![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 sun photometer, while the y-axis shows the surface reflectances retrieved from the AVHRR data using current LCDR and PAL algorithms.](https://www.researchgate.net/profile/Jean-Claude-Roger-2/publication/315475129/figure/fig1/AS:613895689478164@1523375546413/Comparison-of-current-AVHHR-Surface-Reflectance-LCDR-and-PAL-data-for-channel-1-a-and_Q320.jpg)
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remote sensing
Article
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 2and Sadashiva Devadiga 6
1Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;
roger63@umd.edu (J.-C.R.); emilie.murphy@nasa.gov (E.M.); ireshef@umd.edu (I.B.-R.);
cjustice@umd.edu (C.J.); mcl@umd.edu (M.C.); jnagol@umd.edu (J.N.)
2NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA;
eric.f.vermote@nasa.gov (E.F.V.); edward.j.masuoka@nasa.gov (E.M.); robert.e.wolfe@nasa.gov (R.W.)
3NOAA Center for Satellite Applications and Research, College Park, MD 20746, USA;
Ivan.Csiszar@noaa.gov
4Goddard Earth Science Data and Information Services Center (GES DISC), NASA Goddard Space Flight
Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA; david.j.meyer@nasa.gov
5INRA, UnitéEnvironnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (UMR1114),
Domaine St Paul, Site Agroparc, 84914 Avignon CEDEX 09, France; frederic.baret@avignon.inra.fr
6Science Systems and Applications Inc., Lanham, MD 20706, USA; sadashiva.devadiga-1@nasa.gov
*Correspondence: belen.franchgras@nasa.gov
Academic Editors: Jose Moreno, Clement Atzberger and Prasad S. Thenkabail
Received: 27 May 2016; Accepted: 15 March 2017; Published: 21 March 2017
Abstract:
The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique
global remote sensing dataset that ranges from the 1980s 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
geolocation, improvement of 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 Becker-Reshef et al. (2010) and Franch et al. (2015) 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 1980s, the results have errors equivalent to those derived
from MODIS.
Keywords: AVHRR; LCDR; MODIS; surface reflectance; yield monitoring
1. Introduction
The surface reflectance product is a critical input for generating downstream products, such as
vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically-active radiation
Remote Sens. 2017,9, 296; doi:10.3390/rs9030296 www.mdpi.com/journal/remotesensing
Remote Sens. 2017,9, 296 2 of 14
(FAPAR), bidirectional reflectance distribution function (BRDF), albedo, and land cover. A surface
reflectance land climate data record (LCDR) needs to be of the highest possible quality so that
minimal uncertainties propagate in the dependent/downstream products. The generation of such
a record necessitates the use of multi-instrument/multi-sensor science-quality data record and a
strong emphasis on data consistency, which, in this study, is achieved by careful characterization and
processing of the original data, rather than degrading and smoothing the dataset. As a consequence,
the LCDR needs to be derived from accurately calibrated top of the atmosphere reflectance values that
are precisely geo-located, carefully screened for clouds and cloud shadows, corrected for atmospheric
effects using a radiative transfer model-based approach and, finally, corrected for directional effects.
All of these steps are necessary, as spurious trends will appear in the data record if the above effects
are not corrected.
The first requirement for accurate atmospheric correction is a proper absolute calibration of the
instrument. Calibration errors propagate through the whole atmospheric correction chain, in particular
through the aerosol inversion and impact most of the bands in the visible part of the spectrum and
subsequent downstream products. It is very important therefore to assess instrument performance
and independently monitor calibration. The Advanced Very High Resolution Radiometer (AVHRR)
remains an important data source for the study of long-term variations in land surface properties as
it provides the longest time-series of global satellite measurements [
1
]. Vermote and Kaufman [
2
]
presented a method for absolute calibration of the red and near-infrared channels of AVHRR. It was
based on a combination of observations over remote ocean areas and over highly reflective clouds
located in the tropics over the Pacific Ocean. Later, Vermote and Saleous [
3
] validated these results
using a stable Saharan desert site and data from MODIS. The agreement between MODIS and AVHRR
was better than 1%. Inter-comparison of the MODIS Aqua and AVHRR for the 2000–2014 period
reported in this paper has further enabled refinement of the AVHRR record. Using state-of-the-art
algorithms for geolocation, calibration, cloud screening, and atmospheric and surface directional effect
correction, we have been able to achieve the most consistent data record possible. Such a long data
record allows for the development of several applications involving evaluation of trends in surface
properties (e.g., [
4
–
6
]). During the last several years, agricultural monitoring using remote sensing data
has gained increasing interest among the science community, mainly since the development in 2011 of
the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) initiative. The main
objective of GEOGLAM is to strengthen global agricultural monitoring by improving the use of remote
sensing tools for crop production projections and weather forecasting. In this context we demonstrate
the performance of the LCDR, by applying the yield model described in [
7
,
8
] to the V4 series of the
AVHRR data records.
In this paper, we present the latest improvements of the AVHRR BRDF corrected surface
reflectance and NDVI Version 4 products by assessing the accuracy of geolocation (Section 3.1),
calibration (Section 3.2), cloud mask (Section 3.3), and the final surface reflectance product using
AERONET data (Section 3.4) and cross-comparing it to MODIS Aqua (Section 3.5). Additionally, we
evaluate the performance of the LAI, which is derived using surface reflectance (Section 3.6). Finally,
an application of the product to estimate the winter wheat yield in the USA from the 1980s is presented
in Section 3.7.
