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A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

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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.
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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
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 t) and −∞. An eciency 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(OiPi)2
n
i=1OiO2(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 t) and −∞. An eciency 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
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... The recently released Climate Data Records (CDR) NDVI products from AVHRR at 0.05 × 0.05°spatial resolution include improved surface reflectance inputs, corrections for known errors in time, latitude, and longitude variables, and improvements in cloud masking and calibration monitoring (Vermote 2019). Results from AVHRR CDR and Moderate Resolution Imaging Spectroradiometer (MODIS) data show the consistency of the AVHRR CDR dataset, as both datasets have similar error (~10%) (Franch et al. 2017). Thus, AVHRR CDR data could provide a novel way to assess macroecosystem dynamics across a near four-decade time series. ...
... We acquired nearly four decades of gridded NDVI (0.05 × 0.05°) data at the daily temporal scale from the NOAA CDR archive. These daily NDVI arrays were derived from the AVHRR Surface Reflectance products as described in Franch et al. (2017) and Vermote (2019). The methodology described herein employed the recently released Version 5 CDR NDVI products as this version's data utilized improved surface reflectance inputs as well as corrections for known errors in time, latitude, and longitude variables (Vermote 2019). ...
Article
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The Hawaiian Islands have been identified as a global biodiversity hotspot. We examine the Normalized Difference Vegetation Index (NDVI) using Climate Data Records products (0.05 × 0.05°) to identify significant differences in NDVI between neutral El Niño-Southern Oscillation years (1984, 2019) and significant long-term changes over the entire time series (1982–2019) for the Hawaiian Islands and six land cover classes. Overall, there has been a significant decline in NDVI (i.e., browning) across the Hawaiian Islands from 1982 to 2019 with the islands of Lāna’i and Hawai’i experiencing the greatest decreases in NDVI (≥44%). All land cover classes significantly decreased in NDVI for most months, especially during the wet season month of March. Native vegetation cover across all islands also experienced significant declines in NDVI, with the leeward, southwestern side of the island of Hawai’i experiencing the greatest declines. The long-term trends in the annual total precipitation and annual mean Palmer Drought Severity Index (PDSI) for 1982–2019 on the Hawaiian Islands show significant concurrent declines. Primarily positive correlations between the native ecosystem NDVI and precipitation imply that significant decreases in precipitation may exacerbate the decrease in NDVI of native ecosystems. NDVI-PDSI correlations were primarily negative on the windward side of the islands and positive on the leeward sides, suggesting a higher sensitivity to drought for leeward native ecosystems. Multi-decadal time series and spatially explicit data for native landscapes provide natural resource managers with long-term trends and monthly changes associated with vegetation health and stability.
... To this end, the NASA Long Term Data Record (LTDR) 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. The Version 4 contains improvements to geolocation, cloud masking and calibration, making the data record suitable for crop monitoring [27]. This product is still operational, and its usefulness has been demonstrated for a wide variety of applications such as snow cover estimation [28], agricultural modeling [27], Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) retrieval [29,30], global vegetation monitoring [31,32], burned area mapping [33] and albedo estimation products [34]. ...
... The Version 4 contains improvements to geolocation, cloud masking and calibration, making the data record suitable for crop monitoring [27]. This product is still operational, and its usefulness has been demonstrated for a wide variety of applications such as snow cover estimation [28], agricultural modeling [27], Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) retrieval [29,30], global vegetation monitoring [31,32], burned area mapping [33] and albedo estimation products [34]. ...
Chapter
National and International space agencies are determined to keep their fingers on the pulse of crop monitoring through Earth Observation (EO) satellites, which is typically tackled with optical imagery. In this regard, there has long been a trade-off between repetition time and spatial resolution. Another limitation of optical remotely sensed data is their typical discontinuity in time, caused by cloud cover or adverse atmospheric effects. Enduring clouds over agricultural fields can mask key stages of crop growth, leading to uncertainties in crop monitoring practices such as yield predictions. Gap-filling methods can provide a key solution for accurate crop phenology characterization. This chapter first provides a historical overview of EO missions dedicated to crop monitoring. Then, it addresses the rapidly evolving fields of gap-filling and land surface phenology (LSP) metrics calculation using a new in-house developed toolbox, DATimeS. These techniques have been put into practice for homogeneous and heterogeneous demonstration landscapes over the United States. Time series of Difference Vegetation Index (DVI) were processed from two EO data sources: high spatial resolution Sentinel-2 and, low spatial resolution MODIS data. LSP metrics such as start and end of season were calculated after gap filling processing at 1km resolution. Over the homogeneous area both S2 and MODIS are well able to capture the phenology trends of the dominant crop and LSP metrics were successfully mapped. Conversely, the MODIS dataset presented more difficulties than S2 to capture the phenology trend of winter wheat over heterogeneous landscape.
