为什么光栅提取会产生暗淡的误差?

我试图从光栅砖中提取值并得到平均值,但得到的错误我认为与光栅砖的尺寸有关。

NOAA

library(raster)

ERSST <- rotate(brick('sst.mnmean.nc'))
ERSST
class       : RasterBrick 
dimensions  : 89, 180, 16020, 1976  (nrow, ncol, ncell, nlayers)
resolution  : 2, 2  (x, y)
extent      : -179, 181, -89, 89  (xmin, xmax, ymin, ymax) # ignore extent, needs fixing but not relevant for the question
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : X1854.01.01, X1854.02.01, X1854.03.01, X1854.04.01, X1854.05.01, X1854.06.01, X1854.07.01, X1854.08.01, X1854.09.01, X1854.10.01, X1854.11.01, X1854.12.01, X1855.01.01, X1855.02.01, X1855.03.01, ... 
min values  :        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8,        -1.8, ... 
max values  :    32.09937,    31.44189,    31.72137,    31.47466,    33.23633,    31.90788,    35.22922,    33.66898,    32.26702,    32.15502,    31.68270,    31.74512,    31.32458,    31.23049,    29.88974, ... 
Date        : 1854-01-01, 2018-08-01 (min, max)

任意点

xy <- data.frame(x = -49, y = 45)

extract(ERSST, xy, buffer = 1e+05, small = TRUE, fun = mean)
Error in apply(x, 2, fun2) : dim(X) must have a positive length

我之所以认为这是一个维度问题,是因为我在尝试指定要使用的层时遇到了错误

extract(ERSST, xy, buffer = 1e+05, small = TRUE, layer = 10, nl = 10)
Error in x[, lyrs] : incorrect number of dimensions

mERSST <- mean(ERSST)
extract(mERSST, xy, buffer = 1e+05, small = TRUE, fun = mean)
[1] 5.649212

@RobertHijmans的答案让我意识到,我总是从extract中只得到一个值,即使点位于几个网格单元的连接处,如上面的示例所示。

plot(MSST, xlim = c(-60, -40), ylim = c(40, 50))
points(xy)

extract(mERSST, xy, buffer = 1e+05, small = TRUE, cellnumbers = TRUE)
[[1]]
       cell       value 
3845.000000    5.649212 

我只得到一个值,而我希望有4个,不管缓冲区有多小。我是不是遗漏了什么?所以我试着把我的点转换成一个圆,然后用它来提取数据

coordinates(xy) <- ~ x + y
proj4string(xy) <- '+init=epsg:4326'

xy_utm <- spTransform(xy, CRS('+init=epsg:32621'))
gbf_utm <- rgeos::gBuffer(xy_utm, width = 1e5, quadsegs = 250L)
gbf <- spTransform(gbf_utm, CRS(proj4string(xy)))


plot(ERSST[[1]], xlim = c(-60, -40), ylim = c(40, 50))
points(xy, pch = 19)
plot(gbf, add = TRUE)

extract(ERSST[[1]], gbf, small = TRUE, weights = TRUE)

[[1]]
        value weight
[1,] 1.722664   0.25
[2,] 3.683457   0.25
[3,] 5.985203   0.25
[4,] 8.442450   0.25