2. Materials
2.1. Land Climate Data Record (LCDR)
This work builds on previous efforts by [
9
] that created the first three versions of the consistent
long-term land data records spanning a time period of 1981–2000 through processing and reprocessing,
of the AVHRR Global Area Coverage (GAC) data. The NASA LCDR detailed in [
9
] contains gridded
daily surface reflectance and brightness temperatures derived from processing of the data acquired
by the AVHRR sensors onboard four NOAA polar orbiting satellites: NOAA-7, -9, -11, and -14. Daily
Remote Sens. 2017,9, 296 3 of 14
surface reflectance from the AVHRR channels 1 and 2 (at 640 and 860 nm) is a NOAA Climate Data
Record (CDR). These data records are produced in a geographic projection at a spatial resolution of
0.05 degree similar to the Climate Modelling Grid (CMG) used in processing of the daily MODIS
Surface Reflectance CMG data MOD09CMG/MYD09CMG.
With substantial improvements, the version 4 Land Surface CDR products were produced by the
NASA Goddard Space Flight Center (GSFC) and the University of Maryland. The Version 4 series
extended the time period of the records to the present day through processing of the AVHRR data from
the NOAA-16, -17, -18, and -19, with additional improvements to version 3. Improvements include
better geolocation accuracy achieved by using one-line-elements (OLE) instead of two-line-elements
(TLE) for ephemeris, the use of the center of each grid as the reference to be consistent with other
heritage records, such as from MODIS on-board the Earth Observing System (EOS) satellites, and
use of a weighted average of available observations instead of the one best sample used in version 3.
Version 4 was produced by reprocessing the raw GAC dataset for each instrument.
2.2. MODIS Daily Climate Model Grid (CMG) Time-Series
This study uses the MODIS CMG daily surface reflectance Collection 6 data (M{OY}D09CMG)
distributed by the Land Processes Distributed Active Archive Center (LP DAAC, https://lpdaac.usgs.
gov/products/modis_products_table), which are gridded in the linear latitude, longitude projection
at 0.05
◦
resolution (5600 m at the Equator). Science Data Sets (SDSs) provided for this product include
surface reflectance values for bands 1–7, brightness temperatures for bands 20, 21, 31, and 32, solar
and view zenith angles, relative azimuth angle, ozone, granule time, quality assessment, cloud mask,
aerosol optical thickness at 550 nm, and water vapor content.
2.3. Methods
2.3.1. Geolocation
The purpose of geolocation assessment is to identify any errors by comparing the images to
control points that can be easily traceable. Thus, in order to assess the accuracy of the geolocation of a
given sensor, we used ‘coastal chips’ as a reference, which were selected manually using the MODIS
CMG product. This approach has been proven very useful for the AVHRR dataset, where the error
could be significant and the drift of the clock onboard the NOAA satellites leads to a desynchronization
between the satellite clock and the tracking station clock [10].
2.3.2. Calibration Monitoring
The approach relies on using the multi-year MODIS Terra dataset to derive spectral and directional
characterizations of stable desert sites that can be used as invariant targets. A candidate list of such
targets is provided in [
11
]. Subsets of MODIS Terra data are collected and undergo a rigorous screening
based on the quality flags (cloud, cloud shadow, adjacent cloud, high aerosol, or snow). The directional
characterization is derived using the MODIS bidirectional reflectance distribution function (BRDF)
algorithm that relies on a kernel-driven linear BRDF model, defined as a weighted sum of three
kernels representing basic scattering types: isotropic scattering, radiative transfer-type volumetric
scattering based on the Ross-Thick function and geometric-optical surface scattering based on the
Li-Sparse model [
12
]. Using the site directional characterization, we compute a surface reflectance at
the needed acquisition time and viewing conditions. Using the data corrected for directional effects
we are also able to spectrally characterize the sites at the MODIS central wavelengths and account
for spectral difference between MODIS and the AVHRR given the relatively broad AVHRR bands
only for each particular site. Atmospheric parameters (surface pressure, gaseous content, water vapor,
aerosol optical thickness) obtained from assimilated data, MODIS data, MODIS-like and/or ground
measurements are then used in conjunction with the 6S radiative transfer code [
13
] to determine
Remote Sens. 2017,9, 296 4 of 14
the target sensor (MODIS-Aqua, AVHRR) Top Of Atmosphere (TOA) reflectance. The computed
reflectance is compared to the acquired reflectance to infer changes in the instrument calibration.
2.3.3. Cloud Mask
The CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) mission and, in particular the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP),
provides a unique and independent opportunity to evaluate cloud mask products. Despite its relatively
narrow footprint (330 m to 5 km depending on the altitude of the sensed layer), CALIOP acquires data
about 2 min after MODIS Aqua, which makes it ideal for cloud mask evaluation and the MODIS Aqua
cloud mask can then be used itself as a reference. The current AVHRR cloud mask has been evaluated
against MODIS Aqua and the results show that is improved as compared to the CLAVR algorithm [
14
].
This improved technique utilizes albedo thresholds derived from MODIS Aqua data to mask clouds.
