remote sensing
Article
Assessing within-Field Corn and Soybean Yield Variability
from WorldView-3, Planet, Sentinel-2, and Landsat 8
Satellite Imagery
Sergii Skakun 1,2,3, * , Natacha I. Kalecinski 1 , Meredith G. L. Brown 1,4 , David M. Johnson 5 , Eric F. Vermote 3 ,
Jean-Claude Roger 1,3 and Belen Franch 1,6
1
2
3
4
5
6
*
Citation: Skakun, S.; Kalecinski, N.I.;
Brown, M.G.L.; Johnson, D.M.;
Vermote, E.F.; Roger, J.-C.; Franch, B.
Assessing within-Field Corn and
Soybean Yield Variability from
WorldView-3, Planet, Sentinel-2, and
Landsat 8 Satellite Imagery. Remote
Sens. 2021, 13, 872. https://doi.org/
10.3390/rs13050872
Academic Editor:
Enrico Borgogno Mondino
Received: 29 January 2021
Accepted: 23 February 2021
Published: 26 February 2021
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Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;
nkalecin@umd.edu (N.I.K.); mglbrown@umd.edu (M.G.L.B.); roger63@umd.edu (J.-C.R.);
belen.franch@uv.es (B.F.)
College of Information Studies (iSchool), University of Maryland, College Park, MD 20742, USA
NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA;
eric.f.vermote@nasa.gov
NASA Goddard Space Flight Center Code 610, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
National Agricultural Statistics Service, United States Department of Agriculture, 1400 Independence Ave SW,
Washington, DC 20250, USA; david.m.johnson@usda.gov
Physics of the Earth and Thermodynamics, University of Valencia, 46003 Valencia, Spain
Correspondence: skakun@umd.edu; Tel.: +1-301-405-2179
Abstract: Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High
Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and
Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating
timely and synoptic information on the yield status of crops across regional levels. However, the
coarse spatial resolution data inherent to these sensors provides little utility at the management
level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m)
alongside increased observational cadence (near daily) implies information on crops obtainable at
field and within-field scales to support farming needs is now possible. To test this premise, we focus
on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic,
Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their
spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics
to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa.
Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We
show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield
variability for corn and soybean. Our simulation results show that moving to coarser resolution
data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively.
We show that the most important spectral bands explaining yield variability were green (0.560 µm),
red-edge (0.726 µm), and near-infrared (NIR − 0.865 µm). Furthermore, the high temporal frequency
of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection
for yield map generation. Overall, we observed mixed performance of satellite-derived models with
the coefficient of determination (R2 ) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS
and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields,
suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other
biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions
are likely needed alongside the optical imagery to fully explain the yields.
Keywords: agriculture; yield; within-field; corn; soybean; remote sensing; satellite; WorldView-3;
planet; Sentinel-2; Landsat 8
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Remote Sens. 2021, 13, 872. https://doi.org/10.3390/rs13050872
https://www.mdpi.com/journal/remotesensing
Remote Sens. 2021, 13, 872
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1. Introduction
Crop yield assessment and forecasting is one of the major components in agriculture
production monitoring [1]. Timely, accurate, and spatially explicit information on crop
yields at global, national, and regional scales is extremely important [2] for understanding
and allocating the food supply. Furthermore, spatial variability of crop yields at the field
scale can help provide objective information to farmers to improve management practices
and identify yield gaps [3–5] or to improve insurance companies’ risk models [6].
The increased number of very high spatial resolution (VHR, <5 m) sensors onboard
space-borne platforms, including micro and nanosatellites, are providing opportunities for
a new generation of spatially detailed geo-information products for agricultural monitoring.
High temporal resolution (near daily cadence) is additionally required for agricultural applications to monitor and capture the dynamics of crop growth [7–10]. Images acquired by
the WorldView-3 (WV-3, ~1.25 m) satellite or the PlanetScope (Dove-Classic, ~3 m) constellation of satellites have finer spatial resolution compared to images provided by moderate
spatial resolution satellites such as Landsat 8 (30 m) [11] and Sentinel-2 (10–20 m) [12].
While WV-3 provides better spatial resolution and richer spectral information in visible
and near-infrared (NIR) spectrum (eight bands vs. four) compared to Planet/Dove, only
the latter provides a near-daily revisit cycle.
The combined spatial and temporal attributes from WorldView-3 and Planet can provide new insight on the variability of crop parameters at field scale, including yields [13].
