", 
    "29.01.2021", "18.02.2021", "23.02.2021", "28.02.2021", "05.03.2021", 
    "10.03.2021", "15.03.2021", "20.03.2021", "30.03.2021", "04.04.2021", 
    "09.04.2021", "19.04.2021", "29.04.2021", "09.05.2021", "14.05.2021", 
    "01.10.2020"), red = c(1103L, 1084L, 1504L, 1259L, 1230L, 1393L, 
    1225L, 1482L, 1386L, 1316L, 1400L, 1418L, 1546L, 1540L, 1644L, 
    1568L, 1682L, 1828L, 1887L, 1992L, 2024L, 1965L, 1915L, 1600L, 
    1360L, 1520L, 1360L, 1528L, 1600L, 1634L, 1508L, 1380L, 1548L, 
    1456L, 1460L, 1732L, 1080L, 1008L, 1068L, 784L, 580L, 920L, 578L, 
    1016L, 1296L, 1184L, 1374L, 1224L, 1576L, 2094L, 1932L, 1799L, 
    1738L, 248L), green = c(964L, 940L, 1392L, 1054L, 999L, 1244L, 
    964L, 1350L, 1043L, 1044L, 1064L, 1045L, 1170L, 1148L, 1212L, 
    1176L, 1290L, 1404L, 1410L, 1485L, 1501L, 1496L, 1489L, 1392L, 
    1056L, 1248L, 1120L, 1160L, 1296L, 1426L, 1388L, 1172L, 1372L, 
    1352L, 1252L, 1524L, 936L, 1008L, 1092L, 640L, 660L, 884L, 602L, 
    808L, 1256L, 912L, 1162L, 952L, 1208L, 1598L, 1560L, 1495L, 1474L, 
    392L), blue = c(737L, 668L, 864L, 838L, 672L, 1060L, 628L, 1166L, 
    678L, 772L, 792L, 639L, 802L, 756L, 844L, 776L, 930L, 1044L, 
    986L, 1084L, 1084L, 1153L, 1117L, 960L, 712L, 816L, 784L, 664L, 
    960L, 1226L, 1204L, 932L, 1068L, 1200L, 1044L, 1292L, 728L, 832L, 
    860L, 272L, 420L, 644L, 362L, 408L, 1072L, 480L, 826L, 680L, 
    840L, 1166L, 1268L, 1275L, 1138L, 152L), nir = c(2488L, 2262L, 
    2120L, 2516L, 2504L, 2566L, 2414L, 2556L, 2568L, 2621L, 2616L, 
    2584L, 2744L, 2743L, 2840L, 2772L, 2676L, 2865L, 2952L, 3077L, 
    3051L, 3005L, 2890L, 3096L, 2696L, 2904L, 2680L, 2960L, 2392L, 
    2120L, 2020L, 2232L, 2088L, 1928L, 2072L, 2488L, 1912L, 1928L, 
    2296L, 2472L, 2696L, 2792L, 3000L, 2568L, 2740L, 2504L, 2456L, 
    2296L, 2584L, 2976L, 2624L, 2568L, 2696L, 2632L), swir = c(2976L, 
    2885L, 2720L, 3216L, 3104L, 3104L, 3020L, 3120L, 3104L, 3337L, 
    3488L, 3232L, 3603L, 3552L, 3808L, 3614L, 3760L, 3856L, 3859L, 
    3982L, 4172L, 4076L, 4240L, 3040L, 3104L, 3040L, 3040L, 3232L, 
    2848L, 2656L, 2256L, 2656L, 2656L, 2464L, 2592L, 2464L, 2080L, 
    2144L, 2080L, 2016L, 1744L, 2080L, 1888L, 2144L, 2128L, 2256L, 
    2400L, 2464L, 2848L, 3520L, 3280L, 3304L, 3168L, 1376L), B05 = c(1376L, 
    1440L, 1712L, 1568L, 1504L, 1712L, 1554L, 1773L, 1632L, 1632L, 
    1696L, 1632L, 1862L, 1888L, 2000L, 1923L, 2067L, 2189L, 2160L, 
    2322L, 2347L, 2327L, 2287L, 2016L, 1696L, 1952L, 1824L, 1952L, 
    1888L, 1824L, 1712L, 1696L, 1760L, 1648L, 1696L, 1952L, 1376L, 
    1376L, 1424L, 1184L, 1056L, 1296L, 1056L, 1376L, 1632L, 1568L, 
    1648L, 1632L, 1888L, 2520L, 2216L, 2080L, 2128L, 608L), B06 = c(1952L, 
