# Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data

Remy Fieuzal, Claire Marais Sicre, Frederic Baup

► To cite this version:

Remy Fieuzal, Claire Marais Sicre, Frederic Baup. Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10 (12), pp.5412-5422. 10.1109/JS-TARS.2017.2737656. [hal-04774374](https://hal.science/hal-04774374v1)

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# Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data

Remy Fieuzal,¹ Claire Marais Sicre, and Frederic Baup

**Abstract**—This paper aims at estimating the yield of sunflower at field scale by testing different ways to control an agrometeorological model, using the leaf area index (LAI) and/or the dry mass (DM) of the crop. The LAI is derived from multitemporal satellite images acquired in the visible, near-infrared, and microwave wavelengths by five sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2), over a region located in southwestern France, while the DM is measured in situ. Results show that the temporal dynamics of LAI and DM can be accurately simulated over the five studied working farms ($R^2_{DM} > 0.85$ and $R^2_{LAI} > 0.94$ with a relative root-mean-square error (RMSE) < 30%), when the model is controlled by both the DM (measured over only one field) and the LAI (estimated from the modified triangular vegetation index (MTVI2) or $\sigma^{\circ C_{-VV}$). The seed yield is then accurately estimated ($R^2 > 0.85$) over 140 ha, with low errors ranging from 0.20 to $0.54 \text{ t·ha}^{-1}$ by using both the LAI derived from microwave and optical data (LAI<sub>M TVI2</sub> and LAI<sub>σ°C_{-VV</sub>), in order to reduce the overestimations related to the excessive development of the LAI during the first phenological stages, or to the presence of dew on the vegetation, respectively, affecting the LAI derived from the MTVI2 or $\sigma^{\circ C_{-VV}}$.

**Index Terms**—Crop modeling, dry mass (DM), leaf area index (LAI), multifrequency, multipolarization, optical, sunflower, synthetic aperture radar (SAR), yield.

## I. INTRODUCTION

THE estimate and the forecasting of agricultural yields represent a key issue in the context of demographic pressure and climate change. The identification of tools and the development of new methodologies useful for crop monitoring are, thus, required to get worldwide information of crops. To this end, the combination of in-situ punctual measurements with the regular observation capabilities over large areas offered by

satellite imagery appears to be a well-adapted tool to monitor accurately agricultural surfaces [1]–[3]. Numerous studies have clearly demonstrated the interest of using high resolution (<50 m) satellite images to accurately map the land use [4], the agricultural practices (irrigation [5], tillage [6], row orientation [7], seeding [8]), or to detect specific events and to estimate crop parameters (phenological stages [9], [10], lodging [11], leaf area index (LAI), or biomass time courses, yields [12]–[15]).

The studies which combine satellite images and agrometeorological models mainly take into account data acquired in the optical domain, and their performance in simulating the above ground parameters is closely linked to the number of satellite acquisition and their location in time during the crop growth cycle [16], [17]. A not enough well-sampled times series of satellite images prevents from accurately monitoring phenological stages characterized by a high dynamic of crop parameters, whose duration depends on the considered crop and climate conditions [18], [19]. In the case of sunflower, the short crop growth cycle (ranging from 120 to 150 days on temperate climate [20]) reinforces the constraint regarding the acquisition's repetitiveness, especially when using optical data which are limited to cloud free period. Recently, the launch of satellite missions in the microwave domain (e.g., TerraSAR-X, Radarsat-2, Sentinel-1, or Alos-2) offered possibilities of using multisensor's observations, without cloud cover limitation. Nevertheless, only few studies have addressed the possibilities of combining radar data and crop modeling, since the knowledge of temporal signatures of the radar signal is still rare worldwide [21]–[26]. It is, therefore, difficult to integrate confidently synthetic aperture radar (SAR) signals into a crop growth model, without adding noise in the resulting simulations.

Over sunflower, few studies addressed the temporal evolution and/or the sensitivity of backscattering coefficients to crop parameters [27]–[30], but results shown that the performances in LAI retrieval associated to multiconfiguration backscattering coefficients appeared satisfying ($0.60 < R^2 < 0.89$, and relative root-mean-square error (RMSE) < 20%) in view to their assimilation into an agrometeorological model.

In the continuity of the work carried out by [30], the objective of this study aims at estimating the seed yield (named yield in the following) of sunflower at a field scale by controlling an agrometeorological model, named simple algorithm for yield estimates coupled to a water balance model (SAFY-WB) [15],

Manuscript received September 14, 2016; revised February 3, 2017, April 26, 2017, and July 19, 2017; accepted August 4, 2017. Date of publication September 7, 2017; date of current version December 19, 2017. The support, funding, and satellite images (proposal HYD0611 and SOAR-EU and Categorie-1 ESA Project 6843) of this work were provided in part by the European Space Agency, in part by the German Space Agency, in part by the Canadian Space Agency, in part by the SOAR Project, and in part by the Centre National des Etudes Spatiales. (*Corresponding author: Remy Fieuzal.*)

The authors are with the CNES/CNRS/IRD/UPS, Centre d'Études de la BIOsphère, Université de Toulouse, Toulouse 31401, France (e-mail: remy.fieuzal@cesbio.cnes.fr; claire.marais-sicre@cesbio.cnes.fr; frederic.baup@cesbio.cnes.fr).

Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2017.2737656
<figure><img src="image_2.png" /><figcaption>Fig. 1. Location of the study site (five working farms) in southwestern France. The monitored field of sunflower is highlighted, together with the fields where the farmer's agricultural practices were available, and displayed with the meteorological station, on a color-composed image based on three TerraSAR-X images.</figcaption></figure>

with the LAI derived from multisource satellite data acquired at high spatial resolution (optical and multiconfiguration SAR sensors) and the measured dry mass (DM) of the crop. This model simulates the temporal evolutions of vegetation, soil, and atmosphere variables at a daily time step between the day of emergence and harvest. It is based on a simple formalism, and has been validated for different crop types (wheat, corn, soybean, and sunflower) and for contrasted climatic conditions (semiarid and temperate) [13], [15], [26].

This paper is structured as follows: the study site, the ground measurements, and the satellite data are first presented in the Section II. The methodology focuses first on the description of the agrometeorological model before presenting the calibration strategies (see Section III). The results are analyzed and discussed considering three sections. The calibration performances of the model are first presented, followed by the validation of the retrieved sunflower yield over five working farms. Finally, a map of yield is produced and discussed at field scale over 23 independent fields (see Section IV). At last, conclusion and prospects are given in Section V.

## II. MATERIALS

### A. Study Site

The study site is located in an agricultural region mainly dedicated to the cultivation of seasonal crops, as evidence of the 57% of the land occupied by crops [7] (see Fig. 1). The temperate climate allows the cultivation of the main crops encountered in France and more generally worldwide. They can be split into “winters crops” (mainly represented by wheat and rapeseed), and “summers crops” (mainly represented by corn, sunflower, and soybean). The fields of sunflower have very different shapes and sizes, with areas ranging from 0.5 to 18.8 ha (6.1 ha on average in the study area), justifying the use of high spatial resolution data. The fields are almost flat, with slopes never exceeding 1°. During the agricultural season of sunflower,

<figure><img src="image_3.png" /><figcaption>Fig. 2. Chronogram of satellite acquisitions performed in the microwave (TerraSAR-X (TS-X) with SL and SM beam modes, Radarsat-2 (RS-C)), and optical (FS-2, Spot-4/5) domains during the sunflower crop cycle, together with the incidence angle and the orbit (ascending in black, descending in white). The timeline of ground measurements (qualitative measurements (QM) and biomass measurements (BM)).</figcaption></figure>

observations and measurements were regularly performed over a dense network of fields, synchronously to multisource satellite acquisitions [31].

### B. Ground Measurements

Ground data were measured over five working farms, for a total of 23 fields of sunflower, representing an amount of 140 ha. Regular qualitative measurements were performed at each satellite overpass, in order to collect information on the dates of sowing and harvesting, and some phenological stages (e.g., emergence, flowering, or start of senescence). During the agricultural campaign of 2010, the seeds were sowed from 9th of April to 29th of May, and harvested between 27th of August and 20th of October. Two periods of sowing were observed over the studied region, defining “early” and “late” sunflower, respectively, sowed before 13th of April and after 26th of May. In complement, the yields of sunflower were collected for the five different farmers, with values ranging from 1.6 to 3.2 t·ha⁻¹ (mean of 2.4 t·ha⁻¹) at an humidity rate of 15%. These yields, which were in accordance with the interprofessional technical center for metropolitan oilseeds (which ranged from 2.2 to 2.6 t·ha⁻¹ [32]), are finally used to map the sunflower yield at the field scale.

Quantitative measurements of the biomass have been regularly performed over one monitored field of 6.6 ha (see Fig. 1). During the main phenological stages (see Fig. 2), five samples of the vegetation were randomly collected in an elementary sampling unit, corresponding to homogenous areas of 20 by 20 m². Each sample was weighed in order to obtain the fresh mass. The different samples were dried in an oven (65 °C) at least 48 h and then weighed again to know the DM. During the sunflower agricultural season, the DM ranged from 30 g·m⁻² after the crop emergence to a maximum of 1070 g·m⁻².

Climatic data were collected at half-an-hour intervals by a meteorological station nearby the fields of sunflower (see Fig. 1).
The distance between those fields and meteorological station was inferior to 6 km. In this condition, it was assumed that the meteorological station was representative of the climatic conditions over the whole studied area. The half-hourly measurements were daily averaged or accumulated (wind speed, evapotranspiration, air temperature, relative humidity, atmospheric pressure, global solar radiation, and precipitation) to derive the meteorological inputs of the model. During the agricultural season, the air temperature, the global solar radiation, and the potential evapotranspiration range from 4.8 to 26.2 °C, 1.3 to 31.0 MJ·m⁻², and 0.7 to 6.7 mm, respectively, while the amount of precipitation reaches 370 mm.

### C. Satellite Acquisitions

1. **Microwave data:** TerraSAR-X (TS-X) and Radarsat-2 (RS-C) have provided the SAR data in the X-band ($f = 9.65$ GHz, $\lambda = 3.1$ cm) and the C-band ($f = 5.405$ GHz, $\lambda = 5.5$ cm), respectively, every 13 days on average (see Fig. 2). In the X-band, the images were acquired with HH polarization, at incidence angles ranging from 27.3° to 53.3°, using two beam modes (StripMap (SM) and SpotLight (SL)), which were characterized by pixel spacing of approximately 3 and 1.5 m, respectively [33]. In the C-band, all the images were acquired with the full quad-polarization mode (fine quad-pol), which delivers images at HH, VV, HV, and VH polarizations [34]. The incidence angles ranged from 24° to 41°, with pixel spacing equal to 5 m. SAR images were acquired with ascending or descending orbit, at 17:45 or 06:00 GMT, respectively (black and white dots in Fig. 2). All of the radar images underwent radiometric calibration and were georeferenced using orthophotos (with a resolution of 50 cm) provided by the French National Geographic Institute ([35], [33]). More details relating to the processing and to the noise associated to SAR images can be found in [30].

