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# Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images

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• Association climatologique de la Moyenne-Garonne

## Abstract and Figures

This work aims to provide daily estimates of the evolution of popcorn dry masses at the field scale using an agro-meteorological model, named the simple algorithm for yield model combined with a water balance model (SAFY-WB), controlled by the Green Area Index (GAI), derived from satellite images acquired in the microwave and optical domains. Synthetic aperture radar (SAR) satellite information (σ°VH/VV) was provided by the Sentinel-1A (S1-A) mission through two orbits (30 and 132), with a repetitiveness of six days. The optical data were obtained from the Landsat-8 mission. SAR and optical data were acquired over one complete agricultural season, in 2016, over a test site located in the southwest of France. The results show that the total dry masses of corn can be estimated accurately (R² = 0.92) at daily time steps due to a combination of satellite and model data. The SAR data are more suitable for characterizing the first period of crop development (until the end of flowering), whereas the optical data can be used throughout the crop cycle. Moreover, the model offers good performances in plant (R2 = 0.90) and ear (R2 = 0.93) mass retrieval, irrespective of the phenological stage. The results also reveal that four phenological stages (four to five leaves, flowering, ripening, and harvest) can be accurately predicted by the proposed approach (R2 = 0.98; root-mean-square error (RMSE) = seven days). Nevertheless, some important points must be taken into account before assimilation, namely the SAR signal must be corrected with respect to thermal noise before being assimilated, and the relationship estimated between the GAI and SAR signal must be performed over fields cultivated without intercrops. These results are unique in the literature and provide a new way to better monitor corn production over time.
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remote sensing
Article
Temporal Evolution of Corn Mass Production Based
on Agro-Meteorological Modelling Controlled by
Satellite Optical and SAR Images
Frédéric Baup 1, * , Maël Ameline 1,2 , Rémy Fieuzal 3, Frédéric Frappart 4, Samuel Corgne 5
and Jean-François Berthoumieu 2
1CESBIO (Centre d’Etudes Spatiales de la Biosphère), University of Toulouse, 18 avenue Edouard Belin,
31401 Toulouse, France
2Association Climatologique de Moyenne-Garonne et du Sud-ouest, Aérodrome d’Agen,
47520 Le Passage, France
3Airbus-Defense & Space; 31 Rue des Cosmonautes, 31400 Toulouse, France
4LEGOS (Laboratoire d’Etudes en Géophysique et Océanographie Spatiale), University of Toulouse,
14 avenue Edouard Belin, 31400 Toulouse, France
5
LETG (Littoral, Environnement, T
é
l
é
d
é
tection et G
é
omatique), University of Rennes, 35000 Rennes, France
*Correspondence: frederic.baup@cesbio.cnes.fr; Tel.: +33-561-626-344
Received: 11 June 2019; Accepted: 2 August 2019; Published: 22 August 2019


Abstract:
This work aims to provide daily estimates of the evolution of popcorn dry masses at
the ﬁeld scale using an agro-meteorological model, named the simple algorithm for yield model
combined with a water balance model (SAFY-WB), controlled by the Green Area Index (GAI), derived
from satellite images acquired in the microwave and optical domains. Synthetic aperture radar (SAR)
satellite information (
σVH/VV
) was provided by the Sentinel-1A (S1-A) mission through two orbits
(30 and 132), with a repetitiveness of six days. The optical data were obtained from the Landsat-8
mission. SAR and optical data were acquired over one complete agricultural season, in 2016, over a
test site located in the southwest of France. The results show that the total dry masses of corn can be
estimated accurately (R
2
=0.92) at daily time steps due to a combination of satellite and model data.
The SAR data are more suitable for characterizing the ﬁrst period of crop development (until the
end of ﬂowering), whereas the optical data can be used throughout the crop cycle. Moreover, the
model oers good performances in plant (R
2
=0.90) and ear (R
2
=0.93) mass retrieval, irrespective
of the phenological stage. The results also reveal that four phenological stages (four to ﬁve leaves,
ﬂowering, ripening, and harvest) can be accurately predicted by the proposed approach (R
2
=0.98;
root-mean-square error (RMSE) =seven days). Nevertheless, some important points must be taken
into account before assimilation, namely the SAR signal must be corrected with respect to thermal
noise before being assimilated, and the relationship estimated between the GAI and SAR signal must
be performed over ﬁelds cultivated without intercrops. These results are unique in the literature and
provide a new way to better monitor corn production over time.
Keywords: remote sensing; Sentinel-1; SAR; SAFY–WB; crop production; maize
1. Introduction
Identifying the eective management of agricultural land is essential in order to achieve high
socio-economic performance while limiting environmental impacts and ensuring the sustainability of
resources. Through these direct and indirect eects on many surface balances (carbon, nitrogen, water,
or energy), crops appear as the central element of agro-systems, with their development testifying
to the interactions between the edaphological and climatic conditions being closely dependent on
Remote Sens. 2019,11, 1978; doi:10.3390/rs11171978 www.mdpi.com/journal/remotesensing
Remote Sens. 2019,11, 1978 2 of 21
cultural practices (tillage, fertilizer, irrigation, and residue management) [
1
4
]. The quantiﬁcation of
the advantages or disadvantages associated with the implemented cultural practices then involves
the achievement of a balance sheet based on monitoring the cultures, by targeting the key integrative
variables (e.g., leaf area index, biomass, and yield) [
5
7
]. In this context, the use of satellite imagery
combined with a crop functioning model constitutes a useful tool for diagnosis at a landscape scale.
The recent launch of the Sentinel-1, Sentinel-2, and Landsat-8 satellite missions has opened the
way to new opportunities for the combined use of data acquired in visible, infrared, and microwave
wavelengths [
8
10
]. In the past, the lack of near-synchronous acquisition of optical and synthetic
aperture radar (SAR) image time series during the same agricultural season restricted the possibilities
of joint signal analysis to a limited number of studies [
11
13
]. The majority of the studies were then
based on a few images, with the analyses of the satellite signals being often temporally and/or spatially
separated, which made a comparison of the sensitivity and dynamic observed for contrasting surface
states dicult. Nevertheless, these studies provide interesting study cases of the possibilities of using
these data in a context of monitoring agricultural areas, with various issues, ranging from land cover
classiﬁcation [
14
] to the monitoring of crop or soil parameters [
15
17
], the detection of speciﬁc cultural
practices [1820], or damage concerning crops [21].
Vegetation indices derived from optical image reﬂectance are often used for monitoring the
photosynthetic activity of vegetation during the phenological cycle [
22
] or to estimate biophysical
parameters such as the leaf area index (LAI) or the fraction of absorbed photosynthetically active
radiation (FAPAR). Hence, the approaches aiming at modeling the crop cycle at a regional scale were
primarily based on the assimilation of optical images [
22
27
] into a simple agro-meteorological model,
such as GRAMI [
28
], Aquacrop [
29
], or the simple algorithm for yield model combined with a water
balance model (SAFY-WB) [30,31].
The limits encountered by the use of optical reﬂectance/indices are inherent to the image
speciﬁcities, with, for instance, saturation of the indices when the vegetation becomes dense, or
with a lack of observation during cloudy periods. The synthetic aperture radar (SAR) data then
constitute an alternative, providing information about substitution by targeting the same ([
32
,
33
]) or
complementary ([
16
,
34
]) variables as those derived from optical imagery. The combined use of optical
and SAR images to constraint models remains original and stays conﬁned to a limited number of
studies; however, they have all demonstrated the interest of this kind of approach for improving crop
monitoring [
16
,
32
,
33
,
35
,
36
]. Moreover, to the best of our knowledge, the monitoring of the popcorn
variety has never been performed using satellite radar data in order to estimate the time courses of its
masses and phenological stages.
In this context, the objective of this study is to estimate the evolution of popcorn dry masses at a
ﬁeld scale, as well as the daily temporal resolution using an agro-meteorological model (SAFY-WB)
controlled by the Green Area Index (GAI) derived from satellite optical and SAR images. The remainder
of this paper is structured as follows: the materials (site description, satellite and ground data, and
model) and method are ﬁrst presented (Section 2). Section 3presents the results on mass simulations
performed by the model controlled by GAI, derived from satellite data (combined use of SAR and
optical images), followed by a discussion in Section 4. The discussion evaluates the capacity of inferring
corn phenological stages throughout the crop cycle, as well as their associated masses (ear, plant, and
total masses). Moreover, the impact of (1) the SAR signal processing (speckle ﬁltering and thermal
noise removal), and (2) the intercrops’ inﬂuences on SAR signal and masses retrieval are discussed.
2. Materials and Method
2.1. Site Description
The studied area is located in the southwest of France (Figure 1), near the city of Toulouse, in
the Occitanie region (43.49
N 0.93
E). This area is characterized by a temperate climate. It is highly
anthropized, and mainly comprises seasonal crops, grasslands, forests, and urban areas. Among the
Remote Sens. 2019,11, 1978 3 of 21
dierent crop species, this work focused on eight corn ﬁelds dedicated to popcorn production. The
areas of the corn ﬁelds ranged from 2.6 to 30.6 ha (13.8 ha on average).
Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 22
2. Materials and Method
2.1. Site Description
The studied area is located in the southwest of France (Figure 1), near the city of Toulouse, in the
Occitanie region (43.49°N 0.93°E). This area is characterized by a temperate climate. It is highly
anthropized, and mainly comprises seasonal crops, grasslands, forests, and urban areas. Among the
different crop species, this work focused on eight corn fields dedicated to popcorn production. The areas
of the corn fields ranged from 2.6 to 30.6 ha (13.8 ha on average).
Figure 1. Location of the studied fields of popcorn, together with the swathes of Sentinel 1 (S1-A; orbits
132 and 30) and Landsat-8. The fields used for calibration (F4) and validation (F1, F2, and F3) are
represented by yellow and green circles, respectively. Fields F5 to F8 belong to the same working farm
and are used to study the spatial distribution of the production at a working-farm scale. The weather
station is shown by the blue cross.
2.2. Satellite and Ground Data
2.2.1. Satellite Data
Acquisition Calendar
Satellite data were acquired throughout a seasonal crop period, from March to October 2016. The
SAR data (51 images from orbit 30 (25) and 132 (26)) were acquired at C-band (at VV and VH
polarizations) by the Sentinel 1A (S1-A) platform (freely available at Hub https://scihub.copernicus.eu/),
whereas, the 18 optical data were acquired by Landsat-8 Operational Land Imager (OLI) sensor (Figure
2).
Figure 1.
Location of the studied ﬁelds of popcorn, together with the swathes of Sentinel 1 (S1-A;
orbits 132 and 30) and Landsat-8. The ﬁelds used for calibration (F4) and validation (F1, F2, and F3) are
represented by yellow and green circles, respectively. Fields F5 to F8 belong to the same working farm
and are used to study the spatial distribution of the production at a working-farm scale. The weather
station is shown by the blue cross.
2.2. Satellite and Ground Data
2.2.1. Satellite Data
Acquisition Calendar
Satellite data were acquired throughout a seasonal crop period, from March to October 2016.
