A Study of Planets Dove Satellites Radi A Study of Planets Dove - - PowerPoint PPT Presentation

Otago, New Zealand – July 31, 2019

A Study of Planet’s Dove Satellites’ Radi A Study of Planet’s Dove Satellites’ Radiometry

• metry

against Sentinel against Sentinel -2 over U.S. Farmland 2 over U.S. Farmland

Joshua Greenberg, Planet Labs, Inc.

Singapore Strait, Singapore – July 29, 2016

Introduction

Flock Dove Classic RapidEye SkySat Dove-R Sensor Type Four-band frame Imager with a split- frame NIR filter Multispectral push- broom Multispectra / Panchromatic push- frame Four-stripe push- frame Imager Spectral Bands Blue: 455 - 515 nm Green: 500 - 590 nm Red: 590 - 670 nm NIR: 780 - 860 nm Blue: 440 - 510 nm Green: 520 - 590 nm Red: 630 - 685 nm Red Edge: 690 - 730 nm NIR: 760 - 850 nm Blue: 450 - 515 nm Green: 515 - 595 nm Red: 605 - 695 nm NIR: 740 - 900 nm Pan: 450 - 900 nm Blue: 490 nm Green: 565 nm Red: 665 nm NIR: 865 nm Orbit SSO SSO SSO SSO GSD ~3.0 m ~6.5 m ~1.0 m (~0.8 pan) ~3.0 m Frame Size/Swath Width ~ 24.6 km x 16.4 km 77 km ~ 3.2 km x 1.4 km (single camera) ~ 26 km Crossing Time 9:30 - 11:30 am 11:00 am 10:30 - 13:00 9:30 - 11:30

The Planet Fleet

Flock Dove Classic RapidEye SkySat Dove-R Sensor Type Four-band frame Imager with a split- frame NIR filter Multispectral push- broom Multispectra / Panchromatic push- frame Four-stripe push- frame Imager Spectral Bands Blue: 455 - 515 nm Green: 500 - 590 nm Red: 590 - 670 nm NIR: 780 - 860 nm Blue: 440 - 510 nm Green: 520 - 590 nm Red: 630 - 685 nm Red Edge: 690 - 730 nm NIR: 760 - 850 nm Blue: 450 - 515 nm Green: 515 - 595 nm Red: 605 - 695 nm NIR: 740 - 900 nm Pan: 450 - 900 nm Blue: 490 nm Green: 565 nm Red: 665 nm NIR: 865 nm Orbit SSO SSO SSO SSO GSD ~3.0 m ~6.5 m ~1.0 m (~0.8 pan) ~3.0 m Frame Size/Swath Width ~ 24.6 km x 16.4 km 77 km ~ 3.2 km x 1.4 km (single camera) ~ 26 km Crossing Time 9:30 - 11:30 am 11:00 am 10:30 - 13:00 9:30 - 11:30

The present study will look at both Dove Classic and Dove-R. The Planet Fleet

Planet’s current radiometric calibration methodology primarily makes use of well-characterized desert pseudo-invariant sites, a regime far removed from many actual use cases -- e.g. Ag! Dove-R Radiometric Calibration

Planet must understand the behavior of our agricultural data so we can communicate to customers. Moreover, we must understand the behavior of

• ur radiometry over the full dynamic range, not

just bright desert sites.

Motivation for Farmland Crossover Study

Sentinel-2a Dove-R In Blue, Green, and Red, Dove- R has similar central wavelengths and band widths to Sentinel-2a’s and -2b’s B2, B3, and B4 respectively. This study compares the Doves’ NIR band to Sentinel-2’s B8. A comparison to B8A is planned, too. Motivation for Comparison to Sentinel -2

Dove Classic (0f44) Sentinel-2a The comparisons between Dove Classics’ RSRs and Sentinel-2’s are not as close for RGB (though the comparison of NIR to B8 is more reasonable). Motivation for Comparison to Sentinel -2

“The Cropland Data Layer (CDL) is a crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. The CDL is created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section.” -- developers.google.com (and it’s free) Identifying farmland: USDA’s Cropland Data Layer

CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support

Weiguo Han, Zhengwei Yang, Liping Di, Richard Mueller

(USDA provides an awesome website for accessing the CDL.) Cropland Data Layer

• Planet’s crossover

infrastructure requires polygon AOIs to search for crossovers

• CDL is polygonized by

finding contiguous, majority-crop regions of

• ver 45 km2 area.
• Identity of crops is

discarded (for now). Extracting Farmland Polygons from CDL

The official title of this talk references a plan to use the USDA Common Land Units (CLUs). Because I encountered difficulties working with the 2008 CLU dataset, I