2.3.4. Surface Reflectance Accuracy Assessment
Accurate estimation of atmospheric parameters, such as water vapor content or aerosol optical
thickness, is critical and comprises the main source of error in the surface reflectance estimation.
With the purpose of assessing the performance of the AVHRR surface reflectance product, we compare
it with the surface reflectance derived from the top of the atmosphere AVHRR data corrected using
field-based atmospheric data. These data were extracted for over 48 AERONET sites distributed across
the globe [15].
2.3.5. Direct Intercomparison of the Surface Reflectance Products
Inter-comparison of the surface reflectance products from different sensors can be used to evaluate
their performance and check their inter-consistency. The MODIS data are accurately calibrated and
the surface reflectance product has been validated through the various stage (up to Stage III) defined
by the MODIS land validation approach [
16
]. Thus, the MODIS surface reflectance product can be
considered as a good reference to evaluate the AVHRR surface reflectance product. The AVHRR
surface reflectance and MODIS Aqua data over the BELMANIP2 (BEnchmark Land Multisite ANalysis
and Intercomparison of Products) sites were intercompared, using the directional correction [
17
].
BELMANIP2 is an updated version of BELMANIP1 [
18
] that aims at providing a representative set of
relatively flat and homogenous sites sampling the variability of land surface type and state over the
globe. The original BELMANIP2 dataset included 445 sites (Figure 1).
Remote Sens. 2017, 9, 296 4 of 14
2.3.3. Cloud Mask
The CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) mission and, in particular the Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP), provides a unique and independent opportunity to evaluate cloud mask products.
Despite its relatively narrow footprint (330 m to 5 km depending on the altitude of the sensed layer),
CALIOP acquires data about 2 min after MODIS Aqua, which makes it ideal for cloud mask
evaluation and the MODIS Aqua cloud mask can then be used itself as a reference. The current
AVHRR cloud mask has been evaluated against MODIS Aqua and the results show that is improved
as compared to the CLAVR algorithm [14]. This improved technique utilizes albedo thresholds
derived from MODIS Aqua data to mask clouds.
2.3.4. Surface Reflectance Accuracy Assessment
Accurate estimation of atmospheric parameters, such as water vapor content or aerosol optical
thickness, is critical and comprises the main source of error in the surface reflectance estimation. With
the purpose of assessing the performance of the AVHRR surface reflectance product, we compare it
with the surface reflectance derived from the top of the atmosphere AVHRR data corrected using
field-based atmospheric data. These data were extracted for over 48 AERONET sites distributed
across the globe [15].
2.3.5. Direct Intercomparison of the Surface Reflectance Products
Inter-comparison of the surface reflectance products from different sensors can be used to
evaluate their performance and check their inter-consistency. The MODIS data are accurately
calibrated and the surface reflectance product has been validated through the various stage (up to
Stage III) defined by the MODIS land validation approach [16]. Thus, the MODIS surface reflectance
product can be considered as a good reference to evaluate the AVHRR surface reflectance product.
The AVHRR surface reflectance and MODIS Aqua data over the BELMANIP2 (BEnchmark Land
Multisite ANalysis and Intercomparison of Products) sites were intercompared, using the directional
correction [17]. BELMANIP2 is an updated version of BELMANIP1 [18] that aims at providing a
representative set of relatively flat and homogenous sites sampling the variability of land surface
type and state over the globe. The original BELMANIP2 dataset included 445 sites (Figure 1).
Figure 1. BELMANIP-2 and DIRECT network site locations (http://calvalportal.ceos.org/web/
olive/site-description).
2.3.6. Agriculture Application
As a demonstration of the utility of the LCDR, we apply the methods developed by [7,8] to test
the performance of the AVHRR data to monitor wheat yield. These methods are based on the
Figure 1.
BELMANIP-2 and DIRECT network site locations (http://calvalportal.ceos.org/web/olive/
site-description).
Remote Sens. 2017,9, 296 5 of 14
2.3.6. Agriculture Application
As a demonstration of the utility of the LCDR, we apply the methods developed by [
7
,
8
] to test the
performance of the AVHRR data to monitor wheat yield. These methods are based on the assumption
that the yield is positively and linearly correlated to the seasonal maximum NDVI (adjusted for
background noise) at the administrative unit (AU, county, or oblast) level and to the purity of the
wheat signal (percentage of wheat within the pixel). Becker-Reshef [
7
] developed a regression model
that was calibrated and applied at the state level in Kansas using MODIS data and proved to be directly
applicable at the national level in Ukraine. Looking for an improvement in the timeliness of the yield
forecast, Franch [
8
] enhanced the method from Becker-Reshef [
7
] by including growing degree day
(GDD) information. With this method a reliable forecast can be made between 30 days to 45 days prior
to the peak NDVI (i.e., 60 to 75 days prior to harvest), while keeping an accuracy of 10% in the yield
forecast. Note that this method provides the same yield results than Becker-Reshef [
7
] when the yield
forecast is applied during the date of the NDVI peak. In this work, we evaluate the yield model’s
applicability to AVHRR.
