Reports

Various sensors measuring NDVI have been installed across the Delta sites over the last several years. At some sites, the SRS sensors from Decagon/METER have been drifting low, so I wanted to compare them with other NDVI measurements.

• SRS sensor (Decagon/METER): A pair of photodiodes with cosine correcting Teflon diffuser and hemispherical view. Red band is measured at 650nm and NIR band is measured at 810nm.
• SR-411 sensor (Apogee): A pair of photodiodes with acrylic diffuser and hemispherical view. Red band is measured at 650nm and NIR band is measured at 810nm.
• Planet Labs data: L3H (CESTEM) alpha data product. Pixel size is 3m, and there are daily gap-filled images from 2018-2020. Gap fills are done with Planet imagery from the day before and after, combined with daily MODIS data. From the full image, Joe cut out a 730mx730m tile for each site, centered on the tower. Product includes NDVI, GCC, ECI, and the following reflectances: red, NIR, green, blue. For more information, see Box/Biometlab/Remote_Sensing/PlanetLabs/L3H_distribution_lodi_islands_readme.pdf.
• Broadband NDVI: NDVI calculated using reflected SW and PAR data from the tower, called "broadband" because it uses the broader bands of shortwave and PAR radiation rather than the narrow bands of the SRS and Apogee sensors. See Huemmrich et al., 1999 in Journal of Geophysical Research: Atmospheres and Tittebrand et al., 2009 in Theoretical and Applied Climatology.

For some sites with I also compared GCC with the various NDVI values to see how much GCC values varied year over year. Photos were taken by a variety of cameras: Netcam Stardot cameras (the official camera of the Phenocam Network®), Canon point-and-shoot cameras with custom firmware, or a Raspberry Pi camera.

I used Joe's datafetch tool to calculate daily average mid-day values of NDVI and GCC from the various sensors. Mid-day values included data from 11:00 to 13:00, 5 values a day. I despiked the data in Excel.

• NDVI = (NIR-Red)/(NIR+Red)
• GCC = (Green)/(Red + Green + Blue)
• Broadband NDVI = (rho_NIR-rho_PAR)/(rho_NIR+rho_PAR)
  • rho_NIR= (SWout-PARout)/(SWin-PARin)
  • rho_PAR=(PARout/PARin)
  • Units of both SW and PAR should be in W/m2. Use the conversion 4.6 umol/m2/s = 1 W/m2 to convert the usual PAR units (umol/m2/s) to W/m2.

See reports for other Delta sites here:

Bouldin Alfalfa

Bouldin Corn

East End

East Pond / Sherman Wetland Temp Tower (both sites had same set of SRS sensors)

Mayberry

Sherman Wetland

Twitchell Alfalfa / Sherman Barn (both sites had same set of SRS sensors)

 West Pond

 

 

Figure 1. East Pond NDVI. SRS NDVI is about 0.2 lower than broadband NDVI year round. Something happens around 2019-12-05 with the SRS sensor, and data looks bad after that.

 

Figure 2. Individual bands of SRS sensor. 650in goes bad starting Dec 2019.

 

Figure 3. Red reflectances from SRS data. Again, I wouldn't trust the data starting Dec 2019 as the reflectance is much higher than it was in winter 2018-2019.

 

Figure 4. NIR reflectances from SRS data. Seems fine.

 

Figure 5. Linear regression between SRS and broadband NDVI. Fit is affected by the cluster on the lower left part of the graph from low SRS values starting Dec 2019.

 

Figure 6. Linear regression between SRS and broadband NDVI without the bad SRS data. Fit is much better, R2=0.90. Slope has increased from 0.67 to 0.80 after removing bad data.

 

Figure 7. Timeseries of GCC and SRS data. Annual patterns seem reasonable. GCC keeps decreasing from Jan-March while NDVI bottoms out in Jan. Both GCC and NDVI start increasing in mid-March and peak around mid-August before decreasing.

 

Figure 8. Linear regression between GCC and SRS data. Fit is affected by the cluster on the lower left part of the graph from low SRS values starting Dec 2019.

 

Figure 9. Linear regression between GCC and SRS data without the bad SRS data. Fit is better, R2=0.80, and slope decreased from 3.9 to 3.1after removing bad data.