在版本2.6.7中(这似乎是有道理的)。

但是

[[1]]
          value      weight
  [1,] 1.722664 0.001236928
  [2,] 1.722664 0.003935680
  [3,] 1.722664 0.005285056
  [4,] 3.683457 0.005285056
  [5,] 3.683457 0.003935680
  [6,] 3.683457 0.001236928
  [7,] 1.722664 0.002136512
  [8,] 1.722664 0.008321151
  [9,] 1.722664 0.011244799
 [10,] 1.722664 0.011244799
 [11,] 1.722664 0.011244799
 [12,] 3.683457 0.011244799
 [13,] 3.683457 0.011244799
 [14,] 3.683457 0.011244799
 [15,] 3.683457 0.008208703
 [16,] 3.683457 0.001911616
 [17,] 1.722664 0.003036096
 [18,] 1.722664 0.010907455
 [19,] 1.722664 0.011244799
 [20,] 1.722664 0.011244799
 [21,] 1.722664 0.011244799
 [22,] 1.722664 0.011244799
 [23,] 3.683457 0.011244799
 [24,] 3.683457 0.011244799
 [25,] 3.683457 0.011244799
 [26,] 3.683457 0.011244799
 [27,] 3.683457 0.010907455
 [28,] 3.683457 0.003036096
 [29,] 1.722664 0.000449792
 [30,] 1.722664 0.010232767
 [31,] 1.722664 0.011244799
 [32,] 1.722664 0.011244799
 [33,] 1.722664 0.011244799
 [34,] 1.722664 0.011244799
 [35,] 1.722664 0.011244799
 [36,] 3.683457 0.011244799
 [37,] 3.683457 0.011244799
 [38,] 3.683457 0.011244799
 [39,] 3.683457 0.011244799
 [40,] 3.683457 0.011244799
 [41,] 3.683457 0.010232767
 [42,] 3.683457 0.000337344
 [43,] 1.722664 0.003036096
 [44,] 1.722664 0.011244799
 [45,] 1.722664 0.011244799
 [46,] 1.722664 0.011244799
 [47,] 1.722664 0.011244799
 [48,] 1.722664 0.011244799
 [49,] 1.722664 0.011244799
 [50,] 3.683457 0.011244799
 [51,] 3.683457 0.011244799
 [52,] 3.683457 0.011244799
 [53,] 3.683457 0.011244799
 [54,] 3.683457 0.011244799
 [55,] 3.683457 0.011244799
 [56,] 3.683457 0.002923648
 [57,] 5.985203 0.002923648
 [58,] 5.985203 0.011244799
 [59,] 5.985203 0.011244799
 [60,] 5.985203 0.011244799
 [61,] 5.985203 0.011244799
 [62,] 5.985203 0.011244799
 [63,] 5.985203 0.011244799
 [64,] 8.442450 0.011244799
 [65,] 8.442450 0.011244799
 [66,] 8.442450 0.011244799
 [67,] 8.442450 0.011244799
 [68,] 8.442450 0.011244799
 [69,] 8.442450 0.011244799
 [70,] 8.442450 0.002923648
 [71,] 5.985203 0.000337344
 [72,] 5.985203 0.010120319
 [73,] 5.985203 0.011244799
 [74,] 5.985203 0.011244799
 [75,] 5.985203 0.011244799
 [76,] 5.985203 0.011244799
 [77,] 5.985203 0.011244799
 [78,] 8.442450 0.011244799
 [79,] 8.442450 0.011244799
 [80,] 8.442450 0.011244799
 [81,] 8.442450 0.011244799
 [82,] 8.442450 0.011244799
 [83,] 8.442450 0.010007871
 [84,] 8.442450 0.000224896
 [85,] 5.985203 0.002811200
 [86,] 5.985203 0.010795007
 [87,] 5.985203 0.011244799
 [88,] 5.985203 0.011244799
 [89,] 5.985203 0.011244799
 [90,] 5.985203 0.011244799
 [91,] 8.442450 0.011244799
 [92,] 8.442450 0.011244799
 [93,] 8.442450 0.011244799
 [94,] 8.442450 0.011244799
 [95,] 8.442450 0.010682559
 [96,] 8.442450 0.002698752
 [97,] 5.985203 0.001799168
 [98,] 5.985203 0.007871359
 [99,] 5.985203 0.011244799
[100,] 5.985203 0.011244799
[101,] 5.985203 0.011244799
[102,] 8.442450 0.011244799
[103,] 8.442450 0.011244799
[104,] 8.442450 0.011244799
[105,] 8.442450 0.007871359
[106,] 8.442450 0.001799168
[107,] 5.985203 0.001236928
[108,] 5.985203 0.003935680
[109,] 5.985203 0.005285056
[110,] 8.442450 0.005285056
[111,] 8.442450 0.003935680
[112,] 8.442450 0.001236928

在版本2.7-13中,这不可能是正确的。