Satellite data availability during the growing season can be used to predict before harvest
within-the-field crop yields or analyze areas of higher and lower yields for precision farming. It can also be used for stratification purposes within the sampling-based estimation
of yields [14,15]. The latter is especially important, since if satellite data could capture
within-field crop variability during the vegetation season, they would allow one to guide,
and potentially improve, the sampling strategies for ground-based crop yield estimation
and validation.
This is similar to the approach of using satellite-derived classification maps for guiding
sampling strategies to estimate areas of land cover land use change (LCLUC) [16,17].
However, in the case of LCLUC, one must deal with categorical information, while in the
case of yields, it is continuous information. To address that aspect, dedicated frameworks
such as VALidation of European Remote sensing Instruments (VALERI) [18] protocol
have been setup to validate satellite-derived maps of biophysical variables from coarse to
moderate spatial resolutions. The VALERI protocol introduces an elementary sampling unit
(ESU) corresponding to the spatial resolution of a satellite-derived map (e.g., a 30 × 30 m
pixel) and a number of point-based measurements (e.g., 10–15 samples per ESU). Thus, the
variability of the biophysical parameter inside the ESU can be captured.
Previous studies have attempted to connect remote sensing data (both reflectances and
vegetation indices (VIs)) acquired from space-borne VHR sensors with crop yields at field
scale and showed promising results [13,19–23]. Most studies used IKONOS, Quickbird,
SPOT5, or the RapidEye satellites. Even through VHR, each would only provide one or a
few image acquisitions throughout the season, which would bring large uncertainties in
terms of optimal timing for correlating satellite-derived features with yields.
Therefore, space-borne sensors at very high spatial and temporal resolution can
provide new insights and opportunities for precision agriculture [13,24] and further expand
applications of remote sensing sensors in agricultural domains at various geographical
scales. The present study takes advantage of VHR satellite data made available within
the National Aeronautics and Space Administration’s (NASA) program to explore the
strengths and the limitations of multi-spectral sensors to explain crop yield variability at
field scale. Specifically, the present study aims to address the following research questions:
•
•
Q1: How does the spatial resolution affect our ability to explain crop yields at
field scale?
Q2: To what degree can crop yield variability at field scale be explained by using
space-borne multi-spectral sensors only?
–
Remote Sens. 2021, 13, 872
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•
Q3: What is the optimal timing of satellite image acquisitions and optimal spectral
bands for explaining within-field variability?
The study was performed for 2018 and 2019 using VHR data (1–3 m), acquired by
PlanetScope (Dove-Classic) and WorldView-3 satellites. Additionally, use comparisons
were made to moderate spatial resolution data (30 m) as acquired by Landsat-8 and
Sentinel-2 satellites. The crops of corn and soybean were both analyzed with the study
sites being within Iowa, US. The within-field ground refence data were precision collected
by harvesting machines.
2. Materials and Methods
2.1. Study Area and Reference Yield Data
Corn and soybean yields were obtained for 30 fields in 2018 and 2019 from Iowa State
University. Fields were scattered throughout 7 counties in the state of Iowa, namely Boone,
Buena Vista, Hamilton, Jasper, Lucas, Pottawatomie, and Story (Figure 1). The detailed
characteristics of each field are described in Table A1. The yield data were collected by
combine harvesting machines, which provide dry volumetric yields roughly every three
meters along track. These yields were pre-processed before correlating them to the satellite
imagery. Headlands, row ends, and samples outside ±3 standard deviations from the field
mean were removed. In cases when several data records with different yield values were
within 3 m of each other, only the data record with the maximum yield was retained, while
others were filtered out [20,25,26].
Figure 1. Distribution of corn and soybean fields in Iowa, USA.
Average per-field yield values as well as planting and harvest dates are shown in
Figure 2. The average yield for corn was 12.1 t/ha and varied from 9.6 t/ha to 13.6 t/ha
and for soybean was 3.9 t/ha and varied from 2.4 t/ha to 4.5 t/ha. On average, corn and
soybean were planted on 4 May (day of year (DOY) 124) and 23 May (DOY 143). They
Remote Sens. 2021, 13, 872
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were harvested on 30 October (DOY 303) and 22 October (DOY 295), respectively. Figure 3
shows an example of within-field yield distribution for Coles corn and soybean fields
in 2019.
(a)
(b)
Figure 2. Per-field average yield values along with median (solid line) and 10 and 90 percentiles (dashed lines) and planting
(bottom circles) and harvest (top circles) dates for corn (a) and soybean (b) fields represented as day of year (DOY).