    1952L, 1968L, 2050L, 1952L, 2208L, 2062L, 2224L, 2075L, 2081L, 
    2208L, 2208L, 2250L, 2283L, 2462L, 2336L, 2318L, 2448L, 2470L, 
    2574L, 2600L, 2551L, 2519L, 2656L, 2272L, 2512L, 2400L, 2528L, 
    2208L, 2016L, 1872L, 1952L, 1952L, 1824L, 1888L, 2160L, 1696L, 
    1888L, 2016L, 1952L, 2096L, 2272L, 2208L, 2144L, 2352L, 2144L, 
    2144L, 2016L, 2208L, 2688L, 2416L, 2352L, 2464L, 2016L), B07 = c(2185L, 
    2123L, 2080L, 2314L, 2272L, 2400L, 2144L, 2452L, 2208L, 2395L, 
    2400L, 2330L, 2466L, 2400L, 2644L, 2541L, 2525L, 2736L, 2732L, 
    2838L, 2860L, 2762L, 2835L, 2976L, 2528L, 2784L, 2528L, 2784L, 
    2272L, 2080L, 2016L, 2016L, 2016L, 2016L, 2016L, 2320L, 1824L, 
    1952L, 2128L, 2272L, 2576L, 2656L, 2848L, 2464L, 2704L, 2464L, 
    2320L, 2144L, 2336L, 2912L, 2528L, 2480L, 2736L, 2592L), B08A = c(2455L, 
    2336L, 2272L, 2540L, 2524L, 2592L, 2388L, 2604L, 2588L, 2648L, 
    2699L, 2656L, 2833L, 2720L, 2953L, 2902L, 2800L, 2981L, 2992L, 
    3147L, 3167L, 3096L, 3108L, 3232L, 2848L, 2976L, 2848L, 2976L, 
    2464L, 2208L, 2080L, 2256L, 2144L, 2016L, 2128L, 2464L, 1952L, 
    2080L, 2192L, 2464L, 2656L, 2784L, 2976L, 2656L, 2784L, 2528L, 
    2464L, 2400L, 2592L, 3104L, 2784L, 2720L, 2784L, 2720L), B12 = c(2336L, 
    2144L, 2096L, 2592L, 2392L, 2528L, 2336L, 2576L, 2448L, 2656L, 
    2845L, 2512L, 2947L, 2842L, 3087L, 2784L, 2976L, 3232L, 3104L, 
    3253L, 3476L, 3412L, 3612L, 2400L, 2400L, 2400L, 2528L, 2496L, 
    2400L, 2336L, 1744L, 2144L, 2336L, 2224L, 2272L, 2144L, 1760L, 
    1616L, 1632L, 1552L, 1312L, 1568L, 1376L, 1696L, 1680L, 1760L, 
    1888L, 1888L, 2336L, 2976L, 2856L, 2784L, 2664L, 800L), wheat = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L)), class = "data.frame", row.names = c(NA, 
    -54L))

I am interested in the methodological aspect of solving the problem. What is the most correct time series classification algorithm i should use in the context of geodata for field crops. The matter is that it is necessary to use all independent variables (covariates) for forecasting of a class of crops.

Does anyone have experience with this kind of data. Or is there somewhere an illustrative example of an algorithm in R where shown how best to make a classifier?

Thank you for your help.

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  • $\begingroup$ I don't think there is a single "optimal" method. This search may be helpful. One approach would be to normalize your time series in some way, use Dynamic Time Warping if necessary, then do k-nearest neighbors, with an appropriate distance function between multiple time series. $\endgroup$ May 7, 2023 at 11:21

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