... Besides, a lock correction approach was proposed to mitigate the geolocation offset , which showed that the bias of the geolocation of AVHRR products was within two pixels. The quality of the AVHRR cloud mask also improved significantly and agreed with MODIS Aqua >90% (Franch et al., 2017). All of these improvements to the AVH02C1 facilitate the high accuracy and reasonable spatiotemporal variation of the long-term GLASS-AVHRR SLWR dataset. ...
Article
Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm⁻², and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm⁻² for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.
... Land-cover types that could not be assigned to one of these categories were considered missing data (Fig. 1, Supplementary Table 3), which included bare areas, wetlands, and glaciers. To represent the amount of active vegetation, we compiled yearly estimates of the Normalized Difference Vegetation Index (NDVI) CDR acquired from NOAA's National Centers for Environmental Information 65,66 . As the NDVI daily data availability coverage was heterogeneous (i.e., clustered missing values), a monthly average was first taken, from which a yearly average was calculated, with an equal contribution from each month. ...
Article
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Soil microorganisms are central to sustain soil functions and services, like carbon and nutrient cycling. Currently, we only have a limited understanding of the spatial-temporal dynamics of soil microorganisms, restricting our ability to assess long-term effects of climate and land-cover change on microbial roles in soil biogeochemistry. This study assesses the temporal trends in soil microbial biomass carbon and identifies the main drivers of biomass change regionally and globally to detect the areas sensitive to these environmental factors. Here, we combined a global soil microbial biomass carbon data set, random forest modelling, and environmental layers to predict spatial-temporal dynamics of microbial biomass carbon stocks from 1992 to 2013. Soil microbial biomass carbon stocks decreased globally by 3.4 ± 3.0% (mean ± 95% CI) between 1992 and 2013 for the predictable regions, equivalent to 149 Mt being lost over the period, or ~1‰ of soil C. Northern areas with high soil microbial carbon stocks experienced the strongest decrease, mostly driven by increasing temperatures. In contrast, land-cover change was a weaker global driver of change in microbial carbon, but had, in some cases, important regional effects.
... All data were kept in NETCDF format and processed with the SeaDAS 7.4 program. The spatial resolution of this product is 4 × 4 km, (Franch 2017). ...
Conference Paper
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This work has the objective to investigate the recent Sea Surface Temperature (SST) trends on southern Cuban shelves over the years 1982-2018 using monthly AVHRR SST NOAA product. This paper extends and updates the previous studies about SST on the Southern Cuban shelves with the aim of improving understanding of how global-scale climate changes translate into them and it could potentially help to better understand the influence of Sea Surface Temperature on mangroves deaths, coral bleaching, fisheries behavior and species displacement among others. The SST annual average has a value of 27.8784°C and a range of 22.1774-2.4022°C, for the western shelf, while for the eastern one it is 28.3395°C with a range of 23.4504-32.0313°C. The SST trend is 0.0168°C yr-1 and 0.0156°C yr-1 , for western and eastern shelves respectively. During the last 36 years, the SST in the southwestern shelf has increased by 0.725°C; while in the southeast it increases by 0.644°C. If the current conditions that force the behavior of the climate in the Caribbean region are sustained, by 2050 a SST of up to 1,348°C and 1,199°C could be reached in the southwestern and southeastern Cuban shelves.
... The NDVI data set compared very well with already validated and well-established MODIS products. In fact, the study of Franch et al. [48] found a good performance and consistency between the AVHRR and MODIS products, in particular, with respect to atmospheric and water vapor correction methods, but also with respect to error residuals of the Bidirectional Reflectance Distribution Function, and also to systematic calibration biases during the year. Figure 1 shows the long-term mean maps of NDVI and SMOS over northern South America and the location of the study regions, and Table 2 summarizes the data used in this study, including the temporal and spatial resolutions. ...
