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. 2017 Mar 21;Volume 9(Iss 3):10.3390/rs9030296.
doi: 10.3390/rs9030296.

A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring

Belen Franch  1   2 , Eric F Vermote  2 , Jean-Claude Roger  1   2 , Emilie Murphy  1   2 , Inbal Becker-Reshef  1 , Chris Justice  1 , Martin Claverie  1   2 , Jyoteshwar Nagol  1 , Ivan Csiszar  3 , Dave Meyer  4 , Frederic Baret  5 , Edward Masuoka  2 , Robert Wolfe  2 , Sadashiva Devadiga  6
Affiliations

A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring

Belen Franch et al. Remote Sens (Basel). .

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.

Keywords: AVHRR; LCDR; MODIS; surface reflectance, yield monitoring.

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Figures

Figure 1.
Figure 1.
Accuracy assessment of the geolocation of AVHRR products using the coastal chips database (in fraction of pixels). Green is with clock correction, red is without clock correction.
Figure 2:
Figure 2:
Comparison of the NOAA16-AVHRR/MODIS Terra cross calibration over desert sites for band 1 (black solid line) and band 2 (black interrupted line), with the trends obtained using the Ocean and Clouds method [4] for band 1 (blue line and square) and band 2 (red line and square) (from [5]).
Figure 3.
Figure 3.
Evaluation of the global performance of the current cloud mask for NOAA16-AVHRR versus MODIS Aqua cloud mask. Results reported as percentage. Left side is the CLAVR algorithm [10]. Right is the current LCDR improved cloud mask. MODIS Aqua cloud mask is used as truth in this comparison. Red symbols (Match) show the percentage of agreement between AVHRR and MODIS, Green symbols (False) show the percentage of cases where AVHRR erroneously detects clouds, Blue symbols (Missed) show the percentage of cases where AVHRR missed clouds.
Figure 4.
Figure 4.
AVHRR time-series of channel 1 (blue) and channel 2 (red) surface reflectance and the NDVI (green) using a) CLAVR or b) LCDR cloud masks for a deciduous broadleaf site in Madagascar. Black symbols are clouds. The standard deviation of the unfiltered data of the time series (Orig.data) and of the cloud filtered time series (QA Mask for CLAVR, New2 Mask for LCDR cloud mask) are also provided for each of the bands and the NDVI. The percentage of clear data is also provided for each cloud mask at the top of the figure.
Figures 5.
Figures 5.
Comparison of current AVHHR Surface Reflectance (LCDR) and PAL data for channel 1 (a) and channel 2 (b) at 48 AERONET sites for 1999 (from [9]). The x-axis shows the surface reflectance values determined from the 6S code supplied with atmospheric parameters from an AERONET sunphotometer, while the y-axis shows the surface reflectances retrieved from the AVHRR data using current LCDR and PAL algorithms.
Figures 6.
Figures 6.
Cross comparison between AVHRR N16, N18 and N19 and MODIS Terra ratios for the BELMANIP2 sites for the red band (a) and the near infrared band (b).
Figure 7.
Figure 7.
BELMANIP-2 and DIRECT network sites location (http://calvalportal.ceos.org/web/olive/site-description).
Figure 8.
Figure 8.
Comparison of MODIS and AVHRR LAI (a) and FAPAR (b) during 2001 to 2007. Data were extracted over DIRECT sites not used during the training process.
Figure 9.
Figure 9.
National winter wheat predicted yield (a) and production (b) in the U.S., applying the [1] ‘original’ method to AVHRR data plotted against USDA reported statistics (https://quickstats.nass.usda.gov).
Figure 10.
Figure 10.
a) Percentage error evolution when forecasting the winter wheat production (black) and yield (red) with historical AVHRR data. The dashed line represents the error committed when considering a constant production (black) or yield (red) and equal to the average through the time series. b) Nash–Sutcliffe model efficiency coefficient evolution depending on the day of the year of the forecast.
Figure 11.
Figure 11.
National winter wheat predicted yield in the U.S. applying [1] method to LAI (a) and FAPAR (b) AVHRR data.
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References

    1. Becker-Reshef I; Vermote E; Lindeman M; Justice C A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ 2010, 114, 1312–1323.
    1. Franch B; Vermote EF; Becker-Reshef I; Claverie M; Huang J; Zhang J; Justice C; Sobrino JA Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information. Remote Sens. Environ 2015, 161, 131–148.
    1. Zhang X Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data. Remote Sens. Environ 2015, 156, 457–472.
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    1. Vermote EF; Saleous NZ Calibration of NOAA16 AVHRR over a desert site using MODIS data. Remote Sens. Environ 2006, 105, 214–220.

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