*Kamel Didan1,2, Armando Barreto1,2, Ezzulddin Naji1,2

Validation of Vegetation Indices from Landsat MODIS and VIIRS Sensors using Drones and NEON AOP

Hyperspectral Data Collected at Santa Rita Experimental Range

1 Vegetation Index and Phenology Lab., 2 ABE Dept., The University of Arizona* didan@email.arizona.edu

Acknowledgements: This work was supported by NASA grants #NNX08AT05A and #NNX11AG56G (Kamel Didan, PI)

Daily NDVI from surface reflectance from TERRA and AQUA (250m), VIIRS (500m), and Landsat8 (30m). Because of the afternoon clouds, we used DOY 236 for AQUA and VIIRS. Dark areas in the Drone/Sequoia NDVI image correspond to very low NDVI values over bare soil.

Results

Using the Relative Spectral responses from Landsat8, MODIS, VIIRS and Drone camera we convolved the data to create simulated observations from each of these sensors. Statistical analysis allowed us to assess the correlation between these sensors by relating the drone camera NDVI and EVI2 to the NEON derived ones, the NEON to Landsat8 , Landsat8 to MODIS and VIIRS.

Data collected with the VIP drone using the Parrot Sequoia multispectral camera with a solar calibration plate, on August 24, 2017. The raw Rad and NIR data was normalized to surface reflectance (0 – 1). Hugin software and VIP custom tools used to calculate vegetation indices

Drone Mosaic

http://vip.arizona.eduhttp://www.neonscience.org/https://cals.arizona.edu/srer/

http://vip.arizona.eduhttp://www.neonscience.org/https://cals.arizona.edu/srer/

IntroductionVegetation Indices are one of the most widely derived and used satellite remote sensing products for studying the land surface vegetation composition and processes. With more than 35 years (AVHRR, MODIS, VIIRS, and Landsat) of VI data records it is critical to characterize and continuously validate these data records. The different spatial, temporal, and radiometric characteristics contributes to this disparity.While in-situ observations remain the most accurate validation approach, the associated footprint is usually very limited. The other alternative is to consider scaling of multispectral field images which offers a reasonable approach supported by statistical analysis techniques.Drones equipped with multispectral, thermal, and high resolution cameras are now offering potentials for science and high precision observations. They provide an inexpensive, easy, fast, and versatile platforms for collecting imagery over relatively small areas which can then be used to validate satellite observations.

NEON AOPThe National Ecological Observatory Network

(NEON), with its Airborne Observation Platform (AOP) collects hyperspectral imagery at very high spectral resolution over different eco-climatic domains located across the US annually. The hyperspectral data consists of 428 bands extending from 380 to 2510 nm with a 1-meter spatial resolution. This offers a great opportunity for validating higher order products and coarse and medium resolution based remote sensing data.

Conclusions

Sensor RED (nm) NIR (nm) BLUE (nm) Pixel Size Temporal

Drone/ Sequoia 620-700 750-830 N/A 6 cm* Any Time**

NEON AOP Every 5nm (380-2510nm) 1 m yearly

LANDSAT OLI 640-670 850-880 450-510 30 m 16 days

MODIS 620-670 841-876 459-479 250 m 16 days ***

VIIRS 600-680 846-885 436-454 500 m 16 days ***

Sensors & Data specs

VIIRS

MODIS

LANDSAT

Pixel Size

DOY:235 DOY:236 DOY:237 DOY:238

VIIRS

LANDSAT 8

DOY:235 DOY:236 DOY:237 DOY:238 DOY:235 DOY:236 DOY:237 DOY:238

DOY:237 DOY:253

NEON – AOP Flight SRER

The Santa Rita Experimental Range, managed by the UofA and home to the NEON is located East of Green Valley. On August 24th, 25th, 28th and 29th, the NEON AOP collected hyperspectral data over the range. The SRER dataset is composed of 86 hdf5 hyperspectral files.

Our Lab. (vip.arizona.edu) developed an online tool for the exploration of NEON AOP hyperspectral data. The tool has visualization, data extraction, mosaicking, and Landsat, MODIS and VIIRS band convolution capabilities among other features.

NEON Data

NEON AOP DRONE

NIRD

RED

EVI2

NDVI

VIP NEON-AOP DataExplorer

DOY:236DOY:237-240

Drones present a low cost platform for the validation and characterization of satellite based remote sensing data.Across sensors correlations show a high R2 > .85 and are expected to be even more robust if more data over longer periods of time were used. Drone based data remains limited in space due to flight time, and covering a single MODIS or VIIRS pixel may take more than one mission. The Parrot Sequoia multispectral camera NIR band was very different from other space borne sensors making the correlation less robust. Other mini-cameras may have worked better.

Landsat8 MODIS VIIRS

VIP-Lab Drone NEON AOP

RGB

NDVI

EVI2

Simulated Landsat8 from NEON-AOP using band convolution approach.

TERRA

We used the 2-band EVI version (EVI2) since our drone lacks a blue band.

Relative Spectral Responses (RSR)

Relative Spectral Responses (RSR) are used to simulate any sensor data (i.e. Landsat, MODIS) from the hyperspectral NEON AOP data. This allows us to simulate any sensor spectral and spatial resolutions, including our drone imager, and perform correlation analysis in support of validation, assuming the NEON-AOP to be the ground truth (given the spatial resolution and flight height).

RSR measurements are assumed to be constant for all detectors covered by a common filter and are normalized to unity at peak response .

DRONE LANDSAT

MODIS

VIIRS

DRONE

LANDSAT

MODIS

VIIRS

NEON AOP

a) Sensor Validation

b) Calibration using NEON simulated datasets

R2 Landsat 8 Terra Aqua VIIRS

DRONE 0.338 - - -

Landsat 1 0.855 0.887 0.897

VIIRS 0.897 0.883 0.901 1

DRONE * 0.471 0.542 0.507 0.553

AQUA

RGB