2. **Optical data:** Formosat-2 (FS-2) and Spot-4/5 data were provided using the multispectral mode (see Fig. 2). All of the FS-2 images were acquired in blue, green, red, and near-infrared wavelengths (from 0.45 to 0.90 µm), at the same viewing angle (±45°), and with a spatial resolution of 8 m [36]. The Spot-4 and Spot-5 provided images in green, red, and near- and medium-infrared wavelengths (from 0.50 to 1.75 µm), with two different incidence angles (75 and 102°), and a spatial resolutions of, respectively, 20 and 10 m [37]. All of the images were orthorectified using CNES orthorectification tools. The cloud detection and atmospheric correction were applied on FS-2 and SPOT images [38]. Spectral bands in green, red, and near infrared were combined to calculate the normalized difference vegetation index (NDVI [39], (1)), and the modified triangular vegetation index (MTVI2 [40], (2)). Those vegetation indices were then linearly daily interpolated to match with microwave images, with the assumption that changes in crop growth are gradual during short periods (the mean interval between two optical

<figure><img src="image_4.png" /><figcaption>Fig. 3. Synopsis of the main steps involved in the combination of optical or microwave images in the agrometeorological model for yield estimates purposes.</figcaption></figure>

acquisitions is close to eight days)

$$NDVI = (\rho_{\text{nir}} - \rho_{r}) / (\rho_{\text{nir}} + \rho_{r}) \qquad (1)$$

$$MTVI2 = \frac{1.5[1.2(\rho_{\text{nir}} - \rho_{g}) - 2.5(\rho_{r} - \rho_{g})]}{\sqrt{(2\rho_{\text{nir}} + 1)^2 - (6\rho_{\text{nir}} - 5\sqrt{\rho_{r}}) - 0.5}} \qquad (2)$$

where $\rho_g$, $\rho_r$, and $\rho_{\text{nir}}$ correspond to the reflectance acquired in the green, red, and near-infrared wavelengths, respectively.

### III. METHODOLOGY

As illustrated in Fig. 3, the proposed methodology aimed at assimilating ground and optical or microwave satellite data into an agrometeorological model in order to estimate sunflower yield at two different fine scales (working farm and field scales).
<table><caption>TABLE I
VALUES OF EMPIRICAL PARAMETERS USED TO DERIVE LAI FROM SATELLITE MULTISENSOR DATA</caption><thead><tr><th>Satellite</th><th>Index/Signal</th><th>a</th><th>b</th><th>c</th></tr></thead><tbody><tr><td>FS-2 Spot-4/5</td><td>MTVI2</td><td>-6.247</td><td>0.946</td><td>0.643</td></tr><tr><td>TerraSAR-X</td><td>σ<sup>0</sup><sub>X-HH</sub></td><td>-6.919</td><td>1.448</td><td>-6.821</td></tr><tr><td>Radarsat-2</td><td>σ<sup>0</sup><sub>C-HH</sub></td><td>-10.988</td><td>0.922</td><td>-4.881</td></tr><tr><td>Radarsat-2</td><td>σ<sup>0</sup><sub>C-VV</sub></td><td>-10.191</td><td>0.641</td><td>-4.162</td></tr><tr><td>Radarsat-2</td><td>σ<sup>0</sup><sub>C-VH</sub></td><td>-9.740</td><td>0.691</td><td>-13.892</td></tr></tbody></table>

The LAI estimates, based on reflectances or backscattering coefficients, are first presented. Then, the satellite-controlled model is evaluated through two independent calibration and validation steps, by comparing simulated and measured bio-physical parameters (i.e., LAI, DM, and yield). Finally, one map of sunflower yield is generated for the 23 studied fields.

### A. From Satellite to LAI Estimates

Georeferenced backscattering coefficients and reflectances were averaged over each sunflower fields with respect to their shapes, after radiometric calibration (for microwave data) and/or atmospheric disturbances correction (for optical data). Radar reflectances were first angular normalized, during the whole vegetation cycle, using NDVI values in order to remove the effect of the incidence angle on the radar signal as developed in [10], [22], and [30]. Then, optical and radar reflectances were, respectively, used to derive independent LAI values from optical (see (3)) or radar (see (4)) satellite data, using nonlinear empirical relationships and the empirical parameters presented in Table I, as proposed by [41], [30] for study site located in Canada and France, allocated to the cultivation of sunflower

$$ \text{LAI} = a \times \ln(b - c \times \text{MTVI2}) \quad (3) $$

$$ \text{LAI} = (-1/b) \times \ln((\sigma^{\circ} - c)/a) \quad (4) $$

where *a*, *b*, and *c* represent empirical coefficients defined in Table I for the five satellite configurations.

### B. Overview of the Agrometeorological Model

The agrometeorological model named SAFY-WB combines a crop growth (simple algorithm for yield estimates, initially developed for the monitoring of wheat cropped on semiarid climate) and a water balance (FAO-56) model. It simulates the temporal evolutions of vegetation, soil, and atmosphere variables, namely: the LAI, the dry biomass, the soil moisture content at different depths, the evaporation, and the transpiration, between the day of emergence and the total crop senescence. It operates at a daily time step, using climate input variables: the global solar radiation, the air temperature, the precipitation, and the potential evapotranspiration.

The daily production of DM is a function of the absorbed photosynthetically-active radiation balanced by the effective light use efficiency (ELUE) (relationship adapted from [42]).