3. Results
3.1. Geolocation
Geolocation is an important prerequisite to ensure consistency in the land time-series of
observations [
19
]. A number of physical effects such as clouds, atmospheric contamination and
surface anisotropy require compositing multiple daily orbits into a single dataset [
15
,
20
]. Achieving
a high-level accuracy of relative geolocation is a critical step for each orbit [
21
]. Therefore, major
efforts are made in geometric correction and the assessment of geolocation accuracy. The accuracy of
this correction was assessed by using the coastal chips database as a reference. When the on-board
clock was reset, a discontinuity in the accuracy is introduced (Figure 2, red dots). The clock correction
approach developed by [22] significantly improves the geolocation accuracy (Figure 2, green dots).
Remote Sens. 2017, 9, 296 5 of 14
assumption that the yield is positively and linearly correlated to the seasonal maximum NDVI
(adjusted for background noise) at the administrative unit (AU, county, or oblast) level and to the
purity of the wheat signal (percentage of wheat within the pixel). Becker-Reshef [7] developed a
regression model that was calibrated and applied at the state level in Kansas using MODIS data and
proved to be directly applicable at the national level in Ukraine. Looking for an improvement in the
timeliness of the yield forecast, Franch [8] enhanced the method from Becker-Reshef [7] by including
growing degree day (GDD) information. With this method a reliable forecast can be made between
30 days to 45 days prior to the peak NDVI (i.e., 60 to 75 days prior to harvest), while keeping an
accuracy of 10% in the yield forecast. Note that this method provides the same yield results than
Becker-Reshef [7] when the yield forecast is applied during the date of the NDVI peak. In this work,
we evaluate the yield model’s applicability to AVHRR.
3. Results
3.1. Geolocation
Geolocation is an important prerequisite to ensure consistency in the land time-series of
observations [19]. A number of physical effects such as clouds, atmospheric contamination and
surface anisotropy require compositing multiple daily orbits into a single dataset [15,20]. Achieving
a high-level accuracy of relative geolocation is a critical step for each orbit [21]. Therefore, major
efforts are made in geometric correction and the assessment of geolocation accuracy. The accuracy of
this correction was assessed by using the coastal chips database as a reference. When the on-board
clock was reset, a discontinuity in the accuracy is introduced (Figure 2, red dots). The clock correction
approach developed by [22] significantly improves the geolocation accuracy (Figure 2, green dots).
Figure 2. Accuracy assessment of the geolocation of AVHRR products using the coastal chips database
(in fraction of pixels). Green is with clock correction, and red is without clock correction.
Figure 2.
Accuracy assessment of the geolocation of AVHRR products using the coastal chips database
(in fraction of pixels). Green is with clock correction, and red is without clock correction.
Remote Sens. 2017,9, 296 6 of 14
3.2. Calibration Monitoring
Accurate radiometric calibration is a prerequisite to creating a science-quality time-series of
BRDF-corrected surface reflectance and, consequently, higher order downstream products. Calibration
errors can propagate directly into the surface reflectance and create artificial variations that can be
misinterpreted as trends, especially if these variations are due to a slow decay in the calibration
mechanism. Vicarious calibration provides an additional source of calibration information, to verify
and evaluate on-board calibration. As mentioned in the methods section, we will use the approach
of [
3
] for cross-calibration of AVHRR with MODIS to monitor the calibration in the visible to shortwave
infrared bands and to provide correction terms as needed (Figure 3). To assess this approach,
Vermote, et al. [3]
applied it to transfer the MODIS Terra calibration to the MODIS Aqua instrument.
When applied to a stable desert ground site in Niger, the results of this approach agreed to within 1%
of the MODIS Aqua on-board solar diffuser [
3
]. The calibration coefficients used are available from the
project website (http://ltdr.nascom.nasa.gov).
Remote Sens. 2017, 9, 296 6 of 14
3.2. Calibration Monitoring
Accurate radiometric calibration is a prerequisite to creating a science-quality time-series of
BRDF-corrected surface reflectance and, consequently, higher order downstream products.
Calibration errors can propagate directly into the surface reflectance and create artificial variations
that can be misinterpreted as trends, especially if these variations are due to a slow decay in the
calibration mechanism. Vicarious calibration provides an additional source of calibration
information, to verify and evaluate on-board calibration. As mentioned in the methods section, we
will use the approach of [3] for cross-calibration of AVHRR with MODIS to monitor the calibration
in the visible to shortwave infrared bands and to provide correction terms as needed (Figure 3). To
assess this approach, [3] applied it to transfer the MODIS Terra calibration to the MODIS Aqua
instrument. When applied to a stable desert ground site in Niger, the results of this approach agreed
to within 1% of the MODIS Aqua on-board solar diffuser [3]. The calibration coefficients used are
available from the project website (http://ltdr.nascom.nasa.gov).
Figure 3. 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 [2] for band 1 (blue line and square) and band 2 (red line and square) (from [3]).
3.3. Cloud Mask
While the validation of surface reflectance is facilitated by AERONET data, the validation of the
cloud mask remains a significant challenge. To verify the improvement in the cloud mask, we have
undertaken an inter-comparison between the AVHRR cloud mask with the MODIS Aqua cloud mask
for near-coincident (in time) observations. Figure 4 shows the evaluation of the improved AVHRR
cloud mask, where the agreement with MODIS Aqua is higher than 90% compared to an average 60%
agreement for the CLAVR cloud mask. Figure 5 shows the time-series evolution of the surface
reflectance of channel 1 (blue) and channel 2 (red), as well as the NDVI (green) over one BELMANIP2
site (see Section 2.3.4 for a description of the BELMANIP2 sites) located in Madagascar, using the
CLAVR cloud mask (Figure 5a) and the LCDR cloud mask (Figure 5b). These plots show a strong
reduction of noise when using the LCDR cloud mask in channel 1 (from 0.05 to 0.01), channel 2 (from
0.07 to 0.03), and the NDVI (from 0.08 to 0.05).
