如何从 R 中的多个 Aqua Modis netCDF 文件中提取变量？
How to Extract Variables from Multiple Aqua Modis netCDF files in R?
问题：
我在 R 中有一个代码，它 从单个 Aqua Modis netCDF 文件（见下文）中提取每月海面温度 (SST) 值。但是，我在一个文件夹中有一批 59 个 Aqua Modis netCDF 文件。
目标：
我的目标是从所有 59 个 netCDF 文件 的每个 netCDF 文件中提取变量的经度、纬度和 SST，将它们转换使用函数 stack::raster() 转换为光栅文件，然后处理这些文件。
我的数据框有 650 行，它们是 海豚 ID。 我想提取 平均 SST 2016-2021时间段内的每个海豚 ID。一旦我提取了每个海豚 ID 的平均值 SST，我想将这些值写入一个 .csv 文件，文件名为 ' Average_SST'.
很遗憾，由于所有权问题，我无法共享我的数据。
我在空间分析方面完全是个新手，近 4 天来我一直在尝试通过观看 YouTube、阅读教程和 Stack 来解决这个难题溢出。我现在感到非常困惑，我相信我必须遍历所有文件才能实现我的 objective.
如果有人能帮忙，我先谢谢你了。
R-代码：
打开文件夹中的所有 59 个 Aqua Modis netCDF 文件
##Open packages needed for our analysis
library(ncdf4)
library(terra)
library('RNetCDF')
library(raster)
library(sp)
filenames = list.files('~/Documents/Ocean_ColorSST_2016_2021',pattern='*.nc',full.names=TRUE)
##Open the 70 Aqua Modis netCDF files from their folder
SST <- nc_open(filenames[59])
##Print the results for SST
print(SST)
结果：
3 variables (excluding dimension variables):
short sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Sea Surface Temperature
scale_factor: 0.00499999988824129
add_offset: 0
units: degree_C
standard_name: sea_surface_temperature
_FillValue: -32767
valid_min: -1000
valid_max: 10000
display_scale: linear
display_min: -2
display_max: 45
unsigned byte qual_sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Quality Levels, Sea Surface Temperature
_FillValue: 255
valid_min: 0
valid_max: 5
unsigned byte palette[eightbitcolor,rgb] (Contiguous storage)
4 dimensions:
lat Size:4320
long_name: Latitude
units: degrees_north
standard_name: latitude
_FillValue: -999
valid_min: -90
valid_max: 90
lon Size:8640
long_name: Longitude
units: degrees_east
standard_name: longitude
_FillValue: -999
valid_min: -180
valid_max: 180
rgb Size:3 (no dimvar)
eightbitcolor Size:256 (no dimvar)
[1] ">>>> WARNING <<< attribute data_bins is an 8-byte value, but R"
[1] "does not support this data type. I am returning a double precision"
[1] "floating point, but you must be aware that this could lose precision!"
61 global attributes:
product_name: AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc
instrument: MODIS
title: MODISA Level-3 Standard Mapped Image
project: Ocean Biology Processing Group (NASA/GSFC/OBPG)
platform: Aqua
temporal_range: month
processing_version: R2019.0
date_created: 2021-12-03T08:21:22.000Z
history: l3mapgen par=AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc.param
l2_flag_names: LAND,HISOLZEN
time_coverage_start: 2021-09-01T00:45:00.000Z
time_coverage_end: 2021-10-01T02:55:00.000Z
start_orbit_number: 102808
end_orbit_number: 103246
map_projection: Equidistant Cylindrical
latitude_units: degrees_north
longitude_units: degrees_east
northernmost_latitude: 90
southernmost_latitude: -90
westernmost_longitude: -180
easternmost_longitude: 180
geospatial_lat_max: 90
geospatial_lat_min: -90
geospatial_lon_max: 180
geospatial_lon_min: -180
latitude_step: 0.0416666679084301
longitude_step: 0.0416666679084301
sw_point_latitude: -89.9791641235352
sw_point_longitude: -179.97917175293
spatialResolution: 4.64 km
geospatial_lon_resolution: 0.0416666679084301
geospatial_lat_resolution: 0.0416666679084301
geospatial_lat_units: degrees_north
geospatial_lon_units: degrees_east
number_of_lines: 4320
number_of_columns: 8640
measure: Mean
suggested_image_scaling_minimum: -2
suggested_image_scaling_maximum: 45
suggested_image_scaling_type: LINEAR
suggested_image_scaling_applied: No
_lastModified: 2021-12-03T08:21:22.000Z
Conventions: CF-1.6 ACDD-1.3
institution: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group
standard_name_vocabulary: CF Standard Name Table v36
naming_authority: gov.nasa.gsfc.sci.oceandata
id: AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc/L3/AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc
license: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
creator_name: NASA/GSFC/OBPG
publisher_name: NASA/GSFC/OBPG
creator_email: data@oceancolor.gsfc.nasa.gov
publisher_email: data@oceancolor.gsfc.nasa.gov
creator_url: https://oceandata.sci.gsfc.nasa.gov
publisher_url: https://oceandata.sci.gsfc.nasa.gov
processing_level: L3 Mapped
cdm_data_type: grid
keywords: Earth Science > Oceans > Ocean Optics > Sea Surface Temperature