Article
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We evaluated the coupled dynamics of vegetation dynamics (NDVI) and soil moisture (SMOS) at monthly resolution over different regions of tropical South America and the effects of the Eastern Pacific (EP) and the Central Pacific (CP) El Niño–Southern Oscillation (ENSO) events. We used linear Pearson cross-correlation, wavelet and cross wavelet analysis (CWA) and three nonlinear causality methods: ParrCorr, GPDC and PCMCIplus. Results showed that NDVI peaks when SMOS is transitioning from maximum to minimum monthly values, which confirms the role of SMOS in the hydrological dynamics of the Amazonian greening up during the dry season. Linear correlations showed significant positive values when SMOS leads NDVI by 1–3 months. Wavelet analysis evidenced strong 12- and 64-month frequency bands throughout the entire record length, in particular for SMOS, whereas the CWA analyses indicated that both variables exhibit a strong coherency at a wide range of frequency bands from 2 to 32 months. Linear and nonlinear causality measures also showed that ENSO effects are greater on SMOS. Lagged cross-correlations displayed that western (eastern) regions are more associated with the CP (EP), and that the effects of ENSO manifest as a travelling wave over time, from northwest (earlier) to southeast (later) over tropical South America and the Amazon River basin. The ParrCorr and PCMCIplus methods produced the most coherent results, and allowed us to conclude that: (1) the nonlinear temporal persistence (memory) of soil moisture is stronger than that of NDVI; (2) the existence of two-way nonlinear causalities between NDVI and SMOS; (3) diverse causal links between both variables and the ENSO indices: CP (7/12 with ParrCorr; 6/12 with PCMCIplus), and less with EP (5/12 with ParrCorr; 3/12 with PCMCIplus).
... All data were kept in NETCDF format and processed with the SeaDAS 7.4 program. The spatial resolution of this product is 4 × 4 km, (Franch 2017). ...
... Compared with AVHRR images used in previous studies, such as Hori et al. (2017) and Zhou et al. (2013), the AVHRR-SR CDR has calibrates different NOAA polar orbiting instruments and provides consistent global daily average surface reflectance 120 and brightness temperatures, which facilitates their application in longterm snow cover mapping. Evaluation results of the AVHRR-SR CDR in the monitoring of United States wheat yield (Franch et al., 2017) and gap-free daily SCE generation over the Tibetan Plateau (Chen et al., 2018) demonstrated that the AVHRR-SR CDR were reliable in mapping of longterm terrestrial surface variables. Therefore, for purpose of the present study, the daily AVHRR-SR CDR for the period 1981-2019 at 0.05° spatial resolution is employed as the primary input data for GLASS SCE generation. ...
Preprint
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Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change due to its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among existing continental scale SCE products hampers its detailed studies. Using the latest Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free SCE covering the NH landmass for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. The development of GLASS SCE contains five steps. First, a decision tree algorithm with multiple threshold tests was applied to distinguish snow cover (NHSCE-D) with other land cover types from daily AVHRR-SR CDR. Second, gridcells with cloud cover and invalid observations were filled by two existing daily SCE products. The gap-filled gridcells were further merged with NHSCE-D to generate combined daily SCE over the NH (NHSCE-Dc). Third, an aggregation process was used to detect the maximum SCE and minimum gaps in each 8-day periods from NHSCE-Dc. Forth, the gaps after aggregation process were further filled by the climatology of snow cover probability to generate the gap-free GLASS SCE. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r = 0.61, p
... Spectral indices and biophysical attributes are further used to assess crop condition and development, and estimate crop yields and evapotranspiration. Remote sensing-based yield models are generally based on parametric and non-parametric empirical approaches (Balaghi et al., 2008;Becker-Reshef et al., 2010;Franch et al., 2017;Holzman et al., 2018;Johnson, 2014;Kamir et al., 2020). These approaches have been widely used because they are easier and faster to implement over large geographical areas compared to physical based process models and generally require much fewer inputs than process based crop models. ...