Figure 3.
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 [2] for band 1 (blue line and square) and band 2 (red line and square) (from [3]).
3.3. Cloud Mask
While the validation of surface reflectance is facilitated by AERONET data, the validation of the
cloud mask remains a significant challenge. To verify the improvement in the cloud mask, we have
undertaken an inter-comparison between the AVHRR cloud mask with the MODIS Aqua cloud mask
for near-coincident (in time) observations. Figure 4shows the evaluation of the improved AVHRR
cloud mask, where the agreement with MODIS Aqua is higher than 90% compared to an average
60% agreement for the CLAVR cloud mask. Figure 5shows the time-series evolution of the surface
reflectance of channel 1 (blue) and channel 2 (red), as well as the NDVI (green) over one BELMANIP2
site (see Section 2.3.4 for a description of the BELMANIP2 sites) located in Madagascar, using the
CLAVR cloud mask (Figure 5a) and the LCDR cloud mask (Figure 5b). These plots show a strong
reduction of noise when using the LCDR cloud mask in channel 1 (from 0.05 to 0.01), channel 2 (from
0.07 to 0.03), and the NDVI (from 0.08 to 0.05).
Remote Sens. 2017,9, 296 7 of 14
Remote Sens. 2017, 9, 296 7 of 14
Figure 4. Evaluation of the global performance of the current cloud mask for NOAA16-AVHRR
versus the MODIS Aqua cloud mask. Results are reported as percentages. The left side is the CLAVR
algorithm [14]. The right side is the current LCDR improved cloud mask. The 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.
(a)
(b)
Figure 5. 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 (original
data) and of the cloud filtered time series (QA mask for CLAVR, New2 mask for the 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.
Figure 4.
Evaluation of the global performance of the current cloud mask for NOAA16-AVHRR
versus the MODIS Aqua cloud mask. Results are reported as percentages. The left side is the CLAVR
algorithm [
14
]. The right side is the current LCDR improved cloud mask. The 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.
Remote Sens. 2017, 9, 296 7 of 14
Figure 4. Evaluation of the global performance of the current cloud mask for NOAA16-AVHRR
versus the MODIS Aqua cloud mask. Results are reported as percentages. The left side is the CLAVR
algorithm [14]. The right side is the current LCDR improved cloud mask. The 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.
(a)
(b)
Figure 5. 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 (original
data) and of the cloud filtered time series (QA mask for CLAVR, New2 mask for the 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.
Figure 5.
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 (original
data) and of the cloud filtered time series (QA mask for CLAVR, New2 mask for the 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.
Remote Sens. 2017,9, 296 8 of 14
3.4. Surface Reflectance Accuracy Assessment
We have analyzed a comprehensive estimate of the performance of the AVHRR surface feflectance
for 1999 over the AERONET sites [
15
]. The performance was evaluated along with Pathfinder AVHRR
Land (PAL) daily products [
23
] over 48 sites distributed across the globe [
9
]. Atmospheric data
from AERONET sun photometers at each site [
17
] were used as the input to the 6S radiative transfer
model [
13
] to atmospherically correct the top of the atmosphere AVHRR data to determine surface
reflectance values for channels 1 and 2. Figure 6a shows that the AVHHR data for channel 1 follow
the one-to-one line very closely. Similarly, Figure 6b shows the AVHRR results for channel 2, with
good correlation for surface reflectance values up to ~0.5, although the PAL data are further from the
1-to-1 line.
Remote Sens. 2017, 9, 296 8 of 14
3.4. Surface Reflectance Accuracy Assessment
We have analyzed a comprehensive estimate of the performance of the AVHRR surface
feflectance for 1999 over the AERONET sites [15]. The performance was evaluated along with
Pathfinder AVHRR Land (PAL) daily products [23] over 48 sites distributed across the globe [9].
Atmospheric data from AERONET sun photometers at each site [17] were used as the input to the 6S
radiative transfer model [13] to atmospherically correct the top of the atmosphere AVHRR data to
determine surface reflectance values for channels 1 and 2. Figure 6a shows that the AVHHR data for
channel 1 follow the one-to-one line very closely. Similarly, Figure 6b shows the AVHRR results for
channel 2, with good correlation for surface reflectance values up to ~0.5, although the PAL data are
further from the 1-to-1 line.
(a) (b)
Figure 6. 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 sun
photometer, while the y-axis shows the surface reflectances retrieved from the AVHRR data using
current LCDR and PAL algorithms.
3.5. Direct Intercomparison of the Surface Reflectance Products
Figure 7 shows the cross-comparison of AVHHR data with MODIS over the BELMANIP2 sites.