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords
data_bins: 20227868
data_minimum: -1.80000007152557
data_maximum: 40.0000038146973
> SST_brick <- brick(list[59], "sst")
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'brick': object of type 'builtin' is not subsettable
> ##Print the results for SST
> print(SST)
File /Users/kirstymedcalf/Documents/DMAD/Publication/DMAD_Maps_Analysis_Publication/Montenegro_Final_Analysis_Folders/Ocean_ColorSST_2016_2021/AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc (NC_FORMAT_NETCDF4):
3 variables (excluding dimension variables):
short sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Sea Surface Temperature
scale_factor: 0.00499999988824129
add_offset: 0
units: degree_C
standard_name: sea_surface_temperature
_FillValue: -32767
valid_min: -1000
valid_max: 10000
display_scale: linear
display_min: -2
display_max: 45
unsigned byte qual_sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Quality Levels, Sea Surface Temperature
_FillValue: 255
valid_min: 0
valid_max: 5
unsigned byte palette[eightbitcolor,rgb] (Contiguous storage)
4 dimensions:
lat Size:4320
long_name: Latitude
units: degrees_north
standard_name: latitude
_FillValue: -999
valid_min: -90
valid_max: 90
lon Size:8640
long_name: Longitude
units: degrees_east
standard_name: longitude
_FillValue: -999
valid_min: -180
valid_max: 180
rgb Size:3 (no dimvar)
eightbitcolor Size:256 (no dimvar)
[1] ">>>> WARNING <<< attribute data_bins is an 8-byte value, but R"
[1] "does not support this data type. I am returning a double precision"
[1] "floating point, but you must be aware that this could lose precision!"
61 global attributes:
product_name: AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc
instrument: MODIS
title: MODISA Level-3 Standard Mapped Image
project: Ocean Biology Processing Group (NASA/GSFC/OBPG)
platform: Aqua
temporal_range: month
processing_version: R2019.0
date_created: 2021-12-03T08:21:22.000Z
history: l3mapgen par=AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc.param
l2_flag_names: LAND,HISOLZEN
time_coverage_start: 2021-09-01T00:45:00.000Z
time_coverage_end: 2021-10-01T02:55:00.000Z
start_orbit_number: 102808
end_orbit_number: 103246
map_projection: Equidistant Cylindrical
latitude_units: degrees_north
longitude_units: degrees_east
northernmost_latitude: 90
southernmost_latitude: -90
westernmost_longitude: -180
easternmost_longitude: 180
geospatial_lat_max: 90
geospatial_lat_min: -90
geospatial_lon_max: 180
geospatial_lon_min: -180
latitude_step: 0.0416666679084301
longitude_step: 0.0416666679084301
sw_point_latitude: -89.9791641235352
sw_point_longitude: -179.97917175293
spatialResolution: 4.64 km
geospatial_lon_resolution: 0.0416666679084301
geospatial_lat_resolution: 0.0416666679084301
geospatial_lat_units: degrees_north
geospatial_lon_units: degrees_east
number_of_lines: 4320
number_of_columns: 8640
measure: Mean
suggested_image_scaling_minimum: -2
suggested_image_scaling_maximum: 45
suggested_image_scaling_type: LINEAR
suggested_image_scaling_applied: No
_lastModified: 2021-12-03T08:21:22.000Z
Conventions: CF-1.6 ACDD-1.3
institution: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group
standard_name_vocabulary: CF Standard Name Table v36
naming_authority: gov.nasa.gsfc.sci.oceandata
id: AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc/L3/AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc
license: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
creator_name: NASA/GSFC/OBPG
publisher_name: NASA/GSFC/OBPG
creator_email: data@oceancolor.gsfc.nasa.gov
publisher_email: data@oceancolor.gsfc.nasa.gov
creator_url: https://oceandata.sci.gsfc.nasa.gov
publisher_url: https://oceandata.sci.gsfc.nasa.gov
processing_level: L3 Mapped
cdm_data_type: grid
keywords: Earth Science > Oceans > Ocean Optics > Sea Surface Temperature
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords
data_bins: 20227868
data_minimum: -1.80000007152557
data_maximum: 40.0000038146973
##从每个 Aqua Modis 文件中提取 SST、经度和纬度值，为感兴趣的变量创建 59 个列表。但是，我认为我只是提取单个 Aqua Modis 文件的值 [1]
SST_filenames <- ncvar_get(SST, "sst")
lon_filenames <- ncvar_get(SST, "lon")
lat_filenames <- ncvar_get(SST, "lat")
我想我会尝试 brick() 函数但没有成功，因为我仍然认为我是从单个 Aqua Modis 文件中提取值 [1]。 .