Article
Remote sensing derived datasets (e.g. Leaf Area Index (LAI)) are increasingly being used in process based cropping system models to improve the prediction skill of the simulations when implementing operationally at regional scale. However, challenges such as inadequate quality of the available remote sensing data products and high reliance of models on climate variables and their uncertainties still exist. To address these challenges, we developed Geo-CropSim, a spatial modeling framework to use high quality remote sensing products in the Environmental Policy Integrated Climate (EPIC) agroecosystem model to regulate simulated processes and improve predictions of crop yield and evapotranspiration. Geo-CropSim comprises three main features 1) pixel level model initialization using crop emergence dates; 2) ability of the EPIC model to read in the PROSAIL (i.e. combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model) inversion-based crop type LAI; and 3) a stress adjustment function to regulate simulated stress using LAI anomalies. To understand its performance, we implemented it over the State of Nebraska to estimate corn (Zea mays L.) and soybean (Glycine max [Merr.]) yields and evapotranspiration (ET) for 2012 (drought year) and 2015 (normal year) at 500-m resolution. Results showed that emergence dates and seasonal LAI captured spatial and temporal differences in crop progression (e.g. delayed planting in 2015) and growth (e.g. declined LAI in 2012) driven by regional differences in crop management and weather conditions very well. These differences were reflected in Geo-CropSim yield estimates, and showed improved spatial and temporal details compared over those from EPIC simulations obtained without using remote sensing derived emergence and LAI. Results revealed that Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of Geo-CropSim yield estimates, computed based on USDA-NASS reported yields, were 18.85% and 1.22 Mg ha⁻¹ for corn, and 17.90% and 0.46 Mg ha⁻¹ for soybeans, respectively, which are substantially lower than those of original EPIC estimates (MAPE = 33.74% and RMSE = 2.18 Mg ha⁻¹ for corn; and MAPE = 40.71% and RMSE = 0.98 Mg ha⁻¹ for soybeans). Further, Geo-CropSim was able to capture ET and transpiration dynamics reasonably well (e.g. 10–12 % lower values for soybeans compared to corn values), and showed good agreement with flux measurements (i.e. R² values of 0.63 and 0.72, RMSE values of 29.88 and 33.41 mm, and MAPE values of 5.0% and 6.8% for corn and soybean, respectively). Overall, this study demonstrated that Geo-CropSim has considerable potential to serve as a reliable operational tool to assess crop yields and water use under various cropping systems and to help in regional yield monitoring and water resource management.
Chapter
This chapter gives a theoretical overview of various contact, proximal and remote monitoring solutions available for precision agriculture. Visual inspection of crop damage, which can be detected using these sensors, are introduced at first. Precision agriculture methodologies and sensors are reviewed with particular emphasis on variable rate fertilization. Different sensor platforms reviewed in the chapter ranged from drone images to tractor-mounted and hand-held devices, including the overview of autonomous platforms and robots in precision agriculture. After the theoretical overview a couple of use-cases are described to illustrate the most common practices of using proximal sensing sensors for precision agriculture. The use-case from Estonia demonstrates hand-held proximal sensor usage for variable rate fertilization. The use-cases from Lithuania illustrate field-scale monitoring and mapping of soil characteristics.KeywordsPrecision agricultureVariable rate fertilizationN-sensorN-testerSoil sensingCrop sensingRobot platformChlorophyllElectrical conductivity
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In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.
Article
With the launch of NASA's Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities.
Article
Records of top-of-the-atmosphere albedo over several sites around the globe indicate that the formulae given in Rao and Chen (1996) to determine the post-launch calibration of the visible (channel 1, 0.58-0.68 mu m) and near-infrared (channel 2, 0.72-1.1 mu m) channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft overestimate the in-orbit degradation of the two channels, resulting in spurious upward trends in the albedo time series. Therefore, the calibration formulae have been revised to minimize the upward trends, utilizing a 3-year (1995-1997) record of albedo measurements over a calibration site (21-23 N, 28-29 E) in the southeastern Libyan desert. Formulae for the calculation of the revised calibration coefficients as a function of elapsed time in orbit are given. The revised calibration formulae presented here, and those presented in Rao and Chen (1996), yield radiance/albedo values within 5% (relative) of each other for about 900 days after launch in channel 1 and for about 500 days in channel 2.