The SAR data (51 images from orbit 30 (25) and 132 (26)) were acquired at C-band (at VV and VH
polarizations) by the Sentinel 1A (S1-A) platform (freely available at Hub https://scihub.copernicus.eu/),
whereas, the 18 optical data were acquired by Landsat-8 Operational Land Imager (OLI) sensor
(Figure 2).
Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 22
Figure 2. Time course of optical (Landsat-8 OLI) and synthetic aperture radar (SAR; orbit 30 and 132)
acquisition during the cultivation cycle of popcorn (April to October) in 2016. The cloud cover rate over
the fields is mentioned for the optical data (black circle).
Data Pre-Processing
The SAR images were processed in ground range-detected (GRD) mode, with an incidence angle
ranging from 29.1° to 46.0° [10]. The S1-A images were pre-processed (radiometric calibration, thermal
noise, range doppler terrain correction, and re-sampled at 10 × 10 m2 spacing) from the Google Earth
engine website, so as to obtain the sigma-naught ratio (σVH/VV
0), the best index for monitoring the
vegetative part of corn, from the Sentinel-1 data [35,37].
Optical images were acquired by the sensor OLI onboard of Landsat 8 in four narrow spectral
bands, ranging from 0.45 to 0.89 μm, which correspond to the blue, green, red, and near-infrared
domains. They were processed using the level 2A processor, named multi-sensor atmospheric
correction and cloud screening (MACCS), and are available free of charge from Theia Land Data
Services (https://www.theia-land.fr/en/). After preprocessing, only 10 images could be used over the
fields (between days 90 and 290), because of cloud cover (mean temporal repetitiveness of 21 days
instead of 16 in the nominal value). In the following, these images were used to derive the GAI.
2.2.2. Ground Data
Ground data were collected over four fields (identified as ID F1, F2, F3, and F4 in Figure 1). They
concerned agricultural practices (e.g., irrigation, sowing, tillage, and harvesting) and vegetation
parameters (crop height, main phenological stages, biomass, dry matter, and water content). The surface
soil roughness was considered stable during the time after sowing because no tillage events occurred
until harvest. Therefore, it was assumed that surface soil roughness changes had no significant impact
on the optical and SAR signals. Fields F5 to F8 were used to study the spatial distribution of the
production at the working farm scale in Section 3.3 (no ground data were available for these fields). F1
and F4 belong to one working farm, and the others belong to the second working farm.
These fields were sown during March and April and were harvested at the end of September and in
early October 2016, as indicated in Table 1. Throughout the crop season, at least six vegetation
measurements were performed for each field. Each measurement was composed of five plants of corn,
from which the above-ground biomass was directly weighed in situ. Then, the total aboveground dry
mass (TDM) was measured after crop drying (the samples were put in an oven for 48 h at 65 °C ). The
water content was finally derived from the difference between the total biomass and TDM. For these
measurements, the ears and the rest of the plant (leaves and stems) were separated, giving access to the
ear dry mass (EDM) and the plant dry mass (PDM), respectively.
Figure 2.
Time course of optical (Landsat-8 OLI) and synthetic aperture radar (SAR; orbit 30 and 132)
acquisition during the cultivation cycle of popcorn (April to October) in 2016. The cloud cover rate
over the ﬁelds is mentioned for the optical data (black circle).
Remote Sens. 2019,11, 1978 4 of 21
Data Pre-Processing
The SAR images were processed in ground range-detected (GRD) mode, with an incidence angle
ranging from 29.1
to 46.0
[
10
]. The S1-A images were pre-processed (radiometric calibration, thermal
noise, range doppler terrain correction, and re-sampled at 10
×
10 m
2
engine website, so as to obtain the sigma-naught ratio (
σ0
VH/VV
), the best index for monitoring the
vegetative part of corn, from the Sentinel-1 data [35,37].
Optical images were acquired by the sensor OLI onboard of Landsat 8 in four narrow spectral
bands, ranging from 0.45 to 0.89
µ
m, which correspond to the blue, green, red, and near-infrared
domains. They were processed using the level 2A processor, named “multi-sensor atmospheric
correction and cloud screening” (MACCS), and are available free of charge from Theia Land Data
Services (https://www.theia-land.fr/en/). After preprocessing, only 10 images could be used over the
ﬁelds (between days 90 and 290), because of cloud cover (mean temporal repetitiveness of 21 days
instead of 16 in the nominal value). In the following, these images were used to derive the GAI.
2.2.2. Ground Data
Ground data were collected over four ﬁelds (identiﬁed as ID F1, F2, F3, and F4 in Figure 1).
They concerned agricultural practices (e.g., irrigation, sowing, tillage, and harvesting) and vegetation
parameters (crop height, main phenological stages, biomass, dry matter, and water content). The
surface soil roughness was considered stable during the time after sowing because no tillage events
occurred until harvest. Therefore, it was assumed that surface soil roughness changes had no signiﬁcant
impact on the optical and SAR signals. Fields F5 to F8 were used to study the spatial distribution of
the production at the working farm scale in Section 3.3 (no ground data were available for these ﬁelds).
F1 and F4 belong to one working farm, and the others belong to the second working farm.
These ﬁelds were sown during March and April and were harvested at the end of September
and in early October 2016, as indicated in Table 1. Throughout the crop season, at least six vegetation
measurements were performed for each ﬁeld. Each measurement was composed of ﬁve plants of corn,
from which the above-ground biomass was directly weighed in situ. Then, the total aboveground dry
mass (TDM) was measured after crop drying (the samples were put in an oven for 48 h at 65
C). The
water content was ﬁnally derived from the dierence between the total biomass and TDM. For these
measurements, the ears and the rest of the plant (leaves and stems) were separated, giving access to
the ear dry mass (EDM) and the plant dry mass (PDM), respectively.
Table 1.
Dates of the sowing, harvest, and vegetation measurements of the four studied ﬁelds. The
temporal reference is given in day of year (doy) for 2016, and also in
C day, according to an agronomic
point of view.
Field ID Date of
Sowing
Date of Harvest
(doy/C day)
Number of
Samples Date of Sampling (doy/C day)
F1 102 278/2453 7 (133/214, 148/352, 169/599, 195/985,
215/1299, 232/1574, 266/2095)
F2 104 281/2264 6
(133/201, 169/586, 195/971, 215/1285,
232/1561, 266/2081)
F3 87 273/2261 6 (133/292, 169/677, 195/1063,
215/1377, 232/1652, 266/2173)
F4 102 278/2453 7 (133/214, 148/352, 169/599, 195/985,
215/1299, 232/1574, 266/2095)
Figure 3presents an example of temporal dynamics of the dry masses (TDM, EDM, and PDM),
crop water content, and phenological stages from the in-situ data processed for ﬁeld F3 (Figure 1).
The TDM starts increasing from emergence, to reach its maximum (17.97 t
·
ha
1
) just before harvest.
The allocation of the mass to the ear starts from about 1000
C day, and its dry mass reaches about
12 t
·
ha
1
a few days before measurement. The water content, strongly correlated with the biomass
Remote Sens. 2019,11, 1978 5 of 21
(R
2
mean =0.95, not shown here), starts from 94% at emergence, and then decreases signiﬁcantly
from ﬂowering, to reach about 25% at harvest (41% was measured a few days before harvest, whereas
vegetation continued to strongly dry). Considering the measurements, the mass of the grain represents
an average of 77% of the EDM at harvest day. The maximum height of this variety of corn ranged from
1.60 to 1.80 m, depending on the studied ﬁeld.
Remote Sens. 2019, 11, x FOR PEER REVIEW 5 of 22
Table 1. Dates of the sowing, harvest, and vegetation measurements of the four studied fields. The
temporal reference is given in day of year (doy) for 2016, and also in °C day, according to an agronomic
point of view.
Date of
Sowing
Date of Harvest
(doy/°C day)
Number of
Samples
Date of Sampling (doy/°C day)
102
278/2453
7
(133/214, 148/352, 169/599, 195/985,
215/1299, 232/1574, 266/2095)
104
281/2264
6
(133/201, 169/586, 195/971,
215/1285, 232/1561, 266/2081)
87
273/2261
6
(133/292, 169/677, 195/1063,
215/1377, 232/1652, 266/2173)
102
278/2453
7
(133/214, 148/352, 169/599, 195/985,
215/1299, 232/1574, 266/2095)
Figure 3 presents an example of temporal dynamics of the dry masses (TDM, EDM, and PDM), crop
water content, and phenological stages from the in-situ data processed for field F3 (Figure 1). The TDM
starts increasing from emergence, to reach its maximum (17.97 t.ha1) just before harvest. The allocation
of the mass to the ear starts from about 1000 °C day, and its dry mass reaches about 12 t.ha1 a few days
before measurement. The water content, strongly correlated with the biomass (R2 mean = 0.95, not
shown here), starts from 94% at emergence, and then decreases significantly from flowering, to reach
about 25% at harvest (41% was measured a few days before harvest, whereas vegetation continued to
strongly dry). Considering the measurements, the mass of the grain represents an average of 77% of the
EDM at harvest day. The maximum height of this variety of corn ranged from 1.60 to 1.80 m, depending
on the studied field.
Figure 3. Temporal evolution of the crop water content (in blue), total dry masses of the corn (TDM; in
red), divided into the following two components: ear dry mass (EDM; in green) and plant dry mass
(PDM; in black). These measurements are performed for field F3. The vertical dashed red line
represents the harvest date.
The meteorological data (i.e., air temperature, solar radiation, relative humidity, wind speed, and
rainfall) were registered at semi-hourly time steps by one weather station (Figure 1). They were used to
Figure 3.
Temporal evolution of the crop water content (in blue), total dry masses of the corn (TDM;
in red), divided into the following two components: ear dry mass (EDM; in green) and plant dry
mass (PDM; in black). These measurements are performed for ﬁeld F3. The vertical dashed red line
represents the harvest date.
The meteorological data (i.e., air temperature, solar radiation, relative humidity, wind speed,
and rainfall) were registered at semi-hourly time steps by one weather station (Figure 1). They
were used to derive the daily reference evapotranspiration (ET
0
) according to the FAO (Food and
Agriculture Organization) method, and were used as the input variables of the agro-meteorological
model (Figure 4).
2.3. The Agro-Meteorological Model
The agro-meteorological model, named SAFY-WB (simple algorithm for yield estimates coupled
with a water balance model), was used to simulate the time courses (with a daily time step) of the
popcorn masses (total, plant, and ear masses), GAI, and evapotranspiration. The model, detailed in the
literature [
30
,
31
], requires the following meteorological input variables: the global solar radiation, air
temperature, precipitation, and potential evapotranspiration (Figure 4). Only the main processes of the
vegetation growth model are presented hereinafter.
The TDM and GAI are proportional to the absorbed photosynthetically active radiation, according
to the eective light use eciency, and a stress coecient related to the meteorological conditions
(relationship adapted from the literature [38], in Equations (1)–(4)):
TDMdoy =TDMdoy1+TDMdoy, (1)
with, TDMdoy =ELUE ×PARdoy ×Sc, (2)
GAIdoy =GAIdoy1+GAIdoy, (3)
with, GAIdoy =TDM ×SLAdo y ×PLIdoy, (4)
Remote Sens. 2019,11, 1978 6 of 21
where ELUE represents the eective light use eciency, SLA the speciﬁc leaf area, PLI the partitioning
to leaf index, Sc the stress coecient of water and temperature, and doy the day of the year.
