Executive Summary

This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.

Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.

Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.

The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.

Introduction

The Linear Imaging Self Scanning-3 (LISS–3) sensor onboard the Indian Space Research Organisation Resourcesat-2 satellite is a high-resolution land observation instrument consisting of four bands: green, red, near infrared, and shortwave infrared. Resourcesat-2 was launched in 2011, and an identical mission, Resourcesat-2A, was launched in 2016. The primary objectives for data acquired by LISS–3 include monitoring biomass, vegetation, land cover, leaf area index, and normalized difference vegetation index.

The data analysis results provided within this report have been derived from approved Joint Agency Commercial Imagery Evaluation (JACIE) processes and procedures. JACIE was formed to leverage resources from several Federal agencies for the characterization of remote sensing data and to share those results across the remote sensing community. More information about JACIE is available at https://www.usgs.gov/core-science-systems/eros/calval/jacie?qt-science_support_page_related_con=3#qt-science_support_page_related_con.

System Description

This section describes the satellite and operational details for Resourcesat-2 and provides information about the LISS–3 sensor.

Sensor Information

The spectral characteristics and the relative spectral response are listed in table 2 and figure 1, respectively.

Table 2.    

Imaging sensor details for Resourcesat-2 Linear Imaging Self Scanning-3.

[The Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor has a swath width of 140 kilometers; μm, micrometer; m, meter; NIR, near infrared; SWIR, shortwave infrared]

Table 2.    Imaging sensor details for Resourcesat-2 Linear Imaging Self Scanning-3.

Spectral band(s) details	Resourcesat-2 LISS–3
	Lower band (µm)	Upper band (µm)	Radiometric resolution (bits)	Ground sample distance (m)
Band 2—green	0.52	0.59	10	23.5
Band 3—red	0.62	0.68	10	23.5
Band 4—NIR	0.77	0.86	10	23.5
Band 5—SWIR	1.55	1.70	10	23.5

Procedures

ECCOE has established standard processes to identify Earth observing systems of interest and to assess the geometric, radiometric, and spatial qualities of data products from these systems.

The assessment steps are as follows:

Measurements

The observed USGS measurements are listed in table 3. The mean of interior (band-to-band) and exterior (image-to-image) mean errors, standard deviation (STDDEV), and root mean square errors (RMSEs) are listed in meters (pixels). Details about the methodologies used are outlined in the “Analysis” section.

Analysis

This section describes the geometric, radiometric, and spatial performance of LISS–3.

Geometric Performance

This geometric performance for LISS–3 is characterized in terms of the interior (band-to-band alignment) and exterior (geometric location accuracy) geometric analysis results.

Interior (Band to Band)

The band-to-band alignment analysis was completed using the EROS System Characterization (EROSSC) software on three separate images over the United States. Band combinations were registered against each other to determine the mean error, STDDEV, and RMSE, as listed in table 4 with results represented in pixels at a 30-meter (m) GSD (the LISS–3 image was resampled to 30 m). Geometric error maps for each assessed band combination over the New Mexico image, as well as the corresponding histogram graphs, are shown in figures 2–5. The geometric error maps indicate the directional shift and relative magnitude of the shift, and the histogram and error distribution indicate frequency of observed mean error measurements within the image. Together, the interior and exterior geometric analysis results, as reported in the “Interior (Band to Band)” and “Exterior (Geometric Location Accuracy)” sections, provide a comprehensive assessment of geometric accuracy.

Table 4.    

Band-to-band registration error (in pixels).

[ID, identifier; STDDEV, standard deviation; RMSE, root mean square error]

Table 4.    Band-to-band registration error (in pixels).