NASA’s
Figure 3. Example of yield distribution over the Coles fields in
2019 as acquired by combine harvester.
Earth’s
μm)
μm),
μm),
μm),
μm),
μm),
μm
μm),
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2.2. Satellite Data
2.2.1. Satellite Data at Very High Spatial Resolution (VHR): WorldView-3 and Planet
WorldView-3 (WV-3) data were provided by the NASA’s Commercial Archive Data
for NASA investigators (cad4nasa.gsfc.nasa.gov accessed on 29 January 2021) [27]. WV-3
acquires data of the Earth’s surface in eight spectral bands in the visible and infrared range
of electromagnetic spectrum: coastal blue (0.426 µm), blue (0.479 µm), green (0.552 µm),
yellow (0.610 µm), red (0.662 µm), red-edge (0.726 µm), and two near-infrared (NIR1
0.831 µm and NIR2 0.910 µm)’ channels. At-nadir spatial resolution is 1.25 m. WV-3 also
acquires data in eight short-wave infrared bands (ranging from 1195 nm to 2365 nm), but
those were unavailable and, hence, not used. Since only archival images were selected and
no new acquisitions were available, we were able to obtain two WV-3 images over the Coles
fields, for which within-fields yields were available (Figure 4). Though the timing of the
WV-3 images (2 and 21 July 2018) might not have been optimal, those data were still useful
in analyzing WV-3 spectral bands at 1.25 m spatial
resolution to explain within-field corn
“6SV”
and soybean yield variability early in the season (planting dates were 16 May and 6 June
2018 for corn and soybean, respectively). Images’ digital numbers were converted to the topof-atmosphere (TOA) reflectance values and further processed for atmospheric correction.
We used an adapted version of the Land Surface Reflectance Code (LaSRC), previously
developed for the moderate spatial resolution satellites such as Landsat 8 [28] and Sentinel2 [29]. LaSRC is a generic atmospheric correction algorithm for estimating land surface
reflectance (SR), which takes into account absorption by atmospheric gases and scattering
by molecules and aerosols. It is based on the inversion of the “6SV” radiative transfer code.
The result of the atmospheric correction is the estimate of SR in the corresponding spectral
bands of WV-3 satellite.
(a)
(b)
(c)
Figure 4. Example of WorldView-3 (WV-3) images acquired over the Coles corn field on 2 July 2018. True color composite
red-green-blue (a); false color composite near infrared (NIR)-red-green (b); corn yields (c). © 2021 DigitalGlobe, Inc., a
Maxar company, NextView License.
Planet data, acquired by Dove-Classic sensors, were acquired through the NASA
program [30].NASA’s
PlanetScope is composed of a constellation of satellites that provide a neardaily global coverage of the Earth surface [31]. Dove-Classic sensors acquire data in the
four spectral bands: blue (0.485 µm), green (0.545 µm), red (0.630 µm), and near-infrared
(0.820 µm) at 3.25 m spatial resolution. We acquired all cloud-free Planet data over the
fields for the whole growing season in 2018 and 2019 and an SR product provided by Planet.
There were 1167 and 1095 unique scenes, respectively, for those two years.
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2.2.2. Satellite Data at Moderate Spatial Resolution: Landsat 8 and Sentinel-2
We used NASA’s Harmonized Landsat Sentinel-2 (HLS, ver. 1.4) product at 30 m [32]
to correlate satellite-derived features with crop yields. HLS is an analysis ready data
product, which is derived from original TOA reflectance data by applying a series of
pre-processing steps, including co-registration, cloud screening, atmospheric and angular
corrections, and spectral adjustments. This ensures that data acquired by Landsat 8 and
Sentinel-2 satellites are geometrically and radiometrically harmonized and can be used
together. Combination of Landsat 8 and Sentinel-2A/B satellites provides a global median
average revisit interval of 2.9 days [33] and already proved to be beneficial for crop yield
assessment [9]. HLS is organized using a Sentinel-2 tiling system with each tile covering
the area of approximately 110 km × 110 km. We used the following tiles, which covered
corn and soybean fields in the study region: 15TVH, 15TVG, 15TVF, 15TUH, and 15TUF.
We used the following spectral bands: green (wavelength ~0.560 µm), red (~0.660 µm),
NIR (~0.865 µm from the 8A band of Sentinel-2), and two short-wavelength infrared
(SWIR) (~1.5 and ~2.2 µm). Overall, 407 and 400 unique HLS scenes were used in 2018 and
2019, respectively.