Remote Sens. 2019, 11, x FOR PEER REVIEW 6 of 22
derive the daily reference evapotranspiration (ET0) according to the FAO (Food and Agriculture
Organization) method, and were used as the input variables of the agro-meteorological model (Figure
4).
Figure 4. Temporal evolution of the climatic data used as the input of the agro-meteorological model (Rg,
ET0, rainfall, and Tmean).
2.3. The Agro-Meteorological Model
The agro-meteorological model, named SAFY-WB (simple algorithm for yield estimates coupled
with a water balance model), was used to simulate the time courses (with a daily time step) of the
popcorn masses (total, plant, and ear masses), GAI, and evapotranspiration. The model, detailed in the
literature [30,31], requires the following meteorological input variables: the global solar radiation, air
temperature, precipitation, and potential evapotranspiration (Figure 4). Only the main processes of the
vegetation growth model are presented hereinafter.
The TDM and GAI are proportional to the absorbed photosynthetically active radiation, according
to the effective light use efficiency, and a stress coefficient related to the meteorological conditions
(relationship adapted from the literature [38], in Equations (1)(4)):
𝑇𝐷𝑀𝑑𝑜𝑦 =𝑇𝐷𝑀𝑑𝑜𝑦−1 +∆𝑇𝐷𝑀𝑑𝑜𝑦,
(1)
with, 𝑇𝐷𝑀𝑑𝑜𝑦 = 𝐸𝐿𝑈𝐸 × 𝑃𝐴𝑅𝑑𝑜𝑦 ×𝑆𝑐,
(2)
𝐺𝐴𝐼𝑑𝑜𝑦 =𝐺𝐴𝐼𝑑𝑜𝑦−1 +∆𝐺𝐴𝐼𝑑𝑜𝑦,
(3)
with, ∆𝐺𝐴𝐼𝑑𝑜𝑦 =𝑇𝐷𝑀×𝑆𝐿𝐴𝑑𝑜𝑦 ×𝑃𝐿𝐼𝑑𝑜𝑦,
(4)
where ELUE represents the effective light use efficiency, SLA the specific leaf area, PLI the partitioning
to leaf index, Sc the stress coefficient of water and temperature, and doy the day of the year.
The photosynthetically active fraction of solar radiation absorbed by the plants is proportional to
GAI and Rg, according to the Beer-Lambert law (Equation (5)):
Figure 4.
Temporal evolution of the climatic data used as the input of the agro-meteorological model
(Rg, ET0, rainfall, and Tmean).
The photosynthetically active fraction of solar radiation absorbed by the plants is proportional to
GAI and Rg, according to the Beer-Lambert law (Equation (5)):
PARdoy =0.47 ×Rg ×1e(0.6×GAIdoy ). (5)
The leaf production and leaf senescence are controlled by a growing degree-day function. During
the leaf growing period, a fraction of the daily DM production is allocated to the leaf production (LP)
thanks to an empirical parameterization proposed by the authors of [39], in Equations (6) and (7):
PRTdoy =max0, Pla ×e(Plb×PTempdo y), (6)
where, Pla and Plb (empirical parameters) drive the partitioning to the leaves.
The senescence occurred at a prescribed rate, when the air temperature accumulated from plant
emergence (
PTempdoy
) reached a crop-speciﬁc threshold (given by its variety). A temperature-stress
function aected the DM production, considering a 2-degree polynomial speciﬁed by an optimal value
(30 C) and two extreme values (6 and 42 C), beyond which the corn growth stopped ([40]).
The formalism of this model is based on a limited number of parameters (six have been deﬁned
as target parameters through the sensitivity study described by the authors of [
31
]), which allow
for combining the model with the remotely sensed data. Four of them describe the development
stages of the crop, as follows: partitioning to the leaf parameters (Pl
A
and Pl
B
) is eective during the
growth phase (Equations (6) and (7)), while the cumulative temperature, which induces senescence
and the rate of senescence (Stt and Rs), is used to describe the last phenological stages. Parameter D
0
(corresponding to a few days after the emergence of corn) and the eective light use eciency (ELUE)
are related to agricultural practices (Equation (2)).
Four phenological stages are simulated by the model, as follows: four to ﬁve leaves, ﬂowering,
ripening, and harvest. They are derived from D
0
, from the day on which the EDM starts growing,
when GAI starts decreasing, and when GAI reaches 0, respectively.
Remote Sens. 2019,11, 1978 7 of 21
This type of approach has previously been validated for the monitoring of the crop cycle of wheat,
corn, and sunﬂower, using only optical images [
23
,
30
,
41
], and for soybean, sunﬂower, and grain corn
using both optical and SAR images [16,35,42].
2.4. Methodology
The methodology used to simulate the dierent masses from the model driven by satellite data is
given in Figure 5.
Remote Sens. 2019, 11, x FOR PEER REVIEW 7 of 22
 .
(5)
The leaf production and leaf senescence are controlled by a growing degree-day function. During
the leaf growing period, a fraction of the daily DM production is allocated to the leaf production (LP)
thanks to an empirical parameterization proposed by the authors of [39], in Equations (6) and (7):
 ,
(6)
  
(7)
where, Pla and Plb (empirical parameters) drive the partitioning to the leaves.
The senescence occurred at a prescribed rate, when the air temperature accumulated from plant
emergence () reached a crop-specific threshold (given by its variety). A temperature-stress
function affected the DM production, considering a 2-degree polynomial specified by an optimal value
(30 °C) and two extreme values (6 and 42 °C), beyond which the corn growth stopped ([40]).
The formalism of this model is based on a limited number of parameters (six have been defined as
target parameters through the sensitivity study described by the authors of [31]), which allow for
combining the model with the remotely sensed data. Four of them describe the development stages of
the crop, as follows: partitioning to the leaf parameters (PlA and PlB) is effective during the growth phase
(Equations (6) and (7)), while the cumulative temperature, which induces senescence and the rate of
senescence (Stt and Rs), is used to describe the last phenological stages. Parameter D0 (corresponding to
a few days after the emergence of corn) and the effective light use efficiency (ELUE) are related to
agricultural practices (Equation (2)).
Four phenological stages are simulated by the model, as follows: four to five leaves, flowering,
ripening, and harvest. They are derived from D0, from the day on which the EDM starts growing, when
GAI starts decreasing, and when GAI reaches 0, respectively.
This type of approach has previously been validated for the monitoring of the crop cycle of wheat,
corn, and sunflower, using only optical images [23,30,41], and for soybean, sunflower, and grain corn
using both optical and SAR images [16,35,42].
2.4. Methodology
The methodology used to simulate the different masses from the model driven by satellite data is
given in Figure 5.
Figure 5. Methodology used to estimate TDM, PDM, and EDM from a combination of satellite images,
the SAFY-WB model, and ground data.
The satellite data were first preprocessed following different steps according to their wavelength
domain, as described in Section 2.2.1: Data Processing. The green area indices were then derived from
both optical and radar images (GAIopt and GAISAR).
GAIopt was generated through the BVNet tool (Biophysical Variables Neural NETwork), developed
by the authors of [43]. This tool combines a radiative transfer model (SAIL + PROPSPECT = PROSAIL)
Figure 5.
Methodology used to estimate TDM, PDM, and EDM from a combination of satellite images,
the SAFY-WB model, and ground data.
The satellite data were ﬁrst preprocessed following dierent steps according to their wavelength
domain, as described in Section 2.2.1: Data Processing. The green area indices were then derived from
both optical and radar images (GAIopt and GAISAR).
GAI
opt
was generated through the BVNet tool (Biophysical Variables Neural NETwork), developed
by the authors of [
43
]. This tool combines a radiative transfer model (SAIL +PROPSPECT =PROSAIL)
and artiﬁcial neural networks (ANNs). The radiative transfer model was ﬁrst used to constitute a
training dataset, with the constraint of estimating reﬂectance (from 400 to 2500 nm) for a wide range of
conditions regarding the crop biophysical variables. Artiﬁcial neural networks were then trained on
the dataset estimated from PROSAIL (considering reﬂectance as the input variable, and GAI, FAPAR,
and fCover as the output). Regarding GAI, the domain of validity of such an approach ranges between
0 and 6 m2·m2. The trained ANNs were ﬁnally applied to the satellite images, using green, red, and
near-infrared reﬂectances. Such an approach has been validated in the South-West of France using
optical images provided by Formosat-2, Spot-4, and Landsat-8, on independent ground measurements,
showing an accurate performance with a correlation of 0.92 and a root mean square error (RMSE) of
0.4 m
2·
m
2
for corn in the literature [
41
]. These good performances led us to use this approach to
derive the GAI of popcorn from optical imagery.
GAI
SAR
are empirically derived from the sensitivity of the SAR signals to the GAI, from a
non-linear regression (Equation (8)) valid from 0 to 1000
C day. Among the tested SAR conﬁgurations
(
σVH
,
σVH/VV
, and
σVV
), the best performances were obtained with
σVH/VV
(R
2
=0.97; RMSE =
0.28 m
2·
m
2
). After a 1000
C day, the quality of the relationship was strongly degraded, as
σVH/VV
saturated and remained at a high value until harvest, whereas the GAI decreased during senescence,
as shown in the literature [35].
GAISAR =52.92 ×e0.55×σ0
VH/VV (8)
Once generated, GAI
SAR
and/or GAI
opt
were combined to the measured TDM (TDM
mea
), in order
to control the model during the steps of calibration and validation. This step aims at optimizing
the target parameters inside their domains of variation to minimize the bi-objective cost function
(Table 2) [23,31,41].
The bi-objective cost function (Equation (11)) is based on the relative root mean square errors,
considering the satellite derived green area index (GAI
mea
) and using the measured total dry mass dry
as reference (Equations (9) and (10)):
Remote Sens. 2019,11, 1978 8 of 21
CGAI =sN1
GAImea X
0<ti<2500
[GAIsim(ti)GAImea (ti)]2, (9)
CTDM =sN1
TDMmea X
0<ti<2500
[TDMsim(ti)TDMmea(ti)]2, (10)
C=sum
GAImea
1×CGAI
TDMmea
1×CTDM
, (11)
where GAI
sim
or TDM
sim
correspond to the values simulated by the model at time (t
i
), GAI
mea
and
TDM
mea
are the values obtained from the measurements (satellite or ground), and Nrepresents the
amount of data collected between sowing (0 C day) and harvest (2500 C day).
Table 2.
Deﬁnition and domain of variation of the six target parameters, namely: the crop-speciﬁc
parameters (PlA, PlB, Stt, and Rs) and the ﬁeld-speciﬁc parameters (D0and ELUE).
