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. Sherman Barn NDVI. Broadband and SRS data match pretty well during the wet winter season, but SRS NDVI is about 0.05 lower than broadband NDVI during the dry summer and fall seasons. Not much PL data yet, but it has a weird peak in July 2018. I need to check this data.

 

Figure 2. Incoming bands of SRS sensor. No obvious decay.

 

Figure 3. Outgoing bands of SRS sensor. Seems reasonable.

 

Figure 4. Timeseries of incoming bands over the whole lifetime of the SRS sensor, first at Twitchell Alfalfa (Feb 2017-May 2018), then at Sherman Barn (Jul 2018-Jul 2020). I wanted to plot this because there were only a few years of data at Sherman Barn, and the longer time series might help us see decay more easily. Happily, no obvious decay seen from this sensor over the 3-4 years it was used.

 

Figure 5. Time series of outgoing bands over the whole lifetime of the SRS sensor, first at Twitchell Alfalfa (Feb 2017-May 2018), then at Sherman Barn (Jul 2018-Jul 2020). I assume the seasonal maxes are the same within each site, and I don't see any obvious decay.

 

Figure 6. Red reflectances from SRS and PL data. PL reflectance is markedly low in July 2017 than SRS reflectance.

 

Figure 7. NIR reflectances from SRS and PL data match pretty well. SRS reflectance increases about 2 weeks earlier in winter 2018.

 

Figure 8. Linear regression between SRS and other 2 NDVI methods. There's not much PL data, but SRS and broadband NDVI have a good fit with R2=0.94.

 

Figure 9. Timeseries of GCC and SRS data. Annual patterns seem reasonable. GCC_13-15 is the average GCC from 13:00 to 15:00, and GCC_11-13 is the average GCC from 11:00 to 13:00. Both GCC and SRS have similar timing of winter increase and early spring peak; GCC seems to start decreasing in the late spring slightly earlier than NDVI starts decreasing.

 

 

Figure 10. Linear regression between GCC and SRS data. Fit is good, R2=0.94, so here GCC could be a good proxy for NDVI.