<table><caption>TABLE II
PARAMETERS TARGETED (CROP- AND FIELD-SPECIFIC PARAMETERS) DURING THE CALIBRATION STEP OF THE MODEL</caption><thead><tr><th></th><th>Name</th><th>Abbreviation</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="3">Crop-specific parameters</td><td>Partition to the leaves: Parameter A</td><td>P1<sub>A</sub></td><td>–</td></tr><tr><td>Partition to the leaves: Parameter B</td><td>P1<sub>B</sub></td><td>–</td></tr><tr><td>Rate of senescence</td><td>R<sub>S</sub></td><td>°C day</td></tr><tr><td rowspan="3">Field-specific parameters</td><td>Cumulative temperature inducing senescence</td><td>S<sub>TT</sub></td><td>°C</td></tr><tr><td>Day of crop emergence</td><td>D<sub>0</sub></td><td>DoY</td></tr><tr><td>Effective lighth use efficiency</td><td>ELUE</td><td>g·MJ<sup>-1</sup></td></tr></tbody></table>

Leaf production and leaf senescence are controlled by a growing degree-day approach. During the leaf growing period, a fraction of the daily DM production is allocated to the leaf production following the empirical parametrization proposed by [43]. The senescence occurs at a prescribed rate, when the air temperature accumulated from the day of emergence reaches the threshold of temperature inducing senescence (with base temperature of 4 °C, [44]). A detailed description of the SAFY-WB model can be found in [15].

### C. Calibration of the Model

The calibration step is performed over one monitored field, reproducing the device of agricultural decision makers who base their recommendations on data collected on a field considered as representative of an agricultural region. This steps aimed at determining six parameters of the model, defined as target parameters (noted TP in Fig. 3), for each satellite configuration (as their values depend on the considered LAI). Four of them describe development stages of the crop (abbreviated P1<sub>A</sub>, P1<sub>B</sub>, S<sub>TT</sub>, and R<sub>S</sub>, Table II). The partition to the leaves parameters (P1<sub>A</sub> and P1<sub>B</sub>) describe the allocation of biomass to the leaves [45] during the growth phase, based on empirical function of air temperature adapted from [43]. The cumulative temperature which induces senescence and the rate of senescence (S<sub>TT</sub> and R<sub>S</sub>) describe last phenological stages. The two other parameters are related to agricultural practices, namely the day of emergence D<sub>0</sub> and the ELUE.

The control of the model was based on the minimization of the difference (RMSE) between the simulated and satellite derived or ground observed crop parameters (i.e., LAI and DM in the following cost functions C<sub>LAISAT</sub>, C<sub>DM</sub>, and Č, (5) to (7)). The sought solution corresponds to the global minimum of the cost function, reaches by using the simplex method (with random determination of initial values of the parameters to avoid local minima).

The two first calibration cases consist in controlling the model by the LAI derived from optical images (LAI<sub>MTVI2</sub>), in order to compare results with previous studies [13], [15], [45], or by the LAI derived from SAR images (LAI<sub>σ°X-HH</sub>, LAI<sub>σ°C-HH</sub>, LAI<sub>σ°C-VV</sub>, and LAI<sub>σ°C-VH</sub>), in order to analyze the performances of microwave sensors for crop monitoring (see (5)). The third calibration case consists in controlling the model by
the DM collected over one monitored field (DM<sub>OBS</sub>) and the LAI derived from optical or radar images (see (5) to (7)). This last case takes full advantage of satellite survey capabilities and knowledge of agricultural decision makers (e.g., farmers or regional agricultural chamber), and is very original compared to others studies only focuses on remote-sensing data [13], [24], [26], [45]:

$$C_{LAI_{SAT}} = \sqrt{N_{LAI_{SAT}}^{-1} \sum_{150 \le t_i \le 250} [LAI_{SIM}(t_i) - LAI_{SAT}(t_i)]^2} \quad (5)$$

$$C_{DM} = \sqrt{N_{DM_{OBS}}^{-1} \sum_{150 \le t_i \le 250} [DM_{SIM}(t_i) - DM_{OBS}(t_i)]^2} \quad (6)$$

$$\bar{C} = \text{sum} \left[ \frac{LAI_{SAT}^{-1} \times C_{LAI_{SAT}}}{DM_{OBS}^{-1} \times C_{DM}} \right] \quad (7)$$

where LAI<sub>SIM</sub> or DM<sub>SIM</sub> correspond to values simulated by the model at the time (t<sub>i</sub>), LAI<sub>SAT</sub>, or DM<sub>OBS</sub> to values derived from satellite data or ground measurements, and N to the number of data collected between the day 150 and 250.

### D. Application of the Model to Several Fields

As proposed in [13], [15], and [45], once the value of the “crop-specific parameters” has been estimated (i.e., Pl<sub>A</sub>, Pl<sub>B</sub>, and R<sub>S</sub>), the “field-specific parameters” are optimized in order to limit the problem of equifinality (pointed out by [45], as a result of the sensitivity analysis of the model), during the application of the model to several fields. During that step, D<sub>0</sub>, ELUE, and S<sub>TT</sub> are calibrated on the LAI derived from satellite images, in order to account for the heterogeneities of the agricultural practices. Indeed, two periods of sowing were observed over the studied region, with early and late sunflowers, respectively, sowed before 13th of April (day 103) and after 26th of May (day 146). The three remaining “crop-specific parameters” (i.e., Pl<sub>A</sub>, Pl<sub>B</sub>, and R<sub>S</sub>) were considered as stable in time and space for each satellite configuration, and come from the calibration step.