Article
An algorithm for the remote sensing of global cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Ad- ministration (NOAA) polar-orbiting satellites has been developed. The CLAVR-1 (Clouds from AVHRR-Phase I) algorithm classifies 2 3 2 pixel arrays from the Global Area Coverage (GAC) 4-km-resolution archived database into CLEAR, MIXED, and CLOUDY categories. The algorithm uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The various tests and the derivation of their thresholds are presented. CLAVR-1 has evolved through experience in applying it to real- time NOAA-11 data, and retrospectively through the NOAA AVHRR Pathfinder Atmosphere project, where 16 years of data have been reprocessed into cloud, radiation budget, and aerosol climatologies. The classifications are evaluated regionally with image analysis, and it is concluded that the algorithm does well at classifying perfectly clear pixel arrays, except at high latitudes in their winter seasons. It also has difficulties with classi- fications over some desert and mountainous regions and when viewing regions of ocean specular reflection. Generally, the CLAVR-1 fractional cloud amounts, when computed using a statistically equivalent spatial co- herence method, agree to within about 0.05-0.10 of image/analyst estimates on average. There is a tendency for CLAVR-1 to underestimate cloud amount when it is large and to overestimate it when small.
Article
: The AVHRR SST Pathfinder Project began in 1991 with the initial formulation of Version 1. Over the years, improvements have been introduced, e.g. product resolution, quality testing, navigation and SST retrieval accuracy, that have led to a series of versions covering the NOAA-7 through NOAA-18 (1981-present) satellites. During this period Pathfinder has been supported through various grants by NOAA and NASA while the product development and production was undertaken at the Rosenstial School of Marine and Atmospheric Science and product dissemination was provided by the JPL PO.DAAC and the National Oceanographic Data Center, NODC. The AVHRR Pathfinder project is now entering the next phase where production of the Pathfinder SST is being transitioned to NODC to insure future availability and when Version 6 of Pathfinder will be introduced. Recent experience with MODIS derived SST using a new approach to retrieval equation coefficient estimation has reduced the retrieval standard deviation from order 0.5K to less than 0.4K. Introduction of the Reynolds ¼ degree, daily OI SST as the reference SST field has improved Pathfinder data quality and preserved SST observations in high gradient regions. These changes will be included as part of Version 6 together with use of Level 1b obtained from the NOAA CLASS system augmented by improved navigation derived from accurate clock correction and attitude information. Version 6 will produce a ‘skin SST’ properly reflecting the AVHRR radiometric observations. In addition, per pixel uncertainty estimates and time of observation will be included in the product fields to be consistent with the GHRSST (Group for High Resolution SST) standards. The transition to NODC for sustained, long term generation of the AVHRR Pathfinder is based on the selection of the NASA SeaDAS code base that currently supports a variety of ocean color sensors as well as the EOS MODIS SST. Addition of AVHRR to this code base results in a supportable, widely adopted SST production capability that is readily distributed to the ocean color and SST communities. NODC will remain the central focus for Pathfinder product distribution. The transition activity is supported by the NOAA Scientific Data Stewardship (SDS) program. A companion grant from the NASA MEaSUREs program, P. Cornillon, URI, R. Evans, RSMAS, is supporting extension of the Pathfinder SST to include high-resolution 1km data set where available. This extension also will enable the processing of the global 1km METOP AVHRR FRAC data and provide products consistent with the MODIS global 4.63 SST maps. Support for NOAA-19 will be added as well.
Article
Methods for absolute calibration of visible and near-infrared sensors using ocean and cloud views have been developed and applied to channels 1 (red) and 2 (near-infrared) of the Advanced Very High Resolution Radiometer (AVHRR) for the NOAA-7, -9 and -11 satellites. The approach includes two steps. First step is intercalibration between channels 1 and 2 using high altitude (12 km and above) bright clouds as "white' targets. This cloud intercalibration is compared with intercalibration using ocean glint. The second step is an absolute calibration of channel 1 employing ocean off-nadir view (40-70°) in channels 1 and 2 and correction for the aerosol effect. -from Authors