Parameter Deﬁnition Domain of Variation Unit
PlAPartition-to-leaf function 0.05–0.5 -
PlBPartition-to-leaf function 105–102-
Stt
Temperature sum for senescence
0–2000 C day
Rs Rate of senescence 0–105C
D0Day090–250 day
ELUE Eective light-use eciency 0.5–6 g.MJ1
In our case, the calibration/validation steps were performed from the data acquired from ﬁeld
F4 (Figure 1) and ﬁelds F1, F2, and F3, respectively. These steps consist of comparing the simulated
TDM, PDM, and EDM to the measured values. Field F4 was chosen for calibration because it is ﬂat,
it is managed with conventional practices in contrast to others (tillage and no intercrop), and the
culture is homogeneous. Hence, the results of the calibration were not aected by the local slope of
the ﬁeld, the heterogeneity of the culture, or the presence of an intercrop. The calibration step was
performed for one ﬁeld, reproducing how the agricultural decision-makers operate (they based their
recommendations on the data collected in a ﬁeld considered representative of an agricultural region).
As proposed by the authors of [
30
,
31
], the four parameters describing the developmental stages
of the crop were estimated during the calibration (Pl
A
, Pl
B
, Stt, and Rs) step and remained constant for
the validation. Two parameters were optimized (using a simplex method) during the calibration and
validation for each ﬁeld, i.e., those related to the agricultural practices (namely, D0and ELUE).
Finally, a production map was generated at a working farm scale (six ﬁelds, representing 101 ha),
in order to analyze its spatial distribution, by considering three target simulation outputs, namely:
total, ear, and plant masses.
3. Results
3.1. Model Calibration
Figure 6presents the results of the calibration step and compares the temporal evolutions of
the simulated and observed GAI and DM along the crop cycle, from sowing (0
C day) to harvest
C day). The obtained values of the crop-speciﬁc parameters (Pl
A
, Pl
B
, Stt, and Rs) and the
ﬁeld-speciﬁc parameters (D0and ELUE) are presented in Table 3.
The temporal trends of the GAI and DM (TDM, EDM, and PDM) were globally well reproduced.
All of the biophysical variables were simulated with good performances, as demonstrated by the
values of the determination coecient, which were higher than 0.96 for the simulation of GAI, TDM,
EDM, and TDM (Table 4).
Remote Sens. 2019,11, 1978 9 of 21
Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 22
where GAIsim or TDMsim correspond to the values simulated by the model at time (ti), GAImea and TDMmea
are the values obtained from the measurements (satellite or ground), and N represents the amount of
data collected between sowing (0 °C day) and harvest (2500 °C day).
In our case, the calibration/validation steps were performed from the data acquired from field F4
(Figure 1) and fields F1, F2, and F3, respectively. These steps consist of comparing the simulated TDM,
PDM, and EDM to the measured values. Field F4 was chosen for calibration because it is flat, it is
managed with conventional practices in contrast to others (tillage and no intercrop), and the culture is
homogeneous. Hence, the results of the calibration were not affected by the local slope of the field, the
heterogeneity of the culture, or the presence of an intercrop. The calibration step was performed for one
field, reproducing how the agricultural decision-makers operate (they based their recommendations on
the data collected in a field considered representative of an agricultural region).
As proposed by the authors of [30,31], the four parameters describing the developmental stages of
the crop were estimated during the calibration (PlA, PlB, Stt, and Rs) step and remained constant for the
validation. Two parameters were optimized (using a simplex method) during the calibration and
validation for each field, i.e., those related to the agricultural practices (namely, D0 and ELUE).
Finally, a production map was generated at a working farm scale (six fields, representing 101 ha), in
order to analyze its spatial distribution, by considering three target simulation outputs, namely: total,
ear, and plant masses.
3. Results
3.1. Model Calibration
Figure 6 presents the results of the calibration step and compares the temporal evolutions of the
simulated and observed GAI and DM along the crop cycle, from sowing (0 °C day) to harvest (about
2500 °C day). The obtained values of the crop-specific parameters (PlA, PlB, Stt, and Rs) and the
field-specific parameters (D0 and ELUE) are presented in Table 3.
Figure 6. Comparisons between the simulated and measured masses for the calibration field (F4Figure
1). The dry masses are divided into total, plant, and ear dry masses (TDM, PDM, and EDM). GAISAR and
GAIopt are displayed by grey crosses or circles, respectively. Simulations of GAI, TDM, PDM, and EDM
are represented by a continuous grey line, and dashed red, green, and black lines, respectively.
Figure 6.
Comparisons between the simulated and measured masses for the calibration ﬁeld
(F4—Figure 1). The dry masses are divided into total, plant, and ear dry masses (TDM, PDM,
and EDM). GAI
SAR
and GAI
opt
are displayed by grey crosses or circles, respectively. Simulations of
GAI, TDM, PDM, and EDM are represented by a continuous grey line, and dashed red, green, and
black lines, respectively.
Table 3.
Values of the crop-speciﬁc parameters (Pl
A
, Pl
B
, Stt, and Rs) and the ﬁeld-speciﬁc parameters
(D0and ELUE) derived from the calibration step.
Parameter Value
PlA0.08
PlB3.0 ×103
Stt 1361
Rs 3569
D0142
ELUE 3.98
Table 4.
Statistical performances estimated for Green Area Index (GAI), Ear Dry Mass (EDM), Plant Dry
Mass (PDM), and Total above ground Dry Mass (TDM) retrievals during the calibration step of the model.
Target Output n R2a b RMSE rRMSE (%)
GAI 9 0.99 0.97 0.08 0.08 10.58
EDM 7 0.96 0.99 0.05 0.82 27.65
PDM 7 0.97 0.95 0.10 0.45 14.48
TDM 7 0.96 0.96 0.12 1.17 19.35
Figure 6also shows that the GAI, derived from optical or SAR reﬂectances, was quite stable and
exhibited a similar pattern during the ﬁrst phenological stages (between 0 and 500
C day). It was not
signiﬁcantly aected by soil moisture change (as a result of irrigation and/or rainfall) when the surface
was still mainly bare (just after sowing). This result conﬁrms the pertinence of using the ratio of SAR
backscattering coecients (
σVH/VV
) instead of using simple co- or cross-polarization (
σVV
or
σVH
)
to derive the GAI (the latter being much more aected by soil moisture changes, as demonstrated by
the authors of [44]).
During the growing period (between 500 and 1000
C day), the GAI derived from the SAR or optical
images exhibited a similar trend, but was not completely the same. This point has a limited impact on
the simulation, as the agro-meteorological model was not able to produce so much vegetation variation
during the growing period. This approach is thus robust enough to be not signiﬁcantly aected
by the dierence in GAI retrieval until ﬂowering (1000
C day), contrary to empirical approaches
in which a small change in the backscattering coecient induces a proportional change in the GAI
Remote Sens. 2019,11, 1978 10 of 21
retrieval [
45
47
]. From the end of ﬂowering to the harvest, only GAI derived from optical data was
assimilated, as the SAR signal saturated and was not suciently sensitive to senescence [
35
,
37
,
48
,
49
].
3.2. Model Validation—Mass Retrieval
Figure 7shows the comparison between the simulated masses (TDM, EDM, and PDM) and those
measured in-situ (data used for calibration, displayed by black crosses in the ﬁgure, are not used to
calculate the statistical performances of the model retrieval). The performances were good for the three
parameters (R
2
>0.90 and rRMSE <30%; Table 6, with TNR). The results show that the model was able
to simulate the masses over the full range of mass values (up to around 20 t
·
ha
1
). Nevertheless, the
results show that the model was not able to reproduce the behavior of one speciﬁc point (surrounded
in Figure 7a,c). This point was associated with lower in situ values than those for the simulated ones.
This is explained by the falling of leaves, which occurred just before harvest (around 2100
C day). This
phenomenon, illustrated in Figure 8, was not simulated by the model. Consequently, the simulation
overestimated the total dry mass.
Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 22
7a,c). This point was associated with lower in situ values than those for the simulated ones. This is
explained by the falling of leaves, which occurred just before harvest (around 2100 °C day). This
phenomenon, illustrated in Figure 8, was not simulated by the model. Consequently, the simulation
overestimated the total dry mass.
(a)
(b)
(c)
Figure 7. Comparisons between the measured and simulated dry masses of ear (EDM) (a), plant (PDM)
(b), and the total (TDM) (c). Black and red crosses represent the points used for calibration and
validation, respectively. Only red crosses were used to calculate statistical performances. Black dashed
lines represent a confidence interval of ±10% around the trend line (red line).
The values of the field-specific parameters (D0 and ELUE) are given in Table 5 for the eight studied
fields (F1 to F8). The spread of D0 was in accordance with the different dates of sowing, whereas the
values of ELUE (optimized around 4 g.MJ1) were consistent with the literature [41].
Table 5. Values of the field-specific parameters (D0 and ELUE) derived from the validation step.
Parameter
F1
F2
F3
F4
F5
F6
F7
F8
D0 (DoY)
150
157
137
143
104
133
132
144
ELUE (g.MJ1)
4.32
4.40
3.93
3.79
3.14
3.67
3.73
4.12
Figure 7.
Comparisons between the measured and simulated dry masses of ear (EDM) (
a
), plant
(PDM) (
b
), and the total (TDM) (
c
). Black and red crosses represent the points used for calibration and
validation, respectively. Only red crosses were used to calculate statistical performances. Black dashed
lines represent a conﬁdence interval of ±10% around the trend line (red line).
The values of the ﬁeld-speciﬁc parameters (D
0
and ELUE) are given in Table 5for the eight studied
ﬁelds (F1 to F8). The spread of D
0
was in accordance with the dierent dates of sowing, whereas the
values of ELUE (optimized around 4 g.MJ1) were consistent with the literature [41].
Remote Sens. 2019,11, 1978 11 of 21
Table 5. Values of the ﬁeld-speciﬁc parameters (D0and ELUE) derived from the validation step.
Parameter F1 F2 F3 F4 F5 F6 F7 F8
D0(DoY) 150 157 137 143 104 133 132 144
ELUE (g.MJ1)4.32 4.40 3.93 3.79 3.14 3.67 3.73 4.12
Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 22
Figure 8. Comparisons between simulated and measured masses for the validation field (F1). Dry masses
were divided into total, plant, and ear dry masses (TDM, PDM, and EDM, respectively). Simulations of
the GAI, TDM, PDM, and EDM are represented by a continuous grey line, and dashed red, green, and
black lines, respectively. The falling of leaves just before harvest is clearly visible in the last
measurements, acquired around 2100 °C day.
3.3. Mapping the Production of Corn over One Working Farm
Once validated, the method was applied at a field scale for one working farm composed of six fields
of corn (Figure 9). The approach allowed for the estimation of biomass production just after harvest
(TDM, EDM, and PDM) for all of the considered fields. In our case, the total annual production ranged
from 11.11 to 16.70 t.ha1. The results also show that the ear masses differed between the fields, ranging
from 48 t for the lower production (field F7) to 335 t for the highest production (field F2). At the scale of
the working farm, an amount of 1492 t of corn was produced, distributed as 510 t of plant and 982 t of
ears.
Figure 9. Map of corn masses simulated at harvest for a working farm. TDM, EDM, and PDM were
provided from the simulation controlled by the SAR and optical satellite data.
Figure 8.