### E. Yield Mapping

As the formalism of the model does not provide sunflower yield estimates, the yield map is finally produced using a linear relationship estimated between simulated DM and observed yield, using the DM values estimated just before the harvest by the two best satellite signals (MTVI2 and $\sigma^0_{C-VV}$).

## IV. RESULTS AND DISCUSSION

### A. Calibration of the Model

Temporal trends of the LAI and the DM are globally well reproduced whatever the considered strategies, as evidence by the values of the determination coefficient which range from 0.84 to 0.99 (see Table III, Fig. 4). Nevertheless, the errors on DM

<table><caption>TABLE III
STATISTICAL PERFORMANCES (DETERMINATION COEFFICIENTS TOGETHER WITH ABSOLUTE AND RELATIVE RMSE) ASSOCIATED TO THE COMPARISON BETWEEN SIMULATED AND MEASURED LAI AND DM, DURING THE CALIBRATION STEP</caption><thead><tr><th rowspan="2"></th><th colspan="4">LAI</th><th colspan="4">DM</th></tr><tr><th>n</th><th>R<sup>2</sup></th><th>RMSE [m<sup>2</sup>·m<sup>-2</sup>]</th><th>rRMSE [%]</th><th>n</th><th>R<sup>2</sup></th><th>RMSE [g·m<sup>-2</sup>]</th><th>rRMSE [%]</th></tr></thead><tbody><tr><td colspan="9" style="text-align: center;">LAI-controlled</td></tr><tr><td>MTVI2</td><td>8</td><td>0.99</td><td>0.09</td><td>5</td><td>6</td><td>0.94</td><td>225</td><td>45</td></tr><tr><td>σ<sup>0</sup><sub>X-HH</sub></td><td>8</td><td>0.96</td><td>0.18</td><td>11</td><td>6</td><td>0.92</td><td>217</td><td>43</td></tr><tr><td>σ<sup>0</sup><sub>C-HH</sub></td><td>8</td><td>0.89</td><td>0.35</td><td>17</td><td>6</td><td>0.93</td><td>152</td><td>30</td></tr><tr><td>σ<sup>0</sup><sub>C-VV</sub></td><td>8</td><td>0.92</td><td>0.33</td><td>17</td><td>6</td><td>0.93</td><td>150</td><td>30</td></tr><tr><td>σ<sup>0</sup><sub>C-VH</sub></td><td>8</td><td>0.96</td><td>0.18</td><td>11</td><td>6</td><td>0.93</td><td>137</td><td>27</td></tr><tr><td colspan="9" style="text-align: center;">LAI and DM-controlled</td></tr><tr><td>MTVI2</td><td>8</td><td>0.98</td><td>0.16</td><td>9</td><td>6</td><td>0.94</td><td>148</td><td>30</td></tr><tr><td>σ<sup>0</sup><sub>X-HH</sub></td><td>8</td><td>0.91</td><td>0.10</td><td>5</td><td>6</td><td>0.92</td><td>139</td><td>28</td></tr><tr><td>σ<sup>0</sup><sub>C-HH</sub></td><td>8</td><td>0.84</td><td>0.40</td><td>20</td><td>6</td><td>0.94</td><td>123</td><td>25</td></tr><tr><td>σ<sup>0</sup><sub>C-VV</sub></td><td>8</td><td>0.85</td><td>0.44</td><td>23</td><td>6</td><td>0.95</td><td>127</td><td>25</td></tr><tr><td>σ<sup>0</sup><sub>C-VH</sub></td><td>8</td><td>0.92</td><td>0.45</td><td>30</td><td>6</td><td>0.92</td><td>117</td><td>23</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Time courses of the LAI (on the left) and the DM (on the right) simulated by the agrometeorological model. The simulations are represented by black or red lines when the model is controlled by LAI estimated from LAI<sub>MTVI2</sub> or LAI<sub>σ<sup>0</sup><sub>C-VV</sub></sub> (a)–(d). The observed dates of sowing (S) and harvest (H) are indicated by vertical dotted black lines. Ground measurements of DM are displayed by green crosses. Black and red crosses represent the LAI derived from MTVI2 and $\sigma^0_{C-VV}$, respectively.</figcaption></figure>

estimates are greater than 40% when the model is only controlled by the LAI derived from the MTVI2 or $\sigma^0_{X-HH}$, confirmed by an important underestimates of values from the middle to the end of the crop cycle (see Fig. 4(b)). At the opposite, the LAI and the DM time courses are well reproduced when the model is controlled by the LAI derived from C-band images, or by both LAI and DM whatever the considered satellite sensor used to derive the LAI values (see Fig. 4(c) and (d)). Despite the estimation of LAI and DM based on the use of $\sigma^0_{C-VH}$ is statistically good, it is important to point out that this SAR signal cannot be used to simulate accurately the yield, since the senescence period is not identifiable, and, thus, the harvest time [30].
<figure><img src="image_6.png" /><figcaption>Fig. 5. Distribution of RMSE on the DM (on the right) and dry biomass values simulated just before harvest (on the left) according to an error of 20% on crop variables targeted during the calibration step (LAI<sub>MTVI2</sub> (a), (b), LAI<sub>σ°C-VV</sub> (c), (d), LAI<sub>MTVI2</sub> and DM (e), (f), and LAI<sub>σ°C-VV</sub> and DM (g), (h)).</figcaption></figure>

The accurate results presented in Fig. 4 (relative errors on the LAI ranging from 5% to 30% and from 23% to 30% for the DM) are consistent with *in-situ* measurements uncertainties (e.g., hemispherical photography or destructive measurements) and with the variability observed inside a field [13], [46].