2.3. Methods
First, we explored the effect spatial resolution has on capturing field-scale yield
variability. For this, the harvest data from machinery were used to simulate how much
yield variability is reduced when spatial resolution decreases. Yield data from the combine
machinery were again available approximately every 3 m. Yield maps were generated at
spatial resolutions of 10 m, 20 m, and 30 m by averaging all the point yield data within
the coarser pixel. Relative efficiency, which is the ratio between yield variance at coarse
resolution (10 m, 20 m, or 30 m) to yield variance at 3 m resolution, was used as a numerical
metric for yield variability evaluation:
R( x ) = Vy ( x )/Vy ( x0 ),
(1)
where R( x ) is the relative efficiency at spatial resolution x m, Vy ( x ) is the variance of yield
at x m, and x0 = 3 m. This metric is similar to the metric used in land area estimation
and in quantifying the efficiency of satellite data in reducing the variance of the area
estimates [16,34].
We also used the coefficient of variation (CV) to measure a relative variability of yields
within the pixel or a field:
σy
CV = 100% ×
,
(2)
µy
where σy and µy are standard deviation and average of yields y.
In order to explore the yield variability at field scale, the reference within-field corn
and soybean yield values were correlated with the satellite-derived features of SR or VIs
using a multivariable linear regression (Table 1). For 1.25 m WV-3 and 3 m Planet data,
those features were derived for each yield sample separately, while for 30 m HLS data,
those yield values were averaged within each 30 m pixel. Specifically, for each cloud-free
satellite acquisition, a linear regression model was built between yields and SRs or VIs,
and an adjusted coefficient of determination R2adj and a root mean square error (RMSE)
were calculated:
2
∑in=1 yest − yre f
R2 = 1 −
(3)
2 ,
n
∑i=1 yre f − yre f
R2adj = 1 − 1 − R2
RMSE =
r
n−1
,
n− p−1
2
n
1/n ∑i=1 yest − yre f ,
(4)
(5)
Remote Sens. 2021, 13, 872
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RRMSE = 100% × RMSE/yre f ,
(6)
where yest and yre f are estimate and reference yield values, respectively; yre f = 1/n ∑in=1 yre f
is the average of the reference values, n is the number of samples, and p is the number of
variables (without the constant term).
Table 1. Vegetation indices used in the study.
Vegetation Index
Formula
Normalized difference vegetation index
(NDVI)
ρ N IR −ρ RED
ρ N IR +ρ RED
Difference vegetation index (DVI)
ρ N IR − ρ RED
Enhanced vegetation index (EVI)
RED
2.5 ρ N IRN+IR2.4ρRED
+1
Green NDVI (GNDVI)
Green chlorophyll vegetation index (GCVI)
ρ N IR −ρGREEN
ρ N IR +ρGREEN
ρ N IR
ρGREEN − 1
Wide dynamic range vegetation index
(WDRVI)
0.1ρ N IR −ρ RED
0.1ρ N IR +ρ RED
ρ
Soil-adjusted vegetation index (SAVI)
Red edge chlorophyll index (CIre)
1
1
−ρ
ρ N IR −ρ RED
ρ N IR +ρ RED + L (1 +
ρ N IR
ρ RED−EDGE
L), L = 0.33
−1
For WV-3 data.
For Planet and HLS data, the best date in terms of maximum R2adj was determined
to correlate with crop yields. For WV-3, no time series was available, thus we focused on
exploring various spectral bands using a “feature importance” metric derived from the
random forest regression [35]. This was done through multiple random permutations of
input features, and regression was evaluated in terms of information gain. Features that
provided higher informativeness had a higher importance level.
3. Results
3.1. Effect of Spatial Resolution on Capturing Field Scale Yield Variability
Figure 5 shows the effect of spatial resolution on capturing within-field yield variability
using a corn field as an example. These results show the difference between high spatial
resolution data compared to moderate spatial resolution of 10–30 m. For example, only 59%
of yield variability can be explained with 30 m, while the rest is lost because of coarsening
and mixture effects inside the 30 m pixel.