The monthly averaged ratios of the observed (AVHRR data) and the predicted reflectance (MODIS
Aqua corrected reflectance at AVHRR spectral and directional conditions) for AVHRR channel 1
(Figure 7a) and channel 2 (Figure 7b) are plotted as a function of time [3]. The plots show a consistent
evolution of the ratios for the different sensors (NOAA16, NOAA18, and NOAA19) and for the two
channels with values close to one. It should be noted that, at the beginning of each mission, there are
discrepancies between sensors [24] (beginning of the NOAA18 record with NOAA16 and beginning
of the NOAA19 record with NOAA18); this is expected during the outgassing period where both the
thermal bands are not stable and the calibration in the red and near infrared is evolving quickly.
Figure 6.
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 sun
photometer, while the y-axis shows the surface reflectances retrieved from the AVHRR data using
current LCDR and PAL algorithms.
3.5. Direct Intercomparison of the Surface Reflectance Products
Figure 7shows the cross-comparison of AVHHR data with MODIS over the BELMANIP2 sites.
The monthly averaged ratios of the observed (AVHRR data) and the predicted reflectance (MODIS
Aqua corrected reflectance at AVHRR spectral and directional conditions) for AVHRR channel 1
(Figure 7a) and channel 2 (Figure 7b) are plotted as a function of time [
3
]. The plots show a consistent
evolution of the ratios for the different sensors (NOAA16, NOAA18, and NOAA19) and for the two
channels with values close to one. It should be noted that, at the beginning of each mission, there are
discrepancies between sensors [
24
] (beginning of the NOAA18 record with NOAA16 and beginning of
the NOAA19 record with NOAA18); this is expected during the outgassing period where both the
thermal bands are not stable and the calibration in the red and near infrared is evolving quickly.
Remote Sens. 2017,9, 296 9 of 14
Remote Sens. 2017, 9, 296 9 of 14
(a)
(b)
Figure 7. 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).
3.6. Derived LAI/FAPAR Products
Using AVHRR surface reflectance, a LAI/FAPAR product (AVH15C1 product) was derived [25].
The algorithm relies on artificial neural networks (ANN) trained using MODIS LAI/FAPAR products
and AVHRR surface reflectance products, acquired over BELMANIP-2 sites from 2001 to 2007. A full
description of the algorithm and its evaluation process is given in [26]. Using different sites than the
ones used for training (DIRECT network [27], Figure 1), Figure 8 shows that the MODIS and AVHRR
LAI/FAPAR are well correlated (r2 ~ 0.9). However, a clear saturation effect is observed with high
FAPAR (>0.8) values. This saturation affects mainly deciduous forest, associated with a complex 3D
canopy [26].
Figure 7.
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).
3.6. Derived LAI/FAPAR Products
Using AVHRR surface reflectance, a LAI/FAPAR product (AVH15C1 product) was derived [
25
].
The algorithm relies on artificial neural networks (ANN) trained using MODIS LAI/FAPAR products
and AVHRR surface reflectance products, acquired over BELMANIP-2 sites from 2001 to 2007. A full
description of the algorithm and its evaluation process is given in [
26
]. Using different sites than the
ones used for training (DIRECT network [
27
], Figure 1), Figure 8shows that the MODIS and AVHRR
LAI/FAPAR are well correlated (r
2
~0.9). However, a clear saturation effect is observed with high
FAPAR (>0.8) values. This saturation affects mainly deciduous forest, associated with a complex 3D
canopy [26].
Remote Sens. 2017,9, 296 10 of 14
Remote Sens. 2017, 9, 296 10 of 14
Figure 8. Comparison of MODIS and AVHRR LAI (a) and FAPAR (b) from 2001 to 2007. Data were
extracted over DIRECT sites not used during the training process.
3.7. Agriculture Application
With the purpose of evaluating the applicability of the yield models to AVHRR data, we validate
the methods taking advantage of the AVHRR LTDR historical data from 1982 to 2014.
Figure 9 shows the validation of the method using the AVHRR LCDR data from 1982 to 2014.
Note that we removed from the analysis the year 2007 that was identified as a problem in [7], when
a late frost damaged most of the wheat crops in Kansas and Oklahoma. Comparing the statistics of
these figures with the statistics presented in [8], where the model is applied using MODIS data from
2001 to 2012, adding more years to the analysis and using Version 4 AVHRR surface reflectance data
barely affects the error, keeping it at around 7%. These results confirm the good performance of the
method, providing good results during the extreme years in terms of production. The statistics also
display the Nash–Sutcliffe model efficiency coefficient (E) proposed by [28]. It is defined as one minus
the sum of the absolute squared differences between the predicted (P) and observed (O) values,
normalized by the variance of the observed values during the period under investigation.
=−∑
−
∑
−
(1)
The range of E lies between 1.0 (perfect fit) and −∞. An efficiency of lower than zero indicates
that the mean value of the observed time series would have been a better predictor than the model.
Both the yield and the production show E values greater than zero.
(a) (b)
Figure 9. National winter wheat predicted yield (a) and production (b) in the U.S., applying the
‘original’ method [1] to AVHRR data plotted against USDA-reported statistics
(https://quickstats.nass.usda.gov).
Figure 8.
Comparison of MODIS and AVHRR LAI (
a
) and FAPAR (
b
) from 2001 to 2007. Data were
extracted over DIRECT sites not used during the training process.