• pted to work with the 2018 CDL

instead. Zoomed in Sublette County, WY -- 2008 CLUs Note on CDL vs 2008 CLU

S2B at ~ 10:37 AM 1057 (Dove-R) at ~ 10:30 AM local

Tile 1055524 in UTM zone 10 (25km ⨉ 25km)

May 2, 2019 (RGB TOA-rad shown). Example Crossover Tile

S2B 1057 (Dove-R) Each UTM tile was broken down into 100 2.5km ⨉ 2.5km subtiles, numbered 00 to 99. Here is subtile 48: Sampling Subtiles

Products after NDVI filtering and sampling each to 4m GSD S2B 1057 (Dove-R) Ideally the 2018 CDL itself (or CLU dataset) would be used at this point to attempt to sample only crop pixels (and to guess at their identity). That’s not yet implemented; for this analysis I used an NDVI filter but it’s

• imperfect. :)

Sampling Subtiles, cont.

S2B 1057 (Dove-R)

Idle cropland Grapes Grass

2018 CDL Comparing to 2018 CDL for Fun

Pixel value pairs are histogrammed: density scatter plots are shown, for this subtile, for B, G, R, N. 2D Histogramming

Fitting a Gaussian to the histogram “peak” allows one to find a mode, an x-y pair that expresses the relationship between the band values for this subtile. Here, the Sentinel-2B mode value is 1522.4 which corresponds to ~ 65.8 W/sr/μm/m2 radiance in its red band, vs. ~ 63.6 for the Dove sat. Zooming in... 2D Histogramming: Summary Statistic

Each subtile contributes a single x-y point, its 2d histogram mode. A single crossover event -- a crossing of the swaths of one Dove and Sentinel-2 -- can cross multiple UTM tiles, each of which may have many subtiles with usable pixels. The 1057 vs. S2B crossover event on 2019-05-02 contributed

• ver 2000 points (before

subsampling...). All Subtile Modes for a Crossover Event

To accelerate the analysis, I chose 39 Doves (34 Classic, 5 Dove-R), and for each crossover event, randomly chose just 5 subtiles. This decreased the amount of data to analyze by a factor of 600. I analyzed over 28000 distinct crossover events, representing 6 million subtiles (1.6 trillion pixels). This is the largest study I’ve ever attempted on Planet data. Unfortunately, I have not been able to scale our statistical analysis code to process this amount of data before JACIE! Subsampling

Btw, note that high variance appears within data for a single sat.

All crossover events for 0f44 after subsampling: All Crossover Events for a Single Dove Sat

All crossover events for Dove Classic vs Sentinel-2 A and B, after subsampling: All Measurements for All Dove Classic, Plotted Together

All crossover events for Dove-R vs Sentinel-2 A and B, after subsampling: All Measurements for All Dove-R, Plotted Together

Counts distinct crossover events that contributed at least one usable data point after subsampling: approx 2000 total Dove-to-Sentinel-2 Crossovers, counts by month

A hierarchical linear model is used. It accounts for:

• Within-event clustering (by

using random per-crossover- event intercepts)

• Correlation of those intercepts

with event mean radiance

• Similarity of sats to each other -
• the flock mean slope can be a

prior for each sats’ slopes. Fitting Method

1.14 1.12 1.28 0.99 Slopes of fit to all data (grand mean) All Dove Results

(...at 1 SD, across population of sats) 1.14 ± 0.02 1.12 ± 0.02 1.28 ± 0.05 0.99 ± 0.02 Slopes for each sat processed so far (34 Classic, 5 Dove-R) Distribution of Sat Slopes

Dove Classic Percent Differences vs. Sentinel-2 Radiance

Dove-R Percent Differences vs. Sentinel-2 Radiance

London Array Wind Farm, United Kingdom – April 17, 2016

Summary

• A study has been conducted comparing Dove sats to Sentinel-2

in near-simultaneous crossovers over farmland AOIs derived from USDA’s Cropland Data Layer.

• This enables us to say something about the relative and

absolute errors in the Dove top-of-atmosphere radiance products over a more diverse set of landcover, and dimmer landcover, than heretofore.

• At the bottom of the dynamic range, relative errors are

high, although the behavior is better with Dove-R than Dove Classic.

Summary

• Future work will use specific crop information from CDL, and will

take additional effects into account (e.g. BRDF).

• Eventually this analysis will be able to be automatized and

provide regular feedback on how Planet stacks up to other data providers when it comes to cropland.

Summary, continued

go.planet.com/explore19