The impact of uncertainties associated to crop variables targeted during the calibration step on dry biomass simulation was evaluated through the histograms of RMSE on the DM and dry biomass values estimated just before the harvest (see Fig. 5). To this end, 500 profiles of LAI and DM values have been generated considering a random error of 20% (value consistent with previous studies [13], [30] and ground measurements) around the satellite derived LAI or DM measurements, for each acquisition date and for the four cases previously considered in Fig. 4.

The results show that the error on DM is significantly reduced when the model is both controlled by LAI and DM data. The mean values of RMSE decrease from 232 to 146 g·m⁻² for optical (see Fig. 5(a) and (e)), and from 169 to 137 g·m⁻² for σ°C-VV (see Fig. 5(c) and (g)), respectively, with and without DM.

Moreover, the dispersion on DM values is important when the model is only controlled by LAI data (standard deviation on DM

<table><caption>TABLE IV
VALUES OF THE TP ESTIMATED DURING THE CALIBRATION STEP, DEPENDING ON THE SATELLITE DATA USED TO CONTROL THE AGROMETEOROLOGICAL MODEL</caption><thead><tr><th></th><th>PI<sub>A</sub></th><th>PI<sub>B</sub></th><th>S<sub>TT</sub></th><th>R<sub>S</sub></th><th>D<sub>0</sub></th><th>ELUE</th></tr></thead><tbody><tr><td colspan="7">LAI-controlled</td></tr><tr><td>MTVI2</td><td>0.0932</td><td>0.0028</td><td>1281</td><td>5780</td><td>127</td><td>1.79</td></tr><tr><td>σ<sup>0</sup><sub>X-HH</sub></td><td>0.0055</td><td>0.0105</td><td>1410</td><td>3101</td><td>142</td><td>2.23</td></tr><tr><td>σ<sup>0</sup><sub>C-HH</sub></td><td>0.0749</td><td>0.0041</td><td>1240</td><td>6515</td><td>139</td><td>2.45</td></tr><tr><td>σ<sup>°</sup><sub>C-VV</sub></td><td>0.0589</td><td>0.0045</td><td>1313</td><td>1750</td><td>140</td><td>2.45</td></tr><tr><td>σ<sup>°</sup><sub>C-VH</sub></td><td>0.0146</td><td>0.0095</td><td>1043</td><td>20112</td><td>146</td><td>2.88</td></tr><tr><td colspan="7">LAI and DM-controlled</td></tr><tr><td>MTVI2</td><td>0.1943</td><td>0.0020</td><td>1294</td><td>3212</td><td>135</td><td>2.36</td></tr><tr><td>σ<sup>0</sup><sub>X-HH</sub></td><td>0.0625</td><td>0.006</td><td>1451</td><td>1499</td><td>144</td><td>3.09</td></tr><tr><td>σ<sup>0</sup><sub>C-HH</sub></td><td>0.1350</td><td>0.0028</td><td>1193</td><td>6155</td><td>142</td><td>2.69</td></tr><tr><td>σ<sup>°</sup><sub>C-VV</sub></td><td>0.1289</td><td>0.0026</td><td>1116</td><td>6367</td><td>140</td><td>2.42</td></tr><tr><td>σ<sup>°</sup><sub>C-VH</sub></td><td>0.0186</td><td>0.0109</td><td>1489</td><td>0</td><td>152</td><td>3.42</td></tr></tbody></table>

of 223 and 194 g·m⁻², for optical and σ°C-VV, respectively (see Fig. 5(b) and (d)). At the opposite, the dispersion is lower when the model is also controlled by dry biomass measurements (see Fig. 5(f) and (h)). A standard deviation inferior to 139 g·m⁻² is, thus, observed, which represents a maximum of 13% of uncertainty on DM, highlighting the interest of using *in-situ* data collected by agricultural decision makers.

The values of the TP are presented in Table IV for all the considered model-controlled configurations (i.e., LAI or LAI and DM).

An earlier emergence (day 127 compared to 135) is obtained when the model is only LAI<sub>MTVI2</sub> controlled (see Fig. 4(b)), which explains the overestimate of DM during the first phenological stages (between days 150 and 175), while the underestimate of DM during last phenological stages is related to a lower ELUE (1.79 g·MJ⁻¹ compared to 2.36 g·MJ⁻¹). The higher values of ELUE when the model is controlled by the LAI derived from C-band images (compared to that associated to LAI<sub>MTVI2</sub>) are explained by the higher sensitivity of LAI<sub>σ°C-HH</sub>, LAI<sub>σ°C-VV</sub>, or LAI<sub>σ°C-VH</sub> during first phenological stages (from days 150 to 184, Fig. 4(a) and (c)) which constrains the model to produce more biomass during this period by increasing the ELUE value.

The differences in TP values when the model is controlled by both the LAI and the DM are first explained by the maximal values of LAI. Indeed, the calibration on the LAI derived from either σ°C-VH or σ°X-HH, which are characterized by maximal values slightly superior to 2 m²·m⁻² (which is low compared to measurements [13]), induces higher ELUE (superior to 3 g·MJ⁻¹, Table IV) to reach the measured DM values. Moreover, the low-signal sensitivity of σ°C-VH during the senescence period leads to convergence problems as evidence by the unrealistic high or low values of the R<sub>S</sub>. The three other tested configurations (LAI<sub>MTVI2</sub>, LAI<sub>σ°C-HH</sub>, or LAI<sub>σ°C-VV</sub>) are associated with upper maximum of LAI (close to 3 m²·m⁻²) and values of ELUE ranged from 2.36 to 2.69 g·MJ⁻¹ which are consistent with previous studies [47], [48]. In these cases, the main difference concerns the rate of senescence (R<sub>S</sub>),
<figure><img src="image_7.png" /><figcaption>Fig. 6. Time courses of the simulated LAI (on the left) and the DM (on the right) over the 23 fields of sunflower, when the model is controlled by LAI<sub>MTVI2</sub> and LAI<sub>°C-VV</sub>. The fields are distinguished in black and blue corresponding to early or late dates of sowing.</figcaption></figure>

