README.md
In e-sensing/sitsdata: Data sets to support examples in sits package
Data sets for the e-sensing project
This project contains in situ data sets used in the e-sensing project.
These data sets consist of time series from selected locations, which
are used to train machine learning models, and data cubes to run
examples of sits usage.
To load these datasets, first, install the sitsdata package using
devtools:
devtools::install_github("e-sensing/sitsdata")
Next, load it:
library(sitsdata)
In the next sections are examples of how you can use the datasets
available.
In case of any issue in following the steps above, please check the
installation tip section.
Data format
The sitsdata R package contains time-series to be used for
classification with machine learning methods which are available when
the package is loaded using library(sitsdata). All satellite image
time-series have the following columns:
longitude (East-west coordinate of the time series sample in WGS
84).latitude (North-south coordinate of the time series sample in WGS
84).start_date (initial date of the time series).end_date (final date of the time series).label (the class label associated to the sample).cube (the name of the image data cube associated with the data).time_series (list with the values of the time series).
Datasets available
In the sections below is the metadata of each dataset available in the
sitsdata package.
Land Use and Land Cover in Cerrado Biome using MODIS
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_cerrado_mod13q1 |
| Region | Cerrado Biome (Brazil) |
| Number of Time Series | 50160 |
| Satellite | Terra |
| Satellite-Sensor | MODIS |
| Product | MOD13Q1 |
| Data Source | NASA |
| Spatial Resolution | 250 meters |
| Time Extent | 2000-08-28 to 2019-09-01 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | MIR, NIR |
| Spectral Indices | EVI,NDVI |
| Land Cover Classes | Dense_Woodland, Dunes, Fallow_Cotton, Millet_Cotton, Pasture, Rocky_Savanna, Savanna, Savanna_Parkland, Silviculture, Soy_Corn, Soy_Cotton, Soy_Fallow |
| Reference | Lorena Santos, Karine Ferreira, Gilberto Camara, Michelle Picoli, Rolf Simoes, “Controle de qualidade e redução de ruído de classe de séries temporais de imagens de satélite”. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 177, pp 75-88, 2021. Access Link |
| License | 
Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_cerrado_mod13q1")
By using the command above, the dataset will be available in the
samples_cerrado_mod13q1 variable.
Click to learn how to view the data
If you want to view the dataset you just loaded, you can use the
sits R package:
plot(samples_cerrado_mod13q1)
To view it in am interactive map, use:
sits_view(samples_cerrado_mod13q1)
To learn more, please check the sits R package
book.
Land Use and Land Cover in Mato Grosso using MODIS
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_matogrosso_mod13q1 |
| Region | Mato Grosso State (Brazil) |
| Number of Time Series | 1837 |
| Satellite | Terra |
| Satellite-Sensor | MODIS |
| Product | MOD13Q1 |
| Data Source | NASA |
| Spatial Resolution | 250 meters |
| Time Extent | 2000-09-13 to 2016-08-28 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | MIR, NIR |
| Spectral Indices | EVI,NDVI |
| Land Cover Classes | Cerrado, Fallow_Cotton, Forest, Millet_Cotton, Pasture, Soy_Corn, Soy_Cotton, Soy_Fallow, Soy_Millet |
| Reference | Michelle Picoli, Gilberto Camara, et al., “Big Earth Observation Time Series Analysis for Monitoring Brazilian Agriculturee”. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 328-339, 2018. Access LinkCâmara, Gilberto; Picoli, Michelle, et al., “Land cover change maps for Mato Grosso State in Brazil: 2001-2017” (version 3). PANGAEA, 2021. Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_matogrosso_mod13q1")
By using the command above, the dataset will be available in the
samples_matogrosso_mod13q1 variable.
Click to learn how to view the data
If you want to view the dataset you just loaded, you can use the
sits R package:
plot(samples_matogrosso_mod13q1)
To view it in am interactive map, use:
sits_view(samples_matogrosso_mod13q1)
To learn more, please check the sits R package
book.
Land Use and Land Cover in a portion of the Cerrado Using CBERS-4 AWFI
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_cerrado_cbers |
| Region | Cerrado (Brazil) |
| Number of Time Series | 922 |
| Satellite | CBERS-4 |
| Satellite-Sensor | MUX |
| Product | CB4_64_16D_STK |
| Data Source | BDC |
| Spatial Resolution | 64 meters |
| Time Extent | 2018-08-29 to 2019-08-13 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | BAND13, BAND14, BAND15, BAND16 |
| Spectral Indices | EVI,NDVI |
| Land Cover Classes | Cerrado, Fallow_Cotton, Forest, Millet_Cotton, Pasture, Soy_Corn, Soy_Cotton, Soy_Fallow, Soy_Millet |
| Reference | Karine Ferreira, Gilberto Queiroz, et al., “Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products”. Remote Sensing, 12, 4033, 2020. Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_cerrado_cbers")
By using the command above, the dataset will be available in the
samples_cerrado_cbers variable.