3.7. Agriculture Application
With the purpose of evaluating the applicability of the yield models to AVHRR data, we validate
the methods taking advantage of the AVHRR LTDR historical data from 1982 to 2014.
Figure 9shows the validation of the method using the AVHRR LCDR data from 1982 to 2014.
Note that we removed from the analysis the year 2007 that was identified as a problem in [
7
], when a
late frost damaged most of the wheat crops in Kansas and Oklahoma. Comparing the statistics of these
figures with the statistics presented in [
8
], where the model is applied using MODIS data from 2001 to
2012, adding more years to the analysis and using Version 4 AVHRR surface reflectance data barely
affects the error, keeping it at around 7%. These results confirm the good performance of the method,
providing good results during the extreme years in terms of production. The statistics also display the
Nash–Sutcliffe model efficiency coefficient (E) proposed by [
28
]. It is defined as one minus the sum of
the absolute squared differences between the predicted (P) and observed (O) values, normalized by
the variance of the observed values during the period under investigation.
E=1−
∑n
i=1(Oi−Pi)2
∑n
i=1Oi−O2(1)
The range of E lies between 1.0 (perfect fit) and
−∞
. An efficiency of lower than zero indicates
that the mean value of the observed time series would have been a better predictor than the model.
Both the yield and the production show E values greater than zero.
Remote Sens. 2017, 9, 296 10 of 14
Figure 8. Comparison of MODIS and AVHRR LAI (a) and FAPAR (b) from 2001 to 2007. Data were
extracted over DIRECT sites not used during the training process.
3.7. Agriculture Application
With the purpose of evaluating the applicability of the yield models to AVHRR data, we validate
the methods taking advantage of the AVHRR LTDR historical data from 1982 to 2014.
Figure 9 shows the validation of the method using the AVHRR LCDR data from 1982 to 2014.
Note that we removed from the analysis the year 2007 that was identified as a problem in [7], when
a late frost damaged most of the wheat crops in Kansas and Oklahoma. Comparing the statistics of
these figures with the statistics presented in [8], where the model is applied using MODIS data from
2001 to 2012, adding more years to the analysis and using Version 4 AVHRR surface reflectance data
barely affects the error, keeping it at around 7%. These results confirm the good performance of the
method, providing good results during the extreme years in terms of production. The statistics also
display the Nash–Sutcliffe model efficiency coefficient (E) proposed by [28]. It is defined as one minus
the sum of the absolute squared differences between the predicted (P) and observed (O) values,
normalized by the variance of the observed values during the period under investigation.
=−∑
−
∑
−
(1)
The range of E lies between 1.0 (perfect fit) and −∞. An efficiency of lower than zero indicates
that the mean value of the observed time series would have been a better predictor than the model.
Both the yield and the production show E values greater than zero.
(a) (b)
Figure 9. National winter wheat predicted yield (a) and production (b) in the U.S., applying the
‘original’ method [1] to AVHRR data plotted against USDA-reported statistics
(https://quickstats.nass.usda.gov).
Figure 9.
National winter wheat predicted yield (
a
) and production (
b
) in the U.S., applying the ‘original’
method [1] to AVHRR data plotted against USDA-reported statistics (https://quickstats.nass.usda.gov).
Remote Sens. 2017,9, 296 11 of 14
Figure 10 shows the error evolution of the yield and the production when applying the [
8
] method
depending on the day of the forecast. Comparing this plot to the results published in [
8
] that was just
based on the MODIS-era time-series, shows that the inclusion of more days in the analysis provides
more stability in the error evolution. The plot also shows a horizontal line that represents the error if
we assume the yield/production equal to the time series average. In order to study the feasibility of
the model compared to assuming the average yield/production, Figure 10 displays the evolution of
the E coefficient. The yield forecast shows E positive values up from DOY 120 (30 April), while for the
production which is corrected by the official statistics of area, the E coefficient is positive from DOY
100 (10 April).
Remote Sens. 2017, 9, 296 11 of 14
Figure 10 shows the error evolution of the yield and the production when applying the [8]
method depending on the day of the forecast. Comparing this plot to the results published in [8] that
was just based on the MODIS-era time-series, shows that the inclusion of more days in the analysis
provides more stability in the error evolution. The plot also shows a horizontal line that represents
the error if we assume the yield/production equal to the time series average. In order to study the
feasibility of the model compared to assuming the average yield/production, Figure 10 displays the
evolution of the E coefficient. The yield forecast shows E positive values up from DOY 120 (30 April),
while for the production which is corrected by the official statistics of area, the E coefficient is positive
from DOY 100 (10 April).
(a) (b)
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; and (b) Nash–Sutcliffe model efficiency coefficient evolution depending on the day of the year
of the forecast.
We also used the AVH15C1 LAI and FAPAR products with the method devised in [7]. Figure 11
shows the yield validation with the official statistics. Comparing these results with the NDVI (Figure
11a), they show similar errors (8.07% NDVI, 8.17% LAI, and 6.98% FAPAR) and similar correlation
coefficients (0.38 NDVI, 0.46 LAI, and 0.46 FAPAR). Thus, we can conclude that the three different
parameters (NDVI, LAI, and FAPAR) provide equivalent results.
(a)
(b)
Figure 11. National winter wheat predicted yield in the U.S. applying [1] method to LAI (a) and
FAPAR (b) AVHRR data.