At present, one of the popular approaches for collecting reference crop yields is
through the crop cuts [14,15]. At fine spatial scales (usually at less than 1 m), destructive
harvesting of the crops is made, and the yield is estimated from the collected grain. Those
yields are then used as reference and compared to satellite-derived yields at moderate
spatial resolution. However, sampling within the moderate resolution pixel should be
statistically rigorous, thus the selected samples would capture yield variability across the
moderate resolution pixel, and therefore the estimates would be unbiased. Figure 6 shows
a histogram of the CV (Equation (2)) for the yields at 10 m, 20 m, and 30 m resolutions.
CV exceeds 20% and 10% at 30 m and 10 m, respectively. Our results are in line with
the previously developed VALERI protocol [18]. Therefore, a similar protocol shall be
implemented for any sample-based ground measurements of crop yields, when multiple
samples should be selected inside a 10 or 30 m pixel to capture yield variability. The VALERI
protocol recommends 10–15 samples of in-situ ground measurements of the biophysical
variable inside the pixel. One cannot make a single crop cut inside a 30 m pixel and
compare to the satellite-derived yields at moderate spatial resolution.
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5. Dependence of the relative efficiency, the metric showing how well one can capture yield variability, on
Figure 5. Dependence of the relative efficiency, the metric showing how well one can capture yield variability, on spatial
resolution. These results were obtained by simulating yields from the harvester machinery at various spatial resolutions.
Figure 6. Histogram of coefficient of variation of yields inside a moderate spatial resolution pixel (10,
20, and 30 m).
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3.2. Analysis of Corn and Soybean Yield Varibility with WV-3 Data
Table 2 shows values of the coefficient of determination, RMSE values obtained for
the linear models correlating corn and soybean yields, and SR values derived from WV-3
images. A model based on WV-3 image acquired on 21 July 2018 was able to explain 48%
of corn (RMSE = 1.07 t/ha or 9.2%) and 36% of soybean (RMSE = 0.47 t/ha or 13.2%) yield
variability. This was 66 and 45 days after corn and soybean planting, respectively. The SRbased model outperformed a single-regressor model based on the VI. For corn, the best one
was with the red edge chlorophyll index (Cire) and gave R2 = 0.44 and RMSE = 1.11 t/ha
(9.5%) (Figure 7). For soybean, the best VI-based model was with the difference vegetation
index (DVI) and gave R2 = 0.25 and RMSE = 0.50 t/ha (14.3%). Overall, VIs based on the red
spectral band were not able to explain corn yield variability (the best was Wide dynamic
range vegetation index (WDRVI) at 19%), whereas the green-based index Green chlorophyll
vegetation index (GCVI) and the red-edge-based index CIre were able to explain 37% and
44% of yield variability, respectively.
Table 2. Coefficient of determination R2 obtained for the Coles fields for 2018 when using linear
models between yields and
𝑅2 surface reflectance (SR) values (all spectralas a function of the day of year
bands) for the WV-3 images.
Crop
Corn
Corn
Soybean
Soybean
Acquisition Date
𝑹𝟐
2 July 2018
21 July 2018
2 July 2018
21 July 2018
R2
RMSE, t/ha
RRMSE, %
0.35
0.48
0.28
0.36
1.23
1.07
0.50
0.47
10.5
9.2
14.2
13.2
RMSE: root mean square error (Equation (5)); RRMSE: relative RMSE (Equation (6)).
Figure 7. Dependencies between corn yields and NDVI, WDRVI, GCVI, and CIre as derived from the WV-3 imagery
acquired on 21 July 2018, 66 days after the planting date. Number of samples is 17,802.
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Not all spectral bands were equally important in explaining corn and soybean variability. Figure 8 shows performance of various models and relative importance of spectral
bands. Results suggest that, during the stage of corn development when a WV-3 image was
taken, in this case, 66 days after planting, the red-edge (0.726 µm), the green (0.552 µm),
and the NIR (0.831 µm) bands were the most important in explaining yield variance at
field scale. These are in accordance with results obtained for VIs, when red-edge and
green-based indices outperformed red-based indices.
Figure 8. Relative importance of WV-3 spectral bands when correlating them with corn yields. WV-3 was acquired on 21
July 2018. Shown also is R2 for the linear model between SR values and corn yields.
3.3. Analysis of Corn and Soybean Yield Varibility with Planet and HLS Data
Unlike with WV-3, Planet and HLS provided a dense time series of multi-spectral
images, which allowed the determination of R2 optimized timing to correlate satellitederived features with yields. Figure 9 shows examples of how R2 changes with time for
some of the corn and the soybean fields in 2018 and 2019. Results show that, for some
fields, a clear peak was visible, and R2 reached values up to 0.8, suggesting possibility to
explain up to 80% of within-field yield variability, whereas, for other fields, the maximum
seasonal R2 was around 0.1–0.2, suggesting that multi-spectral HLS and Planet data failed
to explain within-field yield variability.