SST_brick <- brick(filenames[59], "sst")
lon_brick <- brick(filenames[59], "lon")
lat_brick <- brick(filenames[59], "lat")
SST_brick 的结果 <- brick(filenames[59], "sst"):
lass : RasterBrick
dimensions : 4320, 8640, 37324800, 1 (nrow, ncol, ncell, nlayers)
resolution : 0.04166667, 0.04166667 (x, y)
extent : -180, 180, -90.00001, 90 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc
names : layer
varname : sst
如果我使用对象 SST，我想我仍然在打开单个 Aqua Modis netCDF 文件的值 [1]
SST_filenames <- ncvar_get(SST, "sst")
lon_filenames <- ncvar_get(SST, "lon")
lat_filenames <- ncvar_get(SST, "lat")
您似乎误解了 RasterStack 或 SpatRaster 对象是什么。你打印了 SST_brick ，它清楚地表明它知道经度和纬度。您不需要其他任何东西。
有
library(terra)
filenames = list.files('~/Documents/Ocean_ColorSST_2016_2021',pattern='*.nc',full.names=TRUE)
并假设 dolphins 是一个矩阵或 data.frame 具有变量“经度”和“纬度”，您可以像这样对一个文件执行此操作：
SST <- rast(filenames[1], "sst")
e <- extract(SST, dolphins[, c("longitude", "latitude")])
但你或许可以像这样一口气做到这一点
SSTs <- rast(filenames, "sst")
e <- extract(SSTs, dolphins[, c("longitude", "latitude")])
问题：
我在 R 中有一个代码，它 从单个 Aqua Modis netCDF 文件（见下文）中提取每月海面温度 (SST) 值。但是，我在一个文件夹中有一批 59 个 Aqua Modis netCDF 文件。
目标：
我的目标是从所有 59 个 netCDF 文件 的每个 netCDF 文件中提取变量的经度、纬度和 SST，将它们转换使用函数 stack::raster() 转换为光栅文件，然后处理这些文件。
我的数据框有 650 行，它们是 海豚 ID。 我想提取 平均 SST 2016-2021时间段内的每个海豚 ID。一旦我提取了每个海豚 ID 的平均值 SST，我想将这些值写入一个 .csv 文件，文件名为 ' Average_SST'.
很遗憾，由于所有权问题，我无法共享我的数据。
我在空间分析方面完全是个新手，近 4 天来我一直在尝试通过观看 YouTube、阅读教程和 Stack 来解决这个难题溢出。我现在感到非常困惑，我相信我必须遍历所有文件才能实现我的 objective.
如果有人能帮忙，我先谢谢你了。
R-代码：
打开文件夹中的所有 59 个 Aqua Modis netCDF 文件
##Open packages needed for our analysis
library(ncdf4)
library(terra)
library('RNetCDF')
library(raster)
library(sp)
filenames = list.files('~/Documents/Ocean_ColorSST_2016_2021',pattern='*.nc',full.names=TRUE)
##Open the 70 Aqua Modis netCDF files from their folder
SST <- nc_open(filenames[59])
##Print the results for SST
print(SST)
结果：
3 variables (excluding dimension variables):
short sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Sea Surface Temperature
scale_factor: 0.00499999988824129
add_offset: 0
units: degree_C
standard_name: sea_surface_temperature
_FillValue: -32767
valid_min: -1000
valid_max: 10000
display_scale: linear
display_min: -2
display_max: 45
unsigned byte qual_sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Quality Levels, Sea Surface Temperature
_FillValue: 255
valid_min: 0
valid_max: 5
unsigned byte palette[eightbitcolor,rgb] (Contiguous storage)
4 dimensions:
lat Size:4320
long_name: Latitude
units: degrees_north
standard_name: latitude
_FillValue: -999
valid_min: -90
valid_max: 90
lon Size:8640
long_name: Longitude
units: degrees_east
standard_name: longitude
_FillValue: -999
valid_min: -180
valid_max: 180
rgb Size:3 (no dimvar)
eightbitcolor Size:256 (no dimvar)
[1] ">>>> WARNING <<< attribute data_bins is an 8-byte value, but R"
[1] "does not support this data type. I am returning a double precision"
[1] "floating point, but you must be aware that this could lose precision!"