Click to learn how to view the data
If you want to view the dataset you just loaded, you can use the
sits R package:
plot(samples_cerrado_cbers)
To view it in am interactive map, use:
sits_view(samples_cerrado_cbers)
To learn more, please check the sits R package
book.
Deforestation in Rondonia using Sentinel-2A
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_prodes_4classes |
| Region | Rondonia (Brazil) |
| Number of Time Series | 393 |
| Satellite | Sentinel-2 |
| Satellite-Sensor | MSI |
| Product | SENTINEL-2-L2A |
| Data Source | BDC |
| Spatial Resolution | 10 meters |
| Time Extent | 2020-06-04 to 2021-08-26 |
| Temporal Resolution | 16-day composite (29 data points per year) |
| Spectral Bands | B02, B03, B04, B08, B8A, B11, B12 |
| Spectral Indices | NDVI, EVI, NBR |
| Land Cover Classes | Burned_Area, Forest, Highly_Degraded, Cleared_Area |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_prodes_4classes")
By using the command above, the dataset will be available in the
samples_prodes_4classes variable.
Click to learn how to view the data
If you want to view the dataset you just loaded, you can use the
sits R package:
plot(samples_prodes_4classes)
To view it in am interactive map, use:
sits_view(samples_prodes_4classes)
To learn more, please check the sits R package
book.
Yearly deforestation samples in the Amazon Biome using Sentinel-2/2A
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_deforestation |
| Region | Amazon Biome (Brazil) |
| Number of Time Series | 6007 |
| Satellite | Sentinel-2 |
| Satellite-Sensor | MSI |
| Product | SENTINEL-2-L2A |
| Data Source | BDC |
| Spatial Resolution | 10 meters |
| Time Extent | 2022-01-05 to 2022-12-23 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | B02, B8A, B11 |
| Spectral Indices | NDVI, EVI, NBR |
| Land Cover Classes | Clear_Cut_Bare_Soil, Clear_Cut_Burned_Area, Clear_Cut_Vegetation, Forest, Mountainside_Forest, Riparian_Forest, Seasonally_Flooded, Water, Wetland |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_deforestation")
By using the command above, the dataset will be available in the
samples_deforestation variable.
Click to learn how to view the data
If you want to view the dataset you just loaded, you can use the
sits R package:
plot(samples_deforestation)
To view it in am interactive map, use:
sits_view(samples_deforestation)
To learn more, please check the sits R package
book.
Image Data cubes used for classification examples
Indices and spectral bands from SENTINEL-2/2A images for tile 20LMR in Rondonia
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | Rondonia-20LMR |
| Region | Rondonia (Brazil) |
| Number of Images | 23 TIF files |
| Satellite | Sentinel-2 |
| Satellite-Sensor | MSI |
| Spatial Resolution | 10 meters |
| Time Extent | 2022-01-05 to 2022-12-23 |
| Tile | 20LMR (MGRS grid) |
| Temporal Resolution | 16-day composite |
| Spectral Bands | B02, B8A, B11 |
| Spectral Indices | NDVI, EVI, NBR |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data_dir <- system.file("extdata/Rondonia-20LMR", package = "sitsdata")
By using the command above, the path to the Rondonia-20LMR dataset
will be stored in the data_dir variable.
To learn more, please check the sits R package
book.
EVI and NDVI MOD13Q1 images for Sinop
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | sinop |
| Region | Sinop (Brazil) |
| Number of Images | 23 TIF files |
| Satellite | Terra |
| Satellite-Sensor | MODIS |
| Spatial Resolution | 250 meters |
| Time Extent | 2013-09-14 to 2014-08-29 |
| Tile | 012010 |
| Temporal Resolution | 16-day composite |
| Spectral Indices | NDVI, EVI |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data_dir <- system.file("extdata/sinop", package = "sitsdata")
By using the command above, the path to the sinop dataset will be
stored in the data_dir variable.
To learn more, please check the sits R package
book.
Installation tips
If you are having network issues installing the sitsdata package,
please consider increasing the timeout limit:
options(timeout = 300) # Set timeout to 5 minutes
After updating the timeout limit, you can install the package using the
devtools command:
devtools::install_github("e-sensing/sitsdata")
If you have any other issues, please ask for help in the issue
section; we are keen to support
you!
e-sensing/sitsdata documentation built on April 17, 2025, 5:32 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Data sets for the e-sensing project
This project contains in situ data sets used in the e-sensing project. These data sets consist of time series from selected locations, which are used to train machine learning models, and data cubes to run examples of sits usage.