Table 3 shows DOY values for which maximal was R2 achieved and its correspondence
to the–harvesting date. The maximal correlation was observed 90–100 days (3 months)
before corn harvest and 70–80 days (2–2.5 months) before soybean harvest.
–
–
–
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(a)
(b)
(c)
(d)
Figure 9. Coefficient of determination R2adj as a function of the day of year for the best and the worst fields using HLS- and
Planet-derived SR values to correlate with yields: (a) corn in 2018; (b) soybean in 2018; (c) corn in 2019; (d) soybean in 2019.
Table 3. Day of year (DOY) for which the maximal R2 was achieved when correlating satellite-derived
SR against crop yields along with crops harvesting dates. HLS: Harmonized Landsat Sentinel-2.
Crop
Year
Harvest
HLS
Planet
Corn
Corn
Soybean
Soybean
2018
2019
2018
2019
298 ± 8
307 ± 12
297 ± 6
292 ± 11
195 ± 19
206 ± 22
209 ± 20
227 ± 24
206 ± 22
216 ± 26
221 ± 16
223 ± 22
Overall, the variable performance in terms of R2 was observed when using SR and
VI satellite-derived features to explain corn and soybean yield variability (Figure 10).
Best results for HLS and Planet were obtained using a linear model incorporating all
spectral bands. This outperformed models utilizing vegetation indices. For HLS, the
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SR-based models gave R2 = 0.62 ± 0.16 (RMSE = 0.95 ± 0.34 t/ha) and 0.50 ± 0.20
(RMSE = 0.39 ± 0.15 t/ha) for corn and soybean, respectively. Average and standard deviation values of R2 and RMSE were calculated for all corn and soybean fields in 2018 and
2019. The best VIs models were those based on green and NIR spectral bands such as
the WV-3 case. Both GCVI and GNDVI gave R2 = 0.48 ± 0.20 (RMSE = 1.07 ± 0.47 t/ha)
and 0.35 ± 0.22 (RMSE = 0.46 ± 0.17 t/ha) for corn and soybean, respectively. Similar
results were achieved for Planet, though the difference between the SR-based model and
the best VIs models was less prominent, 0.32 ± 0.12 (RMSE = 1.92 ± 0.56 t/ha) compared to
0.29 ± 0.13 (RMSE = 1.98 ± 0.57 t/ha) for corn and 0.29 ± 0.20 (RMSE = 0.79 ± 0.41 t/ha)
2 0.30 ± 0.23 (RMSE = 0.80 ± 0.41 t/ha) for soybean. Overall, R2 values for
compared 𝑅to
adj
corn were higher than those for soybean. Addition of SWIR bands to blue, green, red, and
NIR bands in the HLS-based models slightly improved R2 performance from 0.59 ± 0.17
and 0.62 ± 0.16 for corn and from 0.47 ± 0.20 and 0.50 ± 0.20 for soybean.
(a)
(b)
(c)
(d)
2
Figure 10. Median along with 10th and 90th percentile values of the maximum of coefficient of determination R2adj for𝑅adj
various HLS- and Planet-derived features: (a)–(c)
for
corn
and
(b)–(d)
for
soybean.
–
–
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R2 values obtained for HLS were higher than those obtained for Planet because of
different scales employed (3 m vs. 30 m, or an order of 10 × 10 = 100). Averaging 3 m yield
values to the 30 m HLS resolution reduces the variance of yield (Figure 5) that could be
explained with satellite data, therefore increasing the coefficient of determination. Overall,
a strong correlation between HLS- and Planet-based models was observed (Figure 11),
suggesting consistency of results achieved with HLS and Planet data. In other words, the
same set of fields gave higher or lower R2 values for both Planet and HLS.
Figure 11. Comparison of R2 for best models between corn and soybean yields and SR values
obtained with HLS and Planet data.
We found a strong correlation between R2 and field-averaged corn and soybean yields
(Figure 12). As the yield values increased, the performance of HLS- and Planet-based
models to explain within-field yield variability decreased. Such dependence was stronger
for soybean fields than for corn. R2 values were less than 0.5 for corn and soybean fields
with yields larger than 12.5 t/ha and 3.5 t/ha, respectively. These results show limitations
in using multi-spectral satellite imagery in explaining within-field corn and soybean yield
variability, especially for fields with large yield values. We did not find strong correlations
between R2 and yield variance or CV.