61 global attributes:
product_name: AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc
instrument: MODIS
title: MODISA Level-3 Standard Mapped Image
project: Ocean Biology Processing Group (NASA/GSFC/OBPG)
platform: Aqua
temporal_range: month
processing_version: R2019.0
date_created: 2021-12-03T08:21:22.000Z
history: l3mapgen par=AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc.param
l2_flag_names: LAND,HISOLZEN
time_coverage_start: 2021-09-01T00:45:00.000Z
time_coverage_end: 2021-10-01T02:55:00.000Z
start_orbit_number: 102808
end_orbit_number: 103246
map_projection: Equidistant Cylindrical
latitude_units: degrees_north
longitude_units: degrees_east
northernmost_latitude: 90
southernmost_latitude: -90
westernmost_longitude: -180
easternmost_longitude: 180
geospatial_lat_max: 90
geospatial_lat_min: -90
geospatial_lon_max: 180
geospatial_lon_min: -180
latitude_step: 0.0416666679084301
longitude_step: 0.0416666679084301
sw_point_latitude: -89.9791641235352
sw_point_longitude: -179.97917175293
spatialResolution: 4.64 km
geospatial_lon_resolution: 0.0416666679084301
geospatial_lat_resolution: 0.0416666679084301
geospatial_lat_units: degrees_north
geospatial_lon_units: degrees_east
number_of_lines: 4320
number_of_columns: 8640
measure: Mean
suggested_image_scaling_minimum: -2
suggested_image_scaling_maximum: 45
suggested_image_scaling_type: LINEAR
suggested_image_scaling_applied: No
_lastModified: 2021-12-03T08:21:22.000Z
Conventions: CF-1.6 ACDD-1.3
institution: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group
standard_name_vocabulary: CF Standard Name Table v36
naming_authority: gov.nasa.gsfc.sci.oceandata
id: AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc/L3/AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc
license: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
creator_name: NASA/GSFC/OBPG
publisher_name: NASA/GSFC/OBPG
creator_email: data@oceancolor.gsfc.nasa.gov
publisher_email: data@oceancolor.gsfc.nasa.gov
creator_url: https://oceandata.sci.gsfc.nasa.gov
publisher_url: https://oceandata.sci.gsfc.nasa.gov
processing_level: L3 Mapped
cdm_data_type: grid
keywords: Earth Science > Oceans > Ocean Optics > Sea Surface Temperature
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords
data_bins: 20227868
data_minimum: -1.80000007152557
data_maximum: 40.0000038146973
> SST_brick <- brick(list[59], "sst")
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'brick': object of type 'builtin' is not subsettable
> ##Print the results for SST
> print(SST)
File /Users/kirstymedcalf/Documents/DMAD/Publication/DMAD_Maps_Analysis_Publication/Montenegro_Final_Analysis_Folders/Ocean_ColorSST_2016_2021/AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc (NC_FORMAT_NETCDF4):
3 variables (excluding dimension variables):
short sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Sea Surface Temperature
scale_factor: 0.00499999988824129
add_offset: 0
units: degree_C
standard_name: sea_surface_temperature
_FillValue: -32767
valid_min: -1000
valid_max: 10000
display_scale: linear
display_min: -2
display_max: 45
unsigned byte qual_sst[lon,lat] (Chunking: [87,44]) (Compression: shuffle,level 4)
long_name: Quality Levels, Sea Surface Temperature
_FillValue: 255
valid_min: 0
valid_max: 5
unsigned byte palette[eightbitcolor,rgb] (Contiguous storage)
4 dimensions:
lat Size:4320
long_name: Latitude
units: degrees_north
standard_name: latitude
_FillValue: -999
valid_min: -90
valid_max: 90
lon Size:8640
long_name: Longitude
units: degrees_east
standard_name: longitude
_FillValue: -999
valid_min: -180
valid_max: 180
rgb Size:3 (no dimvar)
eightbitcolor Size:256 (no dimvar)
[1] ">>>> WARNING <<< attribute data_bins is an 8-byte value, but R"
[1] "does not support this data type. I am returning a double precision"
[1] "floating point, but you must be aware that this could lose precision!"