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; and (
b
) Nash–Sutcliffe model efficiency coefficient evolution depending on the day of the year
of the forecast.
We also used the AVH15C1 LAI and FAPAR products with the method devised in [
7
]. Figure 11
shows the yield validation with the official statistics. Comparing these results with the NDVI
(Figure 11a), they show similar errors (8.07% NDVI, 8.17% LAI, and 6.98% FAPAR) and similar
correlation coefficients (0.38 NDVI, 0.46 LAI, and 0.46 FAPAR). Thus, we can conclude that the three
different parameters (NDVI, LAI, and FAPAR) provide equivalent results.
Remote Sens. 2017, 9, 296 11 of 14
Figure 10 shows the error evolution of the yield and the production when applying the [8]
method depending on the day of the forecast. Comparing this plot to the results published in [8] that
was just based on the MODIS-era time-series, shows that the inclusion of more days in the analysis
provides more stability in the error evolution. The plot also shows a horizontal line that represents
the error if we assume the yield/production equal to the time series average. In order to study the
feasibility of the model compared to assuming the average yield/production, Figure 10 displays the
evolution of the E coefficient. The yield forecast shows E positive values up from DOY 120 (30 April),
while for the production which is corrected by the official statistics of area, the E coefficient is positive
from DOY 100 (10 April).
(a) (b)
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; and (b) Nash–Sutcliffe model efficiency coefficient evolution depending on the day of the year
of the forecast.
We also used the AVH15C1 LAI and FAPAR products with the method devised in [7]. Figure 11
shows the yield validation with the official statistics. Comparing these results with the NDVI (Figure
11a), they show similar errors (8.07% NDVI, 8.17% LAI, and 6.98% FAPAR) and similar correlation
coefficients (0.38 NDVI, 0.46 LAI, and 0.46 FAPAR). Thus, we can conclude that the three different
parameters (NDVI, LAI, and FAPAR) provide equivalent results.
(a)
(b)
Figure 11. National winter wheat predicted yield in the U.S. applying [1] method to LAI (a) and
FAPAR (b) AVHRR data.
Figure 11.
National winter wheat predicted yield in the U.S. applying [
1
] method to LAI (
a
) and
FAPAR (b) AVHRR data.
Remote Sens. 2017,9, 296 12 of 14
4. Discussion
In this work we present the improvements and assess the AVHRR BRDF-corrected surface
reflectance/NDVI Version 4 product. In addition to the geolocation and cloud mask evaluations,
the assessment is done through four different exercises: first, we compare the product with the
surface reflectance derived using AERONET atmospheric data (Section 3.4); second, we intercompare
the AVHRR with the MODIS surface reflectance products; third, we evaluate the LAI and FAPAR
downstream products; and fourth, we apply a method to the AVHRR historical surface reflectance
dataset to estimate the wheat production in the U.S.
The inter-comparison of MODIS and AVHRR surface reflectance products show ratios close to one,
which means that both time-series are consistent. However, the ratio still shows some noise (maximum
of 2% variation). The reasons for such errors could be associated with errors in the water vapor
correction, an error residual of the BRDF correction or even a systematic variation of the calibration
during the year. All of these possible explanations will be further explored in our future work.
Regarding the yield model, the method developed for MODIS data was evaluated with the
longer AVHRR historical record, which contains greater inter-annual variability in surface conditions
(generally winter wheat yields with lower values: see x-axis data variability of Figure 11a). Additionally,
the method was applied satisfactorily to AVHRR using the same calibration coefficients as for MODIS
and producing equivalent statistics, showing the comparability and consistency of the MODIS and
AVHRR surface reflectance products for this application.
5. Conclusions
This paper evaluated the AVHRR BRDF-corrected surface reflectance/NDVI Version 4 product.
We reviewed the various efforts developed to improve its accuracy, from the geolocation correction
and the cloud mask improvement to the calibration monitoring. Additionally, we evaluated the
performance of the product, first using AERONET data and also by inter-comparison with the
MODIS surface reflectance, an already validated and established product. The results presented
show good performance of the AVHRR product and consistency with MODIS. We also demonstrate
the usefulness and assess the performance of the product by its application to agricultural monitoring.
This agricultural application demonstrates the utility of the LCDR to test the robustness of the yield
forecast methods.
We are still working on the improvement of the product based on a better estimation of the
atmospheric constituents: the aerosols and water vapor content. Future work will also include the
development of a more systematic, robust and statistically significant evaluation of the product.
Acknowledgments:
The authors would like to acknowledge NOAA NCEI for supporting the development of
the surface reflectance/NDVI CDR product through the Cooperative Institute for Climate and Satellites-North
Carolina under Cooperative Agreement NA14NES432003.
Author Contributions:
B.F., E.F.V. and J.C.R. conceived the project idea; B.F., E.F.V., E.M., M.C. and J.N. processed
the data; B.F., E.F.V., J.C.R., I.B.R., C.J., J.N., I.C., D.M., F.B., E.M., R.W. and S.D. performed the analysis; B.F., E.F.V.,
J.C.R., I.B.R., C.J., M.C., F.B. and S.D. wrote and contributed to the manuscript.
Conflicts of Interest: The authors declare no conflicts of interest.
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2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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