(a)
(b)
Figure 12. Cont.
Remote Sens. 2021, 13, 872
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(c)
(d)
Figure 12. Dependence of the coefficient of determination R2 for the best HLS- and Planet-based models on field yield: –
–
(a)–(b) for corn and (c)–(d) for soybean.
4. Discussion and Conclusions
This study aimed at investigating the efficiency of multi-spectral satellite imagery at
various spatial and temporal resolutions to explain within-field yield variability for corn
and soybean crops. First, we explored the effect of spatial resolution on yield variability.
We found that reducing spatial resolution from 3 m to 30 m reduced yield variance at field
scale with relative efficiency (Equation 1) decreasing from 1.0 to 0.6 (Figure 5), therefore
decreasing capabilities of capturing within-field yields with moderate spatial resolution
sensors. At the same time, the variance of yields inside 10, 20, or 30 m pixels increased up
to 20% (Figure 6). These results are consistent with previous recommendations established
within the VALERI protocol [18] to validate coarse and moderate resolution remote sensing
products and have implications on collecting ground-based– crop yields, for example,
–
through crop cuts [14,15]. When validating coarse (100 m–1 km) and moderate (10–30 m)
– must ensure that variability inside the pixel is captured
resolution crop yield maps, one
through multiple (10–15) point-based samples.
In order to detect the optimal timing of correlating satellite-derived features with
corn and soybean yields, a high temporal resolution of satellite data was required. –This
was achieved through the use of near-daily Planet data and a combination of 3–5 day
–
revisit cycles of Sentinel-2 and Landsat 8 combined [33]. The maximal correlations for
corn and soybean were obtained at 3 and 2–2.5 months, respectively, which is consistent
with previous studies utilizing coarse resolution MODIS data and county-level yields [36].
We observed a very variable performance of these datasets to explain within-field yield
variability: coefficient of determination R2 (Equations (3) and (4)) varied from 0.21 to 0.88
(average 0.56 ± 0.19) for 30 m HLS and from 0.09 to 0.77 (0.30 ± 0.16) for 3 m Planet.
Obtained R2 values were higher for HLS than Planet because of various scales employed;
averaging 3 m yields to 30 m reduced within-field yield variance (Figure 5), therefore
potentially benefiting moderate resolution at the expense of details lost. R2 values were
also higher for corn than soybean, which is consistent with previous works utilizing remote
sensing data and usually showing better performance for corn [7,36,37]. The maximal
correlation between satellite-derived features and yields was observed 2–3 months before
the harvest, therefore potentially enabling yield forecasts at field scale end of July to midAugust. We also found that performance of the satellite-derived models was better for corn
Remote Sens. 2021, 13, 872
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and soybean fields with lower average yields: as the yields increase, R2 values decrease. It
means that multi-spectral data saturate over high yields and cannot capture yield variability.
Other variables at such 3 to 30 m scale, such as soil moisture and evapotranspiration
(ET) [38–40], irrigation vs. rainfed, should be considered, as previous studies showed
that field-scale yield variability for corn and soybean in the US is mainly attributed to soil
differences in the field and the water holding capacity [41].
In terms of spectral bands, green (~0.560 µm) and NIR (~0.865 µm) were the most
important for Planet and HLS to explain corn and soybean within-field yield variability.
We did not consider red-edge from Sentinel-2, since it is not available in Landsat 8. When
analyzing WV-3 data, red-edge (0.726 µm) showed to be the most important in explaining
yield variability. Overall, SR-based models outperformed VI-based models highlighting
the importance of incorporating SR values directly into the yield models [9]. VIs based
on NIR and red-edge or green spectral bands performed better than those based on NIR
and red, as the former previously showed a better correspondence to corn and soybean
productivity [42].
The further research should focus on the within-field water-stress indicators that can
be retrieved using the fusion of satellite data and corresponding models and active remote
sensing sensors such as synthetic aperture radar (SAR) and LIDAR.
Author Contributions: Conceptualization and methodology, S.S., D.M.J. and E.F.V.; formal analysis
and investigation, S.S., N.I.K., M.G.L.B. and D.M.J.; resources, E.F.V.; data curation, S.S., N.I.K.,
M.G.L.B., J.-C.R.; writing—original draft preparation, S.S. and N.I.K.; writing—review and editing,
M.G.L.B., D.M.J., E.F.V., J.-C.R. and B.F.; visualization, S.S. and N.I.K.; supervision, E.F.V. and J.-C.R.;
funding acquisition, S.S., E.F.V. and J.-C.R. All authors have read and agreed to the published version
of the manuscript.