Scene ID (location)	Band combination	Mean error (easting)	Mean error (northing)	STDDEV (easting)	STDDEV (northing)	RMSE (easting)	RMSE (northing)
R2LS310212018267036_L1T (Fargo, North Dakota)	Band 2–band 3	0.003	0.001	0.039	0.062	0.039	0.062
	Band 2–band 4	−0.000	−0.001	0.043	0.051	0.043	0.051
	Band 2–band 5	−0.139	−0.293	0.053	0.078	0.149	0.303
	Band 3–band 4	−0.004	−0.009	0.057	0.065	0.057	0.065
	Band 3–band 5	−0.0153	−0.298	0.078	0.060	0.172	0.304
	Band 4–band 5	−0.136	−0.322	0.039	0.056	0.141	0.327
R2LS305022018295036_L1T (Maine)	Band 2–band 3	0.017	−0.015	0.049	0.084	0.052	0.085
	Band 2–band 4	−0.049	0.017	0.118	0.146	0.128	0.146
	Band 2–band 5	−0.154	−0.051	0.130	0.123	0.201	0.133
	Band 3–band 4	−0.065	0.031	0.118	0.175	0.134	0.177
	Band 3–band 5	−0.154	−0.098	0.142	0.117	0.209	0.152
	Band 4–band 5	0.000	0.050	0.173	0.186	0.173	0.192
R2LS305152019264046_L1T (New Mexico)	Band 2–band 3	−0.021	−0.194	0.053	0.068	0.057	0.205
	Band 2–band 4	0.441	−0.241	0.071	0.104	0.447	0.262
	Band 2–band 5	0.367	−0.412	0.054	0.055	0.371	0.415
	Band 3–band 4	0.395	0.003	0.123	0.073	0.413	0.071
	Band 3–band 5	0.399	−0.212	0.073	0.083	0.405	0.228
	Band 4–band 5	−0.122	−0.285	0.037	0.057	0.127	0.291

Exterior (Geometric Location Accuracy)

For this analysis, band 2 (green) of the LISS–3 data was compared against the corresponding band from three Landsat 8 OLI images over sites in the United States using the EROSSC software. Conjugate points in the reference and search images were identified automatically and refined using similarity measures such as normalized cross-correlation metrics, and the mean error, STDDEV, and RMSE results are listed in table 5 with results represented in pixels (and meters) at a 30-m GSD (LISS–3 was resampled to 30 m). For each of the three images, geometric error maps showing the directional shift and relative magnitude of the shift, when compared with the Landsat 8 OLI, along with the corresponding histogram and error distribution, are provided in figures 6–11. The Landsat 8 OLI imagery had a control uncertainty of about 8 m.

Table 5.    

Geometric error of Resourcesat-2 Linear Imaging Self Scanning-3 relative to Landsat 8 Operational Land Imager.

[ID, identifier; STDDEV, standard deviation; RMSE, root mean square error; m, meter]

Table 5.    Geometric error of Resourcesat-2 Linear Imaging Self Scanning-3 relative to Landsat 8 Operational Land Imager.

Scene ID (location)	Mean error (easting)	Mean error (northing)	STDDEV (easting)	STDDEV (northing)	RMSE (easting)	RMSE (northing)
R2LS310212018267036_L1TLC08_L1TP_030027_20181021_20200830_02_T1 (Fargo, North Dakota)	−0.311 pixel (−9.342 m)	−0.209 pixel (−6.286 m)	0.156 pixel (4.667 m)	0.061 pixel (1.833 m)	0.348 pixel (10.434 m)	0.218 pixel (6.546 m)
R2LS305022018295036_L1TLC08_L1TP_010028_20180502_20200901_02_T1 (Maine)	0.885 pixel (26.578 m)	−1.178 pixels (−35.341 m)	0.254 pixel (7.615 m)	0.383 pixel (11.489 m)	0.921 pixel (27.646 m)	1.238 pixels (37.158 m)
R2LS305152019264046_L1TLC08_L1TP_032036_20190515_20200828_02_T1 (New Mexico)	−0.926 pixel (−27.805 m)	−0.567 pixel (−17.014 m)	0.152 pixel (4.569 m)	0.288 pixel (8.652 m)	0.939 pixel (28.177 m)	0.636 pixel (19.082 m)