<table><caption>TABLE V
MEDIAN AND STANDARD DEVIATION VALUES OF THE THREE OPTIMIZED PARAMETERS (D₀, ELUE, AND STT) OBTAINED BY APPLYING THE MODEL OVER THE FIVE WORKING FARMS CONSIDERING TWO CLASSES OF SUNFLOWER: EARLY- AND LATE-SOWED FIELDS</caption><thead><tr><th></th><th>D<sub>0</sub></th><th>ELUE</th><th>S<sub>TT</sub></th></tr></thead><tbody><tr><td colspan="4">LAI-controlled</td></tr><tr><td colspan="4">LAI<sub>MTVI2</sub></td></tr><tr><td>Early-Sowed</td><td>109 ± 12</td><td>1.89 ± 0.19</td><td>1354 ± 150</td></tr><tr><td>Late-Sowed</td><td>148 ± 9</td><td>2.04 ± 0.25</td><td>1181 ± 162</td></tr><tr><td colspan="4">LAI<sub>°C-VV</sub></td></tr><tr><td>Early-Sowed</td><td>126 ± 18</td><td>2.20 ± 0.26</td><td>1346 ± 126</td></tr><tr><td>Late-Sowed</td><td>155 ± 10</td><td>2.49 ± 0.22</td><td>1245 ± 77</td></tr><tr><td colspan="4">LAI and DM-controlled</td></tr><tr><td colspan="4">LAI<sub>MTVI2</sub></td></tr><tr><td>Early-Sowed</td><td>113 ± 11</td><td>2.40 ± 0.23</td><td>1402 ± 145</td></tr><tr><td>Late-Sowed</td><td>148 ± 6</td><td>2.56 ± 0.26</td><td>1246 ± 120</td></tr><tr><td colspan="4">LAI<sub>°C-VV</sub></td></tr><tr><td>Early-Sowed</td><td>116 ± 11</td><td>2.15 ± 0.21</td><td>1248 ± 136</td></tr><tr><td>Late-Sowed</td><td>152 ± 6</td><td>2.41 ± 0.13</td><td>1142 ± 113</td></tr></tbody></table>

explained by the different sensitivity of satellite signals to vegetation drying: change of color for optical and decrease of water content for radar signals. These events are characterized by specific dynamics, leading to higher rate of senescence when the model is controlled by copolarized signals acquired in the C-band (LAI<sub>°C-HH</sub> or LAI<sub>°C-VV</sub>) since the decrease of the radar signal is less marked than the decrease of the optical index [30]. Consequently, the dates of simulated harvest are different for the three configurations. Indeed, the difference between the simulated and the observed harvest days is earlier using the LAI<sub>MTVI2</sub> (four days), while the simulations based on LAI<sub>°C-VV</sub> or LAI<sub>°C-HH</sub> are associated with late harvest (with, respectively, four and nine days of discrepancy).

For the sake of conciseness, the following section only focuses on the most significant satellite data regarding the values of ELUE, the maximal values of LAI, and the behavior of signal during the senescence: LAI derived from σ<sub>°C-VV</sub> and from MTVI2.

## B. Validation of the Model at the Working Farms Scale

1) Contribution of the different satellite configurations on model simulations: Fig. 6 displays the time courses of the LAI and the DM simulated by the agrometeorological model over the five working farms, considering two contrasted classes of sunflower: early- and late-sowed fields (in black and blue, respectively). Whatever the data used to control the model, the simulations are quite similar, excepted when model is controlled by LAI<sub>MTVI2</sub> (due to low ELUE value as explained above), as stated by the mean values of the total biomass estimates of 835 g·m⁻² (against more than 900 g·m⁻² in other cases).

The values of the day of emergence (D₀ in the Table V) are consistent with the observed sowing dates (i.e., early-sowed fields show previous D₀ compared to the late-sowed ones), and the time intervals between early and late D₀ are close (ranging from 25 to 39 days) regardless the satellite data used to control the model. Moreover, the difference between the date of sowing (measured) and emergence (simulated) are consistent with the agronomic knowledge. Higher temporal intervals are observed between the sowing and the emergence for the early-sowed fields compared to the late-sowed fields. This result is explained by the different rates of crop growth and closely linked to the higher temperatures observed at the end of May (daily mean temperature of 18.5 against 12.4 °C during the month of April for the early-sowed fields).

Higher ELUE in Table V values are observed (difference superior to 0.50 g·MJ⁻¹ for simulations based on the MTVI2) when the TP are calibrated on both LAI and DM. On the other hand, the values of ELUE are quite similar when the model is controlled by LAI derived from C-band images, with or without DM. This phenomenon is due to the high ELUE value estimated when using only C-band copolarized SAR data (as explained previously). Moreover, a slight difference is observed between early- and late-sowed sunflower fields for the values of ELUE, regardless the considered satellite configurations. Late-sowed fields present higher ELUE (with a maximal difference of 0.3
<figure><img src="image_8.png" /><figcaption>Fig. 7. Comparison between the simulated dry mass (SDM) and the observed yields (CY) over the five working farms. The linear relationships estimated between the simulated DM and the observed yield are highlighted in black and green (when the model is controlled by LAI<sub>MTVI2</sub> or LAI<sub>σ°C-VV</sub> combined with DM measurements), or red for the mean trend.</figcaption></figure>

g·MJ⁻¹) which appears consistent with agronomic studies [49], [50]. In these cases, the effect of the sowing date on the sunflower growth is observed through various consequences (e.g., lengths of the phenological stages, crop growth rates, or grain yields) depending on the climate of the considered study area. The analysis of the values of the cumulative temperature (S<sub>TT</sub> in the Table V) illustrated the consistency between the simulation and the phenological cycle of the crop. Indeed, S<sub>TT</sub> values are lower for the late-sowed fields, compared to the early-sowed, inducing a difference in the lengths of the crop growth of at least 14 days.