Comparisons between simulated and measured masses for the validation ﬁeld (F1). Dry
masses were divided into total, plant, and ear dry masses (TDM, PDM, and EDM, respectively).
Simulations of the GAI, TDM, PDM, and EDM are represented by a continuous grey line, and dashed
red, green, and black lines, respectively. The falling of leaves just before harvest is clearly visible in the
last measurements, acquired around 2100 C day.
3.3. Mapping the Production of Corn over One Working Farm
Once validated, the method was applied at a ﬁeld scale for one working farm composed of six
ﬁelds of corn (Figure 9). The approach allowed for the estimation of biomass production just after
harvest (TDM, EDM, and PDM) for all of the considered ﬁelds. In our case, the total annual production
ranged from 11.11 to 16.70 t
·
ha
1
. The results also show that the ear masses diered between the ﬁelds,
ranging from 48 t for the lower production (ﬁeld F7) to 335 t for the highest production (ﬁeld F2).
At the scale of the working farm, an amount of 1492 t of corn was produced, distributed as 510 t of
plant and 982 t of ears.
Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 22
Figure 8. Comparisons between simulated and measured masses for the validation field (F1). Dry masses
were divided into total, plant, and ear dry masses (TDM, PDM, and EDM, respectively). Simulations of
the GAI, TDM, PDM, and EDM are represented by a continuous grey line, and dashed red, green, and
black lines, respectively. The falling of leaves just before harvest is clearly visible in the last
measurements, acquired around 2100 °C day.
3.3. Mapping the Production of Corn over One Working Farm
Once validated, the method was applied at a field scale for one working farm composed of six fields
of corn (Figure 9). The approach allowed for the estimation of biomass production just after harvest
(TDM, EDM, and PDM) for all of the considered fields. In our case, the total annual production ranged
from 11.11 to 16.70 t.ha1. The results also show that the ear masses differed between the fields, ranging
from 48 t for the lower production (field F7) to 335 t for the highest production (field F2). At the scale of
the working farm, an amount of 1492 t of corn was produced, distributed as 510 t of plant and 982 t of
ears.
Figure 9. Map of corn masses simulated at harvest for a working farm. TDM, EDM, and PDM were
provided from the simulation controlled by the SAR and optical satellite data.
Figure 9.
Map of corn masses simulated at harvest for a working farm. TDM, EDM, and PDM were
provided from the simulation controlled by the SAR and optical satellite data.
Remote Sens. 2019,11, 1978 12 of 21
4. Discussion
4.1. Can Corn Phenological Stages Be Accurately Inferred?
Some of the main phenological stages were simulated by the model at dierent steps. The
phenological stage “four to ﬁve leaves” was derived from the estimate of D
0
, whereas the day of
ﬂowering was determined when the derivative of the function where PRT was equal to zero (Equation
(6)). Ripening was given by S
tt
, and the day of harvest was deﬁned when the simulated GAI decreased
to reach zero. These stages are very important in crop management, as they trigger nutrient uptake,
e.g., nitrogen, phosphorus, potassium [50], and water consumption for grain production [51,52].
Following these assumptions, the model accurately reproduced the main phenological stages on
average (R
2
=0.98 and RMSE =seven days on average; Figure 10). Nevertheless, the ﬁrst stage (four to
ﬁve leaves) was less accurate because of the diculty in detecting the emergence of the crop from the
satellite images acquired at a 10- or 20-m spatial resolution. During the ﬁrst phenological stages, both
the soil and vegetation contributed to the signal, and the small variation in the vegetation masses did
not signiﬁcantly aect the satellite signal at this spatial resolution (>20 m). Thus, it is very dicult
to simulate the dierence in crop development at the beginning of the growing period. Moreover,
the dates of the phenological stages were estimated from the images taken on the day that the in-situ
measurements were performed. In reality, the phenological stage covers a period of a few days around
the date of observation. This phenomenon, not taken into account by the model, induces some oset
between the day of the observed and simulated phenological stages.
Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 22
4. Discussion
4.1. Can Corn Phenological Stages Be Accurately Inferred?
Some of the main phenological stages were simulated by the model at different steps. The
phenological stage four to five leaves was derived from the estimate of D0, whereas the day of
flowering was determined when the derivative of the function where PRT was equal to zero (Equation
(6)). Ripening was given by Stt, and the day of harvest was defined when the simulated GAI decreased to
reach zero. These stages are very important in crop management, as they trigger nutrient uptake, e.g.,
nitrogen, phosphorus, potassium [50], and water consumption for grain production [51,52].
Following these assumptions, the model accurately reproduced the main phenological stages on
average (R2 = 0.98 and RMSE = seven days on average; Figure 10). Nevertheless, the first stage (four to
five leaves) was less accurate because of the difficulty in detecting the emergence of the crop from the
satellite images acquired at a 10- or 20-m spatial resolution. During the first phenological stages, both the
soil and vegetation contributed to the signal, and the small variation in the vegetation masses did not
significantly affect the satellite signal at this spatial resolution (> 20 m). Thus, it is very difficult to
simulate the difference in crop development at the beginning of the growing period. Moreover, the dates
of the phenological stages were estimated from the images taken on the day that the in-situ
measurements were performed. In reality, the phenological stage covers a period of a few days around
the date of observation. This phenomenon, not taken into account by the model, induces some offset
between the day of the observed and simulated phenological stages.
Figure 10. Comparison between the dates of the four simulated and observed phenological stages of corn
(four to five leaves, flowering, ripening, and harvest).
Flowering and ripening were closely grouped in time and well detected, as they are mainly driven
by the variety of the crop (taken into account in the model).
Finally, the date of harvest was always well reproduced. In the formalism of the model, the date of
harvest was simulated when GAIsim reverted to zero after ripening. The decrease of GAIsim was driven by
a non-linear function governed by the GAI, assimilated during senescence and just after harvest. The
date of harvest can be thus accurately estimated when the satellite data are numerous enough to control
Figure 10.
Comparison between the dates of the four simulated and observed phenological stages of
corn (four to ﬁve leaves, ﬂowering, ripening, and harvest).
Flowering and ripening were closely grouped in time and well detected, as they are mainly driven
by the variety of the crop (taken into account in the model).
Finally, the date of harvest was always well reproduced. In the formalism of the model, the date
of harvest was simulated when GAI
sim
reverted to zero after ripening. The decrease of GAI
sim
was
driven by a non-linear function governed by the GAI, assimilated during senescence and just after
harvest. The date of harvest can be thus accurately estimated when the satellite data are numerous
enough to control this non-linear function (this is our case). Nevertheless, the simulated date of harvest
could have been less accurate if the satellite images were missing during this period. Given that the
Remote Sens. 2019,11, 1978 13 of 21
model was only controlled by optical data during this period, the quality of the results was strongly
correlated with cloud cover. To overcome this limitation, the formalism of this process could be revised
by using a threshold for grain humidity, for example, instead of using GAI
sim
. This threshold, deﬁned
as approximately 32–35%, could be provided by seed-producers for each corn variety.
4.2. Can Vegetation Mass Be Retrieved Irrespective the Phenologic Stage?
As illustrated in Figure 7, the biomass can be accurately retrieved up to a couple of days before
harvest. Nevertheless, in some particular cases, the model did not properly simulate the total masses
just before harvest. These dierences can be explained by the falling of the leaves of the corn during
this period (phenomenon already observed for soybean in the literature [
16
]), which is not simulated by
the model. Consequently, simulations strongly overestimated the total masses, particularly ear masses
(Figure 7c), probably inducing an overestimation of the grain yield (not checked in our study because
of the lack of yield data). To overcome this limitation, the falling of leaves should be implemented
in the model by using a temperature function, for example, as this phenomenon cannot be observed
on radar or optical signals [
35
,
37
]. This improvement will be useful for other crops whose leaves fall
during certain phenological stages, e.g., grain maize, soybean, and rapeseed.
4.3. What Is the Impact of the SAR Preprocessing Algorithm?
The Sentinel-1 signal preprocessing algorithms dier from one institute to another, namely:
openSarToolkit (FAO [
53
54
], S1tilling (available at http://tully.ups-tlse.fr/
koleckt/s1tiling, version of May 2019). The main preprocessing steps involved in this algorithm are
illustrated in Figure 11.
Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 22
this non-linear function (this is our case). Nevertheless, the simulated date of harvest could have been
less accurate if the satellite images were missing during this period. Given that the model was only
controlled by optical data during this period, the quality of the results was strongly correlated with
cloud cover. To overcome this limitation, the formalism of this process could be revised by using a
threshold for grain humidity, for example, instead of using GAIsim. This threshold, defined as
approximately 3235%, could be provided by seed-producers for each corn variety.
4.2. Can Vegetation Mass Be Retrieved Irrespective the Phenologic Stage?
As illustrated in Figure 7, the biomass can be accurately retrieved up to a couple of days before
harvest. Nevertheless, in some particular cases, the model did not properly simulate the total masses just
before harvest. These differences can be explained by the falling of the leaves of the corn during this
period (phenomenon already observed for soybean in the literature [16]), which is not simulated by the
model. Consequently, simulations strongly overestimated the total masses, particularly ear masses
(Figure 7c), probably inducing an overestimation of the grain yield (not checked in our study because of
the lack of yield data). To overcome this limitation, the falling of leaves should be implemented in the
model by using a temperature function, for example, as this phenomenon cannot be observed on radar
or optical signals [35,37]. This improvement will be useful for other crops whose leaves fall during
certain phenological stages, e.g., grain maize, soybean, and rapeseed.
4.3. What Is the Impact of the SAR Preprocessing Algorithm?
The Sentinel-1 signal preprocessing algorithms differ from one institute to another, namely:
openSarToolkit (FAO [53]), Google Earth Engine [54], S1tilling (available at
http://tully.ups-tlse.fr/koleckt/s1tiling, version of May 2019). The main preprocessing steps involved in
this algorithm are illustrated in Figure 11.
Figure 11. Main steps involved in the preprocessing radar signal, according to the algorithm used by
FAO (OpenSar toolkit), Google (GEE; Google Earth Engine), and the French national space institute:
CNES (S1-Tiling).
All of the algorithms integrate the steps of border noise removal [55], and radiometric and
geometric correction [56]. The two main differences concern the application or not of thermal noise
removal (TNR) and of the speckle filter [57]. The impacts of these two algorithms in terms of
backscattering coefficients and biomass estimates are investigated in the two following subsections.
Figure 11.
Main steps involved in the preprocessing radar signal, according to the algorithm used by
FAO (OpenSar toolkit), Google (GEE; Google Earth Engine), and the French national space institute:
CNES (S1-Tiling).
All of the algorithms integrate the steps of border noise removal [
55
geometric correction [
56
]. The two main dierences concern the application or not of thermal noise
removal (TNR) and of the speckle ﬁlter [
57
]. The impacts of these two algorithms in terms of
backscattering coecients and biomass estimates are investigated in the two following subsections.