61 global attributes:
product_name: AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc
instrument: MODIS
title: MODISA Level-3 Standard Mapped Image
project: Ocean Biology Processing Group (NASA/GSFC/OBPG)
platform: Aqua
temporal_range: month
processing_version: R2019.0
date_created: 2021-12-03T08:21:22.000Z
history: l3mapgen par=AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc.param
l2_flag_names: LAND,HISOLZEN
time_coverage_start: 2021-09-01T00:45:00.000Z
time_coverage_end: 2021-10-01T02:55:00.000Z
start_orbit_number: 102808
end_orbit_number: 103246
map_projection: Equidistant Cylindrical
latitude_units: degrees_north
longitude_units: degrees_east
northernmost_latitude: 90
southernmost_latitude: -90
westernmost_longitude: -180
easternmost_longitude: 180
geospatial_lat_max: 90
geospatial_lat_min: -90
geospatial_lon_max: 180
geospatial_lon_min: -180
latitude_step: 0.0416666679084301
longitude_step: 0.0416666679084301
sw_point_latitude: -89.9791641235352
sw_point_longitude: -179.97917175293
spatialResolution: 4.64 km
geospatial_lon_resolution: 0.0416666679084301
geospatial_lat_resolution: 0.0416666679084301
geospatial_lat_units: degrees_north
geospatial_lon_units: degrees_east
number_of_lines: 4320
number_of_columns: 8640
measure: Mean
suggested_image_scaling_minimum: -2
suggested_image_scaling_maximum: 45
suggested_image_scaling_type: LINEAR
suggested_image_scaling_applied: No
_lastModified: 2021-12-03T08:21:22.000Z
Conventions: CF-1.6 ACDD-1.3
institution: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group
standard_name_vocabulary: CF Standard Name Table v36
naming_authority: gov.nasa.gsfc.sci.oceandata
id: AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc/L3/AQUA_MODIS.20210901_20210930.L3b.MO.SST.nc
license: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
creator_name: NASA/GSFC/OBPG
publisher_name: NASA/GSFC/OBPG
creator_email: data@oceancolor.gsfc.nasa.gov
publisher_email: data@oceancolor.gsfc.nasa.gov
creator_url: https://oceandata.sci.gsfc.nasa.gov
publisher_url: https://oceandata.sci.gsfc.nasa.gov
processing_level: L3 Mapped
cdm_data_type: grid
keywords: Earth Science > Oceans > Ocean Optics > Sea Surface Temperature
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords
data_bins: 20227868
data_minimum: -1.80000007152557
data_maximum: 40.0000038146973
##从每个 Aqua Modis 文件中提取 SST、经度和纬度值，为感兴趣的变量创建 59 个列表。但是，我认为我只是提取单个 Aqua Modis 文件的值 [1]
SST_filenames <- ncvar_get(SST, "sst")
lon_filenames <- ncvar_get(SST, "lon")
lat_filenames <- ncvar_get(SST, "lat")
我想我会尝试 brick() 函数但没有成功，因为我仍然认为我是从单个 Aqua Modis 文件中提取值 [1]。 .
SST_brick <- brick(filenames[59], "sst")
lon_brick <- brick(filenames[59], "lon")
lat_brick <- brick(filenames[59], "lat")
SST_brick 的结果 <- brick(filenames[59], "sst"):
lass : RasterBrick
dimensions : 4320, 8640, 37324800, 1 (nrow, ncol, ncell, nlayers)
resolution : 0.04166667, 0.04166667 (x, y)
extent : -180, 180, -90.00001, 90 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : AQUA_MODIS.20210901_20210930.L3m.MO.SST.sst.4km.nc
names : layer
varname : sst
如果我使用对象 SST，我想我仍然在打开单个 Aqua Modis netCDF 文件的值 [1]
SST_filenames <- ncvar_get(SST, "sst")
lon_filenames <- ncvar_get(SST, "lon")
lat_filenames <- ncvar_get(SST, "lat")
您似乎误解了 RasterStack 或 SpatRaster 对象是什么。你打印了 SST_brick ，它清楚地表明它知道经度和纬度。您不需要其他任何东西。
有
library(terra)
filenames = list.files('~/Documents/Ocean_ColorSST_2016_2021',pattern='*.nc',full.names=TRUE)
并假设 dolphins 是一个矩阵或 data.frame 具有变量“经度”和“纬度”，您可以像这样对一个文件执行此操作：
SST <- rast(filenames[1], "sst")
e <- extract(SST, dolphins[, c("longitude", "latitude")])
但你或许可以像这样一口气做到这一点
SSTs <- rast(filenames, "sst")
e <- extract(SSTs, dolphins[, c("longitude", "latitude")])