Radiometric Performance

For this analysis, cloud-free regions of interest were selected within three near-coincident LISS–3 and Landsat 8 OLI scene pairs using the EROSSC software. Once the relative georeferencing error between Landsat 8 OLI and Resourcesat-2 LISS–3 has been corrected, Top of Atmosphere reflectance values from the two sensors are extracted. The scatterplot (fig. 12) is drawn in a way that the x-axis is the reference sensor and the y-axis is the comparison sensor. The linear regression, thus, represents Top of Atmosphere reflectance relative to that of the reference sensor. Ideally, the slope should be near unity and the offset should be near zero. For instance, if the slope is greater than unity, that means the comparison sensor has a tendency to overestimate Top of Atmosphere reflectance compared to the reference sensor.

Top of Atmosphere reflectance comparison results are listed in table 6. A band-by-band graphical comparison between the LISS–3 image over Maine, when compared with the corresponding Landsat 8 OLI band, is shown in figure 12.

Table 6.    

Top of Atmosphere reflectance comparison of Resourcesat-2 Linear Imaging Self Scanning-3 against Landsat 8 Operational Land Imager.

[ID, identifier; NIR, near infrared; SWIR, shortwave infrared; %, percent; R², coefficient of determination]

Table 6.    Top of Atmosphere reflectance comparison of Resourcesat-2 Linear Imaging Self Scanning-3 against Landsat 8 Operational Land Imager.

Scene ID (location)	Statistics	Band 2—Green	Band 3—Red	Band 4—NIR	Band 5—SWIR
R2LS310212018267036_L1TLC08_L1TP_030027_20181021_20200830_02_T1 (Fargo, North Dakota)	Uncertainty (%)	8.750	11.220	15.020	13.410
	R2	0.926	0.939	0.937	0.631
	Radical offset	−0.027	0.017	0.027	−0.072
	Radical slope	0.610	0.655	0.589	0.904
R2LS305022018295036_L1TLC08_L1TP_010028_20180502_20200901_02_T1 (Maine)	Uncertainty (%)	13.340	17.470	12.700	24.170
	R2	0.793	0.852	0.836	0.877
	Radical offset	0.022	0.015	0.021	0.036
	Radical slope	0.695	0.575	0.635	0.584
R2LS305152019264046_L1TLC08_L1TP_032036_20190515_20200828_02_T1 (New Mexico)	Uncertainty (%)	4.49	6.48	5.33	5.46
	R2	0.904	0.908	0.894	0.665
	Radical offset	0.029	0.025	0.030	−0.096
	Radical slope	0.648	0.687	0.658	0.946

Summary and Conclusions

This report summarizes the sensor performance of the Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor based on the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence (ECCOE) system characterization process. In summary, we have determined that this sensor provides an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 m (0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.035 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.

In conclusion, the team has completed an ECCOE standardized system characterization of the Resourcesat-2 LISS–3 sensing system. Although the team followed characterization procedures that are standardized across the many sensors and sensing systems under evaluation, these procedures are customized to fit the individual sensor as was done with LISS–3. The team has acquired the data, defined proper testing methodologies, carried out comparative tests against specific references, recorded measurements, completed data analyses, and quantified sensor performance accordingly. The team also endeavored to retain all data, measurements, and methods. This is key to ensure that all data and measurements are archived and accessible and that the performance results are reproducible.

The ECCOE project and associated Joint Agency Commercial Imagery Evaluation partners are always interested in reviewing sensor and remote sensing application assessments and would like to see and discuss information on similar data and product assessments and reviews. If you would like to discuss system characterization with the U.S. Geological Survey ECCOE and (or) the Joint Agency Commercial Imagery Evaluation team, please email us at eccoe@usgs.gov.