4.3.1. Impact of the Speckle Filter on the Backscattering Coecient at the Field Scale
Figure 12 compares the
σVH/VV
(median value) extracted at a ﬁeld scale along the crop growing
season, over all the popcorn ﬁelds, with or without applying a 3
×
3 window adaptive Lee ﬁlter. With
respect to the mean, the signals were strongly correlated with the y =x regression line (R
2
=0.99). This
result conﬁrms that the application of a speckle ﬁlter has few impact on the SAR signal values extracted
Remote Sens. 2019,11, 1978 14 of 21
at the ﬁeld scale, as this ﬁlter conserves the mean value inside a homogenous area [
57
]. Nevertheless,
the backscattering ratio is not exactly the same because (1) the median values are displayed in Figure 12
(not the mean values), and (2) some ﬁelds have a too much of an elongated shape to apply the adaptive
Lee ﬁlter properly. The only assumption of the ﬁlter is that the sample mean and variance of a pixel are
equal to its local mean and variance, based on the pixels within a ﬁxed neighborhood surrounding
it [
57
]. When the studied ﬁelds have too much of an elongated shape (in the azimuth or range direction
of the image), the neighborhood around one pixel is composed of a mixed-pixel (deﬁned as a mixel).
Contrary to pure pixels, mixels contain multiple constituents within a single pixel (corn, forest, bare
soil, river, etc.), which can blur the value of backscattering coecients to some extent.
These two combined eects—median instead of mean, and shape of the ﬁeld—explain the slight
scattering of the backscattering coecients (RMSE =0.18dB) observed in Figure 12.
Remote Sens. 2019, 11, x FOR PEER REVIEW 15 of 22
4.3.1. Impact of the Speckle Filter on the Backscattering Coefficient at the Field Scale
Figure 12 compares the σ°VH/VV (median value) extracted at a field scale along the crop growing
season, over all the popcorn fields, with or without applying a 3 × 3 window adaptive Lee filter. With
respect to the mean, the signals were strongly correlated with the y = x regression line (R2 = 0.99). This
result confirms that the application of a speckle filter has few impact on the SAR signal values extracted
at the field scale, as this filter conserves the mean value inside a homogenous area [57]. Nevertheless, the
backscattering ratio is not exactly the same because (1) the median values are displayed in Figure 12 (not
the mean values), and (2) some fields have a too much of an elongated shape to apply the adaptive Lee
filter properly. The only assumption of the filter is that the sample mean and variance of a pixel are
equal to its local mean and variance, based on the pixels within a fixed neighborhood surrounding it
[57]. When the studied fields have too much of an elongated shape (in the azimuth or range direction of
the image), the neighborhood around one pixel is composed of a mixed-pixel (defined as a mixel).
Contrary to pure pixels, mixels contain multiple constituents within a single pixel (corn, forest, bare soil,
river, etc.), which can blur the value of backscattering coefficients to some extent.
These two combined effectsmedian instead of mean, and shape of the fieldexplain the slight
scattering of the backscattering coefficients (RMSE = 0.18dB) observed in Figure 12.
Figure 12. Comparison of the backscattering coefficient (σ°VH/VV) extracted at a field scale (between 0 and
1000 °C day), with or without considering a 3 × 3 window adaptive Lee filter. Black and blue crosses
represent the backscattering coefficients acquired in orbit 132 and 30, respectively.
Figure 13b,d,f presents the effects of the filter on the estimates of EDM, TDM, and PDM compared
to the simulation performed using no filtered backscattering coefficients (at the date of the ground
measurements). The relationships estimated between the masses could be considered quite similar for
EDM, TDM, and PDM, except for some points surrounded by red ellipsoids.
These points correspond to the simulations performed in field F4, the more elongated field of the
study, namely: 420 m long by 70 m wide on average (Figure 1). This size must be compared with the
native spatial resolution of the SAR data, which is 20 m (in the range direction) by 22 m (in the azimuth
direction). This means that a maximum of three pixels are pure along the range direction. For this field,
the median backscattering coefficients (σ°VH/VV) were superior when the filter was applied. The
overestimation of σ°VH/VV induced an overestimation of the associated GAISAR and thus justified the
higher simulations of the dry masses, as observed in Figure 13b,d,f. This phenomenon was not as
pronounced on others fields, as they were larger and had a more regular rectangular shape.
Figure 12.
Comparison of the backscattering coecient (
σVH/VV
) extracted at a ﬁeld scale (between
0 and 1000
C day), with or without considering a 3
×
3 window adaptive Lee ﬁlter. Black and blue
crosses represent the backscattering coecients acquired in orbit 132 and 30, respectively.
Figure 13b,d,f presents the eects of the ﬁlter on the estimates of EDM, TDM, and PDM compared
to the simulation performed using no ﬁltered backscattering coecients (at the date of the ground
measurements). The relationships estimated between the masses could be considered quite similar for
EDM, TDM, and PDM, except for some points surrounded by red ellipsoids.
These points correspond to the simulations performed in ﬁeld F4, the more elongated ﬁeld of
the study, namely: 420 m long by 70 m wide on average (Figure 1). This size must be compared with
the native spatial resolution of the SAR data, which is 20 m (in the range direction) by 22 m (in the
azimuth direction). This means that a maximum of three pixels are pure along the range direction. For
this ﬁeld, the median backscattering coecients (
σVH/VV
) were superior when the ﬁlter was applied.
The overestimation of
σVH/VV
induced an overestimation of the associated GAI
SAR
and thus justiﬁed
the higher simulations of the dry masses, as observed in Figure 13b,d,f. This phenomenon was not as
pronounced on others ﬁelds, as they were larger and had a more regular rectangular shape.
4.3.2. Impact of Thermal Noise Removal (TNR) on Backscattering Coecients and on Mass Retrieval
Figure 14 shows the inﬂuence of the thermal noise removal on the backscattering coecient
acquired at VH polarization, according to the acquisition orbit (30 or 132). The thermal noise comes
from the electronic circuits and is one of the major sources of noise in microwaves. It mainly aects
the signal when the signal to noise ratio (SNR) is low. Given that the cross-polarization signal is
less powerful for corn than the co-polarization [
35
,
37
], this phenomenon mainly aects the cross
Remote Sens. 2019,11, 1978 15 of 21
polarization states. In the case of corn, the results of Figure 14 show that this correction can reach 2 dB
in the worst case, when the VH decreases to
24 dB. Conversely, this correction is weak (less than
0.5 dB) when the HV signal becomes greater than 16 dB.
Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 22
Figure 13. Comparison of simulated EDM (a,b), PDM (c,d), TDM (e,f) with and without considering the
thermal noise removal (left column) or the speckle filter (right column).
4.3.2. Impact of Thermal Noise Removal (TNR) on Backscattering Coefficients and on Mass Retrieval
Figure 14 shows the influence of the thermal noise removal on the backscattering coefficient
acquired at VH polarization, according to the acquisition orbit (30 or 132). The thermal noise comes from
the electronic circuits and is one of the major sources of noise in microwaves. It mainly affects the signal
when the signal to noise ratio (SNR) is low. Given that the cross-polarization signal is less powerful for
corn than the co-polarization [35,37], this phenomenon mainly affects the cross polarization states. In the
case of corn, the results of Figure 14 show that this correction can reach 2 dB in the worst case, when the
VH decreases to 24 dB. Conversely, this correction is weak (less than 0.5 dB) when the HV signal
becomes greater than 16 dB.
In VV polarization, there is no significant difference with or without the thermal noise removal, as
the SNR is superior to the one obtained using VH signal (results not shown here).
Figure 13.
Comparison of simulated EDM (
a
,
b
), PDM (
c
,
d
), TDM (
e
,
f
) with and without considering
the thermal noise removal (left column) or the speckle ﬁlter (right column).
In VV polarization, there is no signiﬁcant dierence with or without the thermal noise removal, as
the SNR is superior to the one obtained using VH signal (results not shown here).
The impact of the thermal noise on mass retrieval (EDM, PDM, and TDM) is illustrated in
Figure 13a,c,e. In this ﬁgure, the masses simulated using the GAI
SAR
derived from the backscattering
signal corrected from the thermal noise (x-axes) are presented, versus those simulated without the
thermal noise (y-axes). The results show that the performances for mass retrieval were strongly
degraded when the TNR was not considered (Table 6). The relative errors exceeded 58%, whereas
they were lower than 30% when the radar signal was corrected for thermal noise. Moreover, the
mean coecients of determination decreased on average from 0.91 to 0.72. This phenomenon can be
explained by the fact that
σVH
was overestimated when not corrected for thermal noise, especially
when the signal was low, as illustrated in Figure 14. This phenomenon was particularly well marked
during the ﬁrst phenological stages of the crop, just after sowing, when soil was still bare and dry. The
overestimation of
σVH
directly impacted the value of D
0
because the model wrongly considered that
Remote Sens. 2019,11, 1978 16 of 21
the vegetation started growing early in the season. Consequently, D
0
decreased, and the duration of
the crop cycle lengthened until harvest. This led to an overestimation of the simulated masses (EDM,
PDM, and TDM) compared to the ground measurements.
Remote Sens. 2019, 11, x FOR PEER REVIEW 17 of 22
Figure 14. Comparison of backscattering coefficient extracted at a field scale (VH polarization), with or
without considering the thermal noise correction. Black and blue crosses represent the backscattering
coefficients acquired in orbit 132 and 30, respectively.
The impact of the thermal noise on mass retrieval (EDM, PDM, and TDM) is illustrated in Figure
13a,c,e. In this figure, the masses simulated using the GAISAR derived from the backscattering signal
corrected from the thermal noise (x-axes) are presented, versus those simulated without the thermal
noise (y-axes). The results show that the performances for mass retrieval were strongly degraded when
the TNR was not considered (Table 6). The relative errors exceeded 58%, whereas they were lower than
30% when the radar signal was corrected for thermal noise. Moreover, the mean coefficients of
determination decreased on average from 0.91 to 0.72. This phenomenon can be explained by the fact
that σ°VH was overestimated when not corrected for thermal noise, especially when the signal was low,
as illustrated in Figure 14. This phenomenon was particularly well marked during the first phenological
stages of the crop, just after sowing, when soil was still bare and dry. The overestimation of σ°VH directly
impacted the value of D0 because the model wrongly considered that the vegetation started growing
early in the season. Consequently, D0 decreased, and the duration of the crop cycle lengthened until
harvest. This led to an overestimation of the simulated masses (EDM, PDM, and TDM) compared to the
ground measurements.
Table 6. Statistical performances of the mass inversion (EDM, PDM, and TDM) with or without
considering the thermal noise removal and speckle filter. TNRthermal noise removal.
n
a
b
RMSE
rRMSE
with TNR and without speckle filter
EDM
19
0.93
0.87
0.11
1.07
28.70
PDM
19
0.90
0.65
0.16
0.59
15.67
TDM
19
0.92
0.85
0.14
1.77
23.56
with TNR and with speckle filter
EDM
19
0.95
0.96
0.08
0.94
25.32
PDM
19
0.68
0.65
0.92
1.23
32.74
TDM
19
0.95
0.92
1.09
1.46
19.44
without TNR
EDM
19
0.94
1.81
0.57
2.19
58.57
PDM
19
0.36
0.99
3.34
3.72
98.77
TDM
19
0.87
1.65
4.02
4.46
59.40
Figure 14.