To load these datasets, first, install the sitsdata package using
devtools:
devtools::install_github("e-sensing/sitsdata")
Next, load it:
library(sitsdata)
In the next sections are examples of how you can use the datasets available.
In case of any issue in following the steps above, please check the installation tip section.
Data format
The sitsdata R package contains time-series to be used for
classification with machine learning methods which are available when
the package is loaded using library(sitsdata). All satellite image
time-series have the following columns:
longitude(East-west coordinate of the time series sample in WGS 84).latitude(North-south coordinate of the time series sample in WGS 84).start_date(initial date of the time series).end_date(final date of the time series).label(the class label associated to the sample).cube(the name of the image data cube associated with the data).time_series(list with the values of the time series).
Datasets available
In the sections below is the metadata of each dataset available in the
sitsdata package.
Land Use and Land Cover in Cerrado Biome using MODIS
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_cerrado_mod13q1 |
| Region | Cerrado Biome (Brazil) |
| Number of Time Series | 50160 |
| Satellite | Terra |
| Satellite-Sensor | MODIS |
| Product | MOD13Q1 |
| Data Source | NASA |
| Spatial Resolution | 250 meters |
| Time Extent | 2000-08-28 to 2019-09-01 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | MIR, NIR |
| Spectral Indices | EVI,NDVI |
| Land Cover Classes | Dense_Woodland, Dunes, Fallow_Cotton, Millet_Cotton, Pasture, Rocky_Savanna, Savanna, Savanna_Parkland, Silviculture, Soy_Corn, Soy_Cotton, Soy_Fallow |
| Reference | Lorena Santos, Karine Ferreira, Gilberto Camara, Michelle Picoli, Rolf Simoes, “Controle de qualidade e redução de ruído de classe de séries temporais de imagens de satélite”. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 177, pp 75-88, 2021. Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_cerrado_mod13q1")
By using the command above, the dataset will be available in the
samples_cerrado_mod13q1 variable.
If you want to view the dataset you just loaded, you can use the sits R package:
plot(samples_cerrado_mod13q1)
To view it in am interactive map, use:
sits_view(samples_cerrado_mod13q1)
To learn more, please check the sits R package book.
Land Use and Land Cover in Mato Grosso using MODIS
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_matogrosso_mod13q1 |
| Region | Mato Grosso State (Brazil) |
| Number of Time Series | 1837 |
| Satellite | Terra |
| Satellite-Sensor | MODIS |
| Product | MOD13Q1 |
| Data Source | NASA |
| Spatial Resolution | 250 meters |
| Time Extent | 2000-09-13 to 2016-08-28 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | MIR, NIR |
| Spectral Indices | EVI,NDVI |
| Land Cover Classes | Cerrado, Fallow_Cotton, Forest, Millet_Cotton, Pasture, Soy_Corn, Soy_Cotton, Soy_Fallow, Soy_Millet |
| Reference | Michelle Picoli, Gilberto Camara, et al., “Big Earth Observation Time Series Analysis for Monitoring Brazilian Agriculturee”. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 328-339, 2018. Access LinkCâmara, Gilberto; Picoli, Michelle, et al., “Land cover change maps for Mato Grosso State in Brazil: 2001-2017” (version 3). PANGAEA, 2021. Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_matogrosso_mod13q1")
By using the command above, the dataset will be available in the
samples_matogrosso_mod13q1 variable.
If you want to view the dataset you just loaded, you can use the sits R package:
plot(samples_matogrosso_mod13q1)
To view it in am interactive map, use:
sits_view(samples_matogrosso_mod13q1)
To learn more, please check the sits R package book.
Land Use and Land Cover in a portion of the Cerrado Using CBERS-4 AWFI
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_cerrado_cbers |
| Region | Cerrado (Brazil) |
| Number of Time Series | 922 |
| Satellite | CBERS-4 |
| Satellite-Sensor | MUX |
| Product | CB4_64_16D_STK |
| Data Source | BDC |
| Spatial Resolution | 64 meters |
| Time Extent | 2018-08-29 to 2019-08-13 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | BAND13, BAND14, BAND15, BAND16 |
| Spectral Indices | EVI,NDVI |
| Land Cover Classes | Cerrado, Fallow_Cotton, Forest, Millet_Cotton, Pasture, Soy_Corn, Soy_Cotton, Soy_Fallow, Soy_Millet |
| Reference | Karine Ferreira, Gilberto Queiroz, et al., “Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products”. Remote Sensing, 12, 4033, 2020. Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_cerrado_cbers")
By using the command above, the dataset will be available in the
samples_cerrado_cbers variable.