Funding: This research was funded by the NASA Land-Cover/Land-Use Change (LCLUC) Program, grant number 80NSSC18K0336, and Cooperative Agreement NASA Harvest, grant number
80NSSC18M0039.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments: We would like to thank NASA for providing support in generating the Harmonized Landsat Sentinel-2 (HLS) product. We would like to thank Iowa State University for providing
field-scale yield data records. DigitalGlobe/Maxar data were provided by NASA’s Commercial
Archive Data for NASA investigators (cad4nasa.gsfc.nasa.gov accessed on 29 January 2021) under
the National Geospatial-Intelligence Agency’s NextView license agreement.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1 provides detailed information on the fields used in the study, including their
location, crop type, and registered yields.
Table A1. Detailed information on corn and soybean fields used in the study. DOY is the day of the year. Shown are
per-field average yield and standard deviation (SE).
Field Id
County in
Iowa
Latitude,
Longitude
Crop
Year
Yield, t/ha
Planting/
Harvesting DOY
Baird
Uthe
Allee
Norman
McNay
Tilton
Jasper
Boone
Buena Vista
Story
Lucas
Story
41.632, −93.305
41.931, −93.754
42.586, −95.00
41.952, −93.689
40.956, −93434
41.954, −93.675
Corn
Corn
Corn
Corn
Corn
Corn
2018
2018
2018
2018
2018
2018
13.1 ± 1.2
13.0 ± 1.9
12.8 ± 0.6
12.7 ± 3.0
11.8 ± 2.2
11.8 ± 3.1
126/289
120/310
138/291
120/305
132/290
120/305
Remote Sens. 2021, 13, 872
16 of 18
Table A1. Cont.
Field Id
County in
Iowa
Latitude,
Longitude
Crop
Year
Yield, t/ha
Planting/
Harvesting DOY
Coles
McNay
Compost
McNay
Dairy
McNay
Allee
Coles
Herman
Reynoldson
Armstrong
Norman
Baird
Herman
NE Shop
Old Feed
Coles
Woodruff
Swine
Dairy
Armstrong
Baird
Coles
Pesek
Hamilton
Lucas
Story
Lucas
Story
Lucas
Buena Vista
Hamilton
Boone
Boone
Pottawatomie
Lucas
Jasper
Boone
Lucas
Lucas
Hamilton
Story
Boone
Story
Pottawatomie
Jasper
Hamilton
Boone
42.481, −93.523
40.975, −93.432
41.977, −93.656
40.979, −93.417
41.974, −93.648
40.972, −93.32
42.584, −95.00
42.488, −93.523
41.909, −93.739
41.925, −93.761
41.329, −95.181
41.952, −93.689
41.639, −93.312
41.909, −93.739
40.979, −93.417
40.975, −93.432
42.488, −93.523
41.992, −93.694
42.048, −93.701
41.975, −93.649
41.305, −95.180
41.632, −93.305
42.481, −93.523
42.043, −93.701
Corn
Corn
Soybean
Soybean
Soybean
Soybean
Soybean
Soybean
Soybean
Corn
Corn
Corn
Corn
Corn
Corn
Corn
Corn
Soybean
Soybean
Soybean
Soybean
Soybean
Soybean
Soybean
2018
2018
2018
2018
2018
2018
2018
2018
2018
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
2019
11.7 ± 1.2
10.7 ± 2.2
4.1 ± 0.9
4.1 ± 0.6
3.9 ± 0.7
3.9 ± 0.6
3.7 ± 0.3
3.5 ± 0.5
2.4 ± 0.6
13.7 ± 1.3
13.1 ± 2.3
12.7 ± 1.6
12.4 ± 1.3
11.7 ± 1.6
10.5 ± 1.5
9.7 ± 2.4
9.6 ± 1.4
4.5 ± 1.3
4.4 ± 1.1
4.3 ± 0.6
3.9 ± 0.6
3.6 ± 0.6
2.8 ± 0.5
3.0 ± 0.4
136/302
120/291
137/293
136/298
137/293
138/298
152/295
157/310
135/295
116/319
115/293
116/320
126/310
116/318
113/297
112/290
153/312
156/298
157/300
155/289
124/281
126/281
157/310
133/288
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