Comparison of backscattering coecient extracted at a ﬁeld scale (VH polarization), with or
without considering the thermal noise correction. Black and blue crosses represent the backscattering
coecients acquired in orbit 132 and 30, respectively.
Table 6.
Statistical performances of the mass inversion (EDM, PDM, and TDM) with or without
considering the thermal noise removal and speckle ﬁlter. TNR—thermal noise removal.
nR2a b RMSE rRMSE
with TNR and without speckle ﬁlter
EDM 19 0.93 0.87 0.11 1.07 28.70
PDM 19 0.90 0.65 0.16 0.59 15.67
TDM 19 0.92 0.85 0.14 1.77 23.56
with TNR and with speckle ﬁlter
EDM 19 0.95 0.96 0.08 0.94 25.32
PDM 19 0.68 0.65 0.92 1.23 32.74
TDM 19 0.95 0.92 1.09 1.46 19.44
without TNR
EDM 19 0.94 1.81 0.57 2.19 58.57
PDM 19 0.36 0.99 3.34 3.72 98.77
TDM 19 0.87 1.65 4.02 4.46 59.40
4.4. What Is the Impact of an Intercrop?
Intercrops play a major role in the quality of mass estimates. As illustrated in Figure 15, intercrops
strongly aected the signal
σ0
VH/VV
at the beginning of the corn cycle (between 0 and 500
C day).
In our case, these crops were mainly composed of beans (Vicia faba L.). They were sown at the end of
the year (before October–November) and were mechanically and chemically destroyed in May–June
2016, when corn began vegetative development. To restrain the impact of the SAR signal on the
simulations, only
σ0
VH/VV
higher than
7.5 dB was assimilated (level deﬁned by the intersection of the
black and green curves in Figure 15 at the bottom). The GAI
opt
estimated from the optical data was not
aected by the presence of this crop, as it was undergoing senescence (Figure 15, at the top). At this
stage, the color of the crop was close to the bare soil color (yellow, brown), and the BVnet algorithm
(used to invert the GAI
opt
) considered the reﬂectance to be the same as that observed for bare soil.
Consequently, the algorithm simulated a GAI close to 0.
Remote Sens. 2019,11, 1978 17 of 21
Remote Sens. 2019, 11, x FOR PEER REVIEW 18 of 22
4.4. What Is the Impact of an Intercrop?
Intercrops play a major role in the quality of mass estimates. As illustrated in Figure 15, intercrops
strongly affected the signal 𝜎𝑉𝐻/𝑉𝑉
0 at the beginning of the corn cycle (between 0 and 500 °C day). In our
case, these crops were mainly composed of beans (Vicia faba L.). They were sown at the end of the year
(before OctoberNovember) and were mechanically and chemically destroyed in MayJune 2016, when
corn began vegetative development. To restrain the impact of the SAR signal on the simulations, only
𝜎𝑉𝐻/𝑉𝑉
0 higher than 7.5 dB was assimilated (level defined by the intersection of the black and green
curves in Figure 15 at the bottom). The GAIopt estimated from the optical data was not affected by the
presence of this crop, as it was undergoing senescence (Figure 15, at the top). At this stage, the color of
the crop was close to the bare soil color (yellow, brown), and the BVnet algorithm (used to invert the
GAIopt) considered the reflectance to be the same as that observed for bare soil. Consequently, the
algorithm simulated a GAI close to 0.
Figure 15. Temporal evolution of the GAIopt (a) and backscattering ratio 𝜎𝑉𝐻/𝑉𝑉
0 (b) according to the
presence of intercrops (beans) inside the corn plot.
Without precaution, the impact of intercrops could affect the empirical function used to estimate
the GAI from the SAR signals (Equation (8)). Figure 16 illustrates the impact of intercrops on the
relationship estimated between GAI and 𝜎𝑉𝐻/𝑉𝑉
0. With intercrops, 𝜎𝑉𝐻/𝑉𝑉
0 strongly increased (until 5 dB),
whereas the GAI of the corn remained low (less than 0.5 m2.m2). It is thus very important to remove a
parcel with intercrops to estimate this relationship.
Figure 15.
Temporal evolution of the GAI
opt
(
a
) and backscattering ratio
σ0
VH/VV
(
b
) according to the
presence of intercrops (beans) inside the corn plot.
Without precaution, the impact of intercrops could aect the empirical function used to estimate
the GAI from the SAR signals (Equation (8)). Figure 16 illustrates the impact of intercrops on the
relationship estimated between GAI and
σ0
VH/VV
. With intercrops,
σ0
VH/VV
strongly increased (until
5 dB), whereas the GAI of the corn remained low (less than 0.5 m
2·
m
2
). It is thus very important to
remove a parcel with intercrops to estimate this relationship.
Remote Sens. 2019, 11, x FOR PEER REVIEW 19 of 22
Figure 16. Impact of the presence of an intercrop on the empirical relationship estimated between σVH/VV
0
and the GAI of corn.
5. Conclusions
The aim of this work was to estimate the temporal evolution of popcorn dry masses (ear, plant, and
total amount) at a field scale using an agro-meteorological model (SAFY-WB) controlled by GAI,
derived from optical (GAIopt) and synthetic aperture radar (SAR) (GAIsar) satellite images and total dry
masses (TDM) measured in-situ.
The results show that once calibrated for one field, the total dry masses were accurately simulated
with a daily time step in the range 0 to 20 t.ha1 (R2 = 0.92; rRMSE = 23%). The simulated TDM must be
nevertheless corrected from the underestimation trend, by applying a scaling factor equal to 1.17
(TDMsim = 0.85.TDMmeas + 0.14) in order to obtain accurate absolute values. Moreover, the use of the
model permitted separating the TDM into ear and plant dry masses (EDM and PDM) with good
performances (R2 > 0.90). The approach also permitted accurate simulation (seven days of offset) of the
following four phenological stages: four to five leaves, flowering, ripening, and harvest. The Discussion
section pointed out some important points to consider for a good estimate of dry masses of corn during
the preprocessing steps. In our study case, it was not wise to apply a speckle filter to the SAR data
(because of the specific shape of some of the fields), whereas it was important to apply the thermal noise
removal in order to obtain an accurate simulation of the dry masses.
From an agronomic point of view, the results have shown that new agricultural techniques (with
intercrop management) significantly affects the SAR signal, which becomes unusable for establishing
the relationship with GAI during the first phenological stages of the crop. This phenomenon was not
observed when plots of corn were cultivated traditionally without intercrops.
This work could be extended to larger areas governed by heterogeneous climatic conditions. In this
case, a dense network of weather stations is required to control the agro-meteorological model with local
climatic measurements (wind, temperature, radiation, etc.). Moreover, the limitation in GAISAR retrieval
could be overcome by using a lower frequency signal to control the model, similar to that offered by
ALOS-2 PALSAR sensors (L-band).
Author Contributions: F.B.: writing original draft preparation, methodology, analysis and supervision; M.A:
software, validation and reference data; F.B. R.F, F.F. and S.C.: writingreview and editing; F.B. and J-F. B.: funding
acquisition.
Figure 16.
Impact of the presence of an intercrop on the empirical relationship estimated between
σ0
VH/VV and the GAI of corn.
5. Conclusions
The aim of this work was to estimate the temporal evolution of popcorn dry masses (ear, plant,
and total amount) at a ﬁeld scale using an agro-meteorological model (SAFY-WB) controlled by GAI,
derived from optical (GAI
opt
) and synthetic aperture radar (SAR) (GAI
sar
) satellite images and total
dry masses (TDM) measured in-situ.
Remote Sens. 2019,11, 1978 18 of 21
The results show that once calibrated for one ﬁeld, the total dry masses were accurately simulated
with a daily time step in the range 0 to 20 t
·
ha
1
(R
2
=0.92; rRMSE =23%). The simulated TDM must
be nevertheless corrected from the underestimation trend, by applying a scaling factor equal to 1.17
(TDM
sim
=0.85.TDM
meas
+0.14) in order to obtain accurate absolute values. Moreover, the use of
the model permitted separating the TDM into ear and plant dry masses (EDM and PDM) with good
performances (R
2
>0.90). The approach also permitted accurate simulation (seven days of oset)
of the following four phenological stages: four to ﬁve leaves, ﬂowering, ripening, and harvest. The
Discussion section pointed out some important points to consider for a good estimate of dry masses of
corn during the preprocessing steps. In our study case, it was not wise to apply a speckle ﬁlter to the
SAR data (because of the speciﬁc shape of some of the ﬁelds), whereas it was important to apply the
thermal noise removal in order to obtain an accurate simulation of the dry masses.
From an agronomic point of view, the results have shown that new agricultural techniques (with
intercrop management) signiﬁcantly aects the SAR signal, which becomes unusable for establishing
the relationship with GAI during the ﬁrst phenological stages of the crop. This phenomenon was not
observed when plots of corn were cultivated traditionally without intercrops.
This work could be extended to larger areas governed by heterogeneous climatic conditions.
In this case, a dense network of weather stations is required to control the agro-meteorological model
with local climatic measurements (wind, temperature, radiation, etc.). Moreover, the limitation in
GAI
SAR
retrieval could be overcome by using a lower frequency signal to control the model, similar to
that oered by ALOS-2 PALSAR sensors (L-band).
Author Contributions:
F.B.: writing—original draft preparation, methodology, analysis and supervision; M.A.:
software, validation and reference data; F.B. R.F., F.F. and S.C.: writing—review and editing; F.B. and J.-F.B.:
funding acquisition.
Funding:
This research is part of the PRECIEL project, funded by ACMG, Agralis, Nouvelle-Aquitaine Region,
European Union, and CESBIO, and certiﬁed by Agri Sud-Ouest Innovation.
Acknowledgments:
We are very grateful to the farmers involved (the Nataïs society, M. Labedan, the domain of
Villeneuve, M. Darrieux), and to Sylvaine Laburthe for her valuable English proofreading.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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... The SAFYE model combines the SAFY model with a water balance component, aiming to simulate crop growth with the temporal evolution of LAI, dry masses, evapotranspiration, and soil water content. It has been validated for different crop types, such as wheat (Dong et al., 2016;Duchemin et al., 2015;Zhang et al., 2019a), corn (Battude et al., 2017;Baup et al., 2019), soybean (Betbeder et al., 2016;Liao et al., 2019), in various climates. The basic theory of the SAFYE model was the same as SAFY that crop above-ground phytomass was converted from solar radiation based on the light-use efficiency model (Monteith, 1972). ...
... In later-stage water deficit treatments, the model did not properly simulate the total harvested biomass, with a slight overestimation. A similar situation has been reported by (Baup et al., 2019), who observed a large overestimation in total harvested biomass. This could be due to dropping of the leaves from plants during the maturity stage (Betbeder et al., 2016). ...
... This could be due to dropping of the leaves from plants during the maturity stage (Betbeder et al., 2016). Therefore, Baup et al. (2019) proposed to implement the falling leaves module for this situation. ...