If you want to view the dataset you just loaded, you can use the sits R package:
plot(samples_cerrado_cbers)
To view it in am interactive map, use:
sits_view(samples_cerrado_cbers)
To learn more, please check the sits R package book.
Deforestation in Rondonia using Sentinel-2A
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_prodes_4classes |
| Region | Rondonia (Brazil) |
| Number of Time Series | 393 |
| Satellite | Sentinel-2 |
| Satellite-Sensor | MSI |
| Product | SENTINEL-2-L2A |
| Data Source | BDC |
| Spatial Resolution | 10 meters |
| Time Extent | 2020-06-04 to 2021-08-26 |
| Temporal Resolution | 16-day composite (29 data points per year) |
| Spectral Bands | B02, B03, B04, B08, B8A, B11, B12 |
| Spectral Indices | NDVI, EVI, NBR |
| Land Cover Classes | Burned_Area, Forest, Highly_Degraded, Cleared_Area |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_prodes_4classes")
By using the command above, the dataset will be available in the
samples_prodes_4classes variable.
If you want to view the dataset you just loaded, you can use the sits R package:
plot(samples_prodes_4classes)
To view it in am interactive map, use:
sits_view(samples_prodes_4classes)
To learn more, please check the sits R package book.
Yearly deforestation samples in the Amazon Biome using Sentinel-2/2A
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | samples_deforestation |
| Region | Amazon Biome (Brazil) |
| Number of Time Series | 6007 |
| Satellite | Sentinel-2 |
| Satellite-Sensor | MSI |
| Product | SENTINEL-2-L2A |
| Data Source | BDC |
| Spatial Resolution | 10 meters |
| Time Extent | 2022-01-05 to 2022-12-23 |
| Temporal Resolution | 16-day composite (23 data points per year) |
| Spectral Bands | B02, B8A, B11 |
| Spectral Indices | NDVI, EVI, NBR |
| Land Cover Classes | Clear_Cut_Bare_Soil, Clear_Cut_Burned_Area, Clear_Cut_Vegetation, Forest, Mountainside_Forest, Riparian_Forest, Seasonally_Flooded, Water, Wetland |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data("samples_deforestation")
By using the command above, the dataset will be available in the
samples_deforestation variable.
If you want to view the dataset you just loaded, you can use the sits R package:
plot(samples_deforestation)
To view it in am interactive map, use:
sits_view(samples_deforestation)
To learn more, please check the sits R package book.
Image Data cubes used for classification examples
Indices and spectral bands from SENTINEL-2/2A images for tile 20LMR in Rondonia
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | Rondonia-20LMR |
| Region | Rondonia (Brazil) |
| Number of Images | 23 TIF files |
| Satellite | Sentinel-2 |
| Satellite-Sensor | MSI |
| Spatial Resolution | 10 meters |
| Time Extent | 2022-01-05 to 2022-12-23 |
| Tile | 20LMR (MGRS grid) |
| Temporal Resolution | 16-day composite |
| Spectral Bands | B02, B8A, B11 |
| Spectral Indices | NDVI, EVI, NBR |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data_dir <- system.file("extdata/Rondonia-20LMR", package = "sitsdata")
By using the command above, the path to the Rondonia-20LMR dataset
will be stored in the data_dir variable.
To learn more, please check the sits R package book.
EVI and NDVI MOD13Q1 images for Sinop
The following table presents the metadata of this dataset:
| Attribute | Details |
|----|----|
| Dataset ID | sinop |
| Region | Sinop (Brazil) |
| Number of Images | 23 TIF files |
| Satellite | Terra |
| Satellite-Sensor | MODIS |
| Spatial Resolution | 250 meters |
| Time Extent | 2013-09-14 to 2014-08-29 |
| Tile | 012010 |
| Temporal Resolution | 16-day composite |
| Spectral Indices | NDVI, EVI |
| Reference | SITS Access Link |
| License | Attribution 4.0 International (CC BY 4.0) |
To use this dataset, you can use the following command:
library(sits)
library(sitsdata)
data_dir <- system.file("extdata/sinop", package = "sitsdata")
By using the command above, the path to the sinop dataset will be
stored in the data_dir variable.
To learn more, please check the sits R package book.
Installation tips
If you are having network issues installing the sitsdata package,
please consider increasing the timeout limit:
options(timeout = 300) # Set timeout to 5 minutes
After updating the timeout limit, you can install the package using the devtools command:
devtools::install_github("e-sensing/sitsdata")
If you have any other issues, please ask for help in the issue section; we are keen to support you!
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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