Article
Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural water management in arid and semi-arid regions. For this purpose, we developed a generic data assimilation methodology by integrating both the Shuffled Complex Evolution (SCE) and the Ensemble Kalman Filter (EnKF) algorithms into the Simple Algorithm For Yield and Evapotranspiration (SAFYE) model to provide improved simulation of winter wheat biomass and yield, and simulation of evapotranspiration (ET) under different water-supply scenarios. An experiment with nine irrigation scenarios was conducted during the 2013-2015 growing cycles. Field spectral data were employed to retrieve the leaf area index (LAI), which was then used as a single state variable to determine other parameters in the SAFYE model using a global optimization algorithm. Time-series LAI was eventually assimilated in the SAFYE model based on the EnKF algorithm to improve overall model simulation. The results showed that the simulated crop growth dynamics followed the measurements well in most cases when the estimated LAI was assimilated. The accuracy of simulated biomass at the daily step was improved, with the maximum RMSE decreased from 199.4 to 123.8 g m − 2 and from 466.6 to 393.4 g m − 2 for the 2013-2014 and 2014-2015 growing seasons respectively. A good agreement was also achieved between the estimated and field measured grain yield (R 2 = 0.901, RMSE= 31.9 g m − 2 , RRMSE=6.55%) for both growing seasons. The simulation of soil water content in the top 0-20 cm soil layer was better (RMSE: 3.3-5.0 mm) than that of 0-100 cm layer (RMSE: 11.7-29.6 mm). Accuracy of the simulated ET under early-stage water deficit scenarios was lower than that under other scenarios, with a positive mean relative error of 14% (3.4-24.3%) during two growing seasons. This study demonstrates the great potential of coupling remote sensing data to improve the performance of SAFYE model in modeling winter wheat growth.
... [25] and [38] have already demonstrated the benefits of the simultaneous assimilation of optical and SAR data into SAFY for maize monitoring. They showed better results for DM and yield retrieval by the combined assimilation of LAI derived from Landsat-8 and LAI derived from backscatter coefficients of Sentinel-1. ...
... In this study, contrary to the previous ones assimilating remote sensing data in the SAFY model [19], [25], [26], [37], [38], we did not perform a calibration step to fix the values of the parameters previously considered as crop specific (i.e. PLa, PLb, STT, RS, SW, RW and SPW). ...
Article
Full-text available
This paper investigates the potential of the assimilation of both Synthetic Aperture Radar (SAR) -derived Dry Mass (DM) and optically-derived Green Area Index (GAI) in an agro-meteorological model (i.e. SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R2 = 0.84 and 0.92, respectively and relative Root Mean Square Error, RMSEr = 41 % and 28 %, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or mono-sensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push towards the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SAR-derived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.
... Fortunately, recent Sentinel 2 satellite missions provide observations at high spatial resolution (10m) every 5 days all over the globe which could partially solve this problem. Also, it was shown that the combined use of optical and Synthetic Aperture Radar (SAR) satellite data, like Sentinel 1, can overcome this issue (see Ameline et al., 2018;Baup et al., 2019;Betbeder et al., 2016;Remy Fieuzal et al., 2017;Revill et al., 2013). Indeed, the signal of the SAR satellites is not affected by clouds and they can even observe the surface at night. ...
... This constraint is partially mitigated because of the ongoing Sentinel missions that will provide high spatial (10 m) and temporal (5 days) resolution images all across the globe. Additionally, the assimilation of the C-band synthetic-aperture radar (SAR) from Sentinel-1 should be considered to lift this constraint, as shown in many studies (Ameline et al., 2018;Baup et al., 2019;R. Fieuzal et al., 2017a;Revill et al., 2013a). ...
Thesis
Les changements climatiques et la croissance démographique de la population mondiale amènent aujourd'hui le monde agricole à s'adapter pour faire face à ces deux enjeux majeurs. Si les surfaces agricoles, qui représentent près d'un tiers des terres émergées, contribuent largement aux émissions mondiales de gaz à effet de serre, elles offrent également la possibilité de mettre en place des leviers d'atténuation des changements climatiques. Dans ce contexte, ces travaux de thèse ont vocation à enrichir nos connaissances sur le fonctionnement des surfaces agricoles, à fournir des outils d'évaluation de la contribution des surfaces cultivées aux évolutions du climat, et à quantifier les effets biogéochimiques (stockage de C) et biogéophysiques (effet albédo) d'atténuation des changements climatiques via la mise en œuvre de cultures intermédiaires. Pour répondre à ces objectifs, deux approches de modélisation ont été développées au cours de ces travaux. Le premier volet de cette thèse s'est intéressé à développer une approche de modélisation spatialisée, permettant de fournir des estimations des productions (biomasses et rendements), des flux de CO2 et d'eau, ces variables servant à la quantification des bilans de carbone et d'eau pour les parcelles de grandes cultures. À cette fin, le modèle agro-météorologique SAFYE-CO2 assimilant des produits satellites d'indice de végétation à hautes résolutions spatiale et temporelle a été développé et appliqué à différentes cultures (blé, maïs et tournesol) et végétations d'intercultures (repousses spontanées, mauvaises herbes, cultures intermédiaires). Cette approche a pu être validée sur un réseau de parcelles du Sud-Ouest de la France, en tirant parti d'un grand nombre d'images satellites et de données de validation sur la zone de l'Observatoire Spatial Régional. Elle a notamment permis d'estimer avec précision les productions de blé, de tournesol et de maïs, ainsi que les flux de CO2 et d'eau sur les cultures de blé et de tournesol. La végétation, pouvant se développer sur les parcelles pendant les périodes d'interculture, a également été prise en compte afin d'améliorer l'estimation des flux de CO2 et d'eau. Cela a notamment permis de quantifier l'impact des cultures intermédiaires sur les composantes du bilan C des parcelles allouées aux grandes cultures sur la zone d'étude. Le second volet visait à développer un modèle d'introduction de cultures intermédiaires à l'échelle européenne, afin d'estimer le forçage radiatif induit par la modification de l'albédo de surface engendré par cette pratique. Grace à des produits albédo moyenne résolution (1/20°), développés par le CNRM (et en collaboration avec ce laboratoire), cette approche de modélisation a permis de fournir des estimations de l'effet albédo relatifs aux cultures intermédiaires. Plusieurs scenarii d'introduction ont été simulés pour rendre compte de l'impact de certains facteurs, tels que la neige ou la pluie. Ils ont permis d'alerter sur le potentiel impact négatif de l'assombrissement du sol, induit à long terme (via l'enrichissement des sols en matière organique) par les cultures intermédiaires sur le forçage radiatif des surfaces cultivées. Enfin, comme tout changement de pratique agricole induit des effets biogéochimiques et biogéophysiques sur le climat, une analyse de ces effets couplés a été menée grâce à l'utilisation combinée de ces deux approches de modélisation. Nous en concluons qu'une fois les cultures intermédiaires mises en place, le sol devrait être couvert en permanence pour que l'effet assombrissement du sol ne fasse pas perdre les autres bénéfices climatiques engendrés par cette pratique agricole.
... Oliveira et al. [30], for instance, investigated the phosphorus effect on corn dry mass. Baup et al. [31], in turn, developed a simple algorithm to estimate daily changes in corn dry mass in the field based on a satellite-derived Green Area Index. Finally, Oliveira et al. [32] examined the effects of manganese and silicon on corn dry mass. ...
Article
Full-text available
This work aimed to evaluate the effect of calcium on the development of corn plants grown with the omission and excess of calcium in a nutrient solution. The experiment was conducted in a greenhouse from March to May 2012. Three concentrations of calcium (0, 200, and 600 mg Ca L−1) were added to the nutrient solution, which was renewed weekly, for a total of 40 days. The following variables were measured weekly: the number of leaves, average stem diameter, dry weight of the plant shoots and roots, and visual leaf diagnosis. The results showed that when the plants were deprived of calcium, their root systems were significantly reduced, as determined by the Tukey test (p ≤ 0.05). The plants with calcium deprivation had shorter roots and a dark brown color and displayed initial symptoms of chlorosis in their young leaves, which eventually led to necrosis and tipping. Hydroponics is promising and has shown satisfactory production results, contributing to the improvement of the environment, job creation, and increased profit for rural producers.
... Consequently, the possibility to drive these two dependent variables by means of independent time series (SAR-derived DM and optically-derived GAI) should provide the right conditions for optimizing rapeseed monitoring and yields modeling. Benefits of combined assimilation of optically-derived GAI and SAR-derived DM in agrometeorological models have been already proved for maize [25], [26], soybean [50], [51] or sunflower [52] but remained to be demonstrated for rapeseed. Besides, the analysis of residuals distribution by phenological stages carried out in section III.D offered the opportunity to develop an assimilation strategy by periods through a weighing scheme according to the confidence in SAR and/or optical -based relationship for each phenological stage. ...
Article
Full-text available
This paper aims to evaluate the potential of multi-temporal and multi-orbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive Normalized Difference Vegetation Index (NDVI), green fraction cover (fCover) and Green Area Index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by Synthetic Aperture Radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these Remote Sensing Indicators (RSI) were physically interpreted, compared each other and to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017-2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement of their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on: the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest) and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multi-orbital SAR data can be merged without significantly degrading the performance of SAR-based relationships, while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring.
... Soil water content may be estimated from active or passive microwave signatures since such signatures are sensitive to the dielectric constant of the soil (Brisco and Brown 1998;Martínez-Fernández et al. 2016). At present, the spatial resolution of satellite SAR sensors is high (1~10 m), but the operational application of SAR sensors to smart farming is still at a research stage (Lopez-Sanchez, Hajnsek, and Ballester-Berman 2012; Inoue, Sakaiya, and Wang 2014; Baup et al. 2019). ...
Article
Present climate and socioeconomic issues would threaten the global food and environmental security. Smart farming (SF) based on advances in sensing, robotic, and information and communication technologies is a promising approach to support the efficient, sustainable, and profitable crop production. This paper discusses the background needs for SF and the role of remote sensing. Recent advances in remote sensing technology (platforms, sensors, algorithms) for diagnostic information of crops and soils are reviewed based on some leading case studies. The operation of a bundle of similar satellites (constellation) allows timely or frequent observations, and their spatial resolution (1 ~ 10 m) is applicable to agricultural regions of relatively small farmlands. The efficient use of high-resolution satellite sensors would strongly support the diagnostics and decision-making in SF on regional scales. Drone-based remote sensing would allow low-cost, high resolution, and flexible observations of crops and soils. Diagnostic information on crop growth, water stress, soil fertility, weed, disease, lodging, and 3D topography can be created from the optical, thermal and/or video images. The linkage between the remote sensing function and drone-based application of seeds, pesticides, fertilizes would greatly enhance the efficiency of labor and material applications and profitability.
... This constraint is partially mitigated because of the ongoing Sentinel missions that will provide high spatial-(10 m) and temporal-resolution (5 days) images all across the globe. Additionally, the assimilation of the C-band synthetic-aperture radar (SAR) from Sentinel-1 should be considered to lift this constraint, as shown in many studies [71][72][73][74]. Finally, thermal-infrared remote sensing, which allows the estimation of vegetation water stress [75], could be considered to better constrain the model. ...
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