2) Crop yield retrieval at working farm scale: The estimates of crop yield (CY) based on the LAI derived from the MTVI2 and DM show high performances with coefficient of determination equal to 0.85 (see Fig. 7, (8)), which is consistent with previous study based on only optical data (where the LAI was derived from the NDVI [13]). Nevertheless, best results are obtained when the model is controlled by LAI<sub>σ°C-VV</sub> and DM (R² of 0.89 and rRMSE = 7%, (9)) (coefficient of determination is equal to 0.86 when the model is only controlled by LAI<sub>σ°C-VV</sub>). The map of sunflower yields is processed using the mean yield of simulations controlled by optical (“LAI<sub>MTVI2</sub> and DM”) and microwave (“LAI<sub>σ°C-VV</sub> and DM”) data to increase the robustness of the method (see (10)), as discussed in the following section:

$$ 
\begin{gathered}
\text{CY}_{\text{LAIMTVI2\&DM}} = 0.39 \times \text{SDM}_{\text{LAIMTVI2\&DM}} - 1.63 \\
(R^2 = 0.85, r\text{RMSE} = 9\%) \qquad (8)
\end{gathered}
$$

$$ 
\begin{gathered}
\text{CY}_{\text{LAI}\sigma^\circ\text{C-VV}\&\text{DM}} = \\
0.44 \times \text{SDM}_{\text{LAI}\sigma^\circ\text{C-VV}\&\text{DM}} - 1.62 \\
(R^2 = 0.89, r\text{RMSE} = 7\%) \qquad (9)
\end{gathered}
$$

$$ 
\begin{gathered}
\text{CY}_{\text{MEAN}} = 0.41 \times \text{SDM}_{\text{MEAN}} - 1.62 \\
(R^2 = 0.75, r\text{RMSE} = 15\%) \qquad (10)
\end{gathered}
$$

where CY and SDM represent crop yield and the simulated DM, respectively, and R² and rRMSE represent the coefficient of determination and the relative RMSE of the relationships.

3) Mapping of the sunflower yield at field scale: The yields estimated over the 23 fields from the two independent approaches (when the model is LAI<sub>MTVI2</sub> or LAI<sub>σ°C-VV</sub> controlled) are close on average (median values are equal to 2.56 and 2.40 t·ha⁻¹, respectively) and in variability (the values ranged from 1.18 to 3.53 t·ha⁻¹ and from 1.32 to 4.06 t·ha⁻¹, respectively). In these two cases, results demonstrate the ability of the approach to reproduce the important spatial variability of yields observed during one year (values ranging from 1.6 to 3.2 t·ha⁻¹), with an average difference of 0.37 t·ha⁻¹ (and a standard deviation of difference of 0.26 t·ha⁻¹). Moreover, 70% of the simulated yields have a difference inferior to 20% (representing a mean relative difference of 11% for 16 of the 23 sunflower fields bounded by the dashed lines in Fig. 8(a)), which is considered acceptable for crop yield monitoring purpose. The similarity of the results provides an interesting potential to estimate yield of sunflower either with optical data (as demonstrated in previous studies) or radar signal (which is not affected by cloud cover). Nevertheless, seven sunflower fields show an absolute difference superior to 20%. Three and four yields are overestimated when the model is controlled by the LAI<sub>MTVI2</sub> and by the LAI<sub>σ°C-VV</sub> (red and light green circles in Fig. 8(a), respectively). The higher development of the sunflower leaves (compared to other organs of the crop) explains the three first cases. Indeed, a strong increase of the LAI leads to overestimate the grain yield because the model always simulates the same repartition of biomass between leaves and other organs, whatever the considered field (the approach is, thus, limited to constant ratio between leaves and grain masses).

Four fields are characterized by a difference superior to 20%, when estimated yields from LAI<sub>σ°C-VV</sub> are higher than those estimated from LAI<sub>MTVI2</sub>. This difference comes from the strong increase in the radar signal during the early crop's growth after the emergence (06/23/2010, day 174). On this date, the SAR images were acquired early in the morning (at 6 A.M.), when dew was present on the vegetation, as evidenced by the difference between the air temperature and the dew point (derived from the measurements of air temperature and relative humidity) of less than 2 °C. Consequently, the presence of free water on the crop leads to an important increase of SAR signal, as previously pointed out by [51], [52]. This increase of SAR signal depends on the crop development. Indeed, it is absent for “late-sowed” fields as sunflower has not yet emerged, limited for the early-sowed fields where vegetation is well developed (i.e., the maximum of LAI has already been reached and the inflorescence has appeared), and important for the late-sowed
<figure><img src="image_9.png" /><figcaption>Fig. 8. Comparison of sunflower yields estimated by using the SAFY-WB model, respectively, controlled by LAI<sub>MTVI2</sub> and DM or LAI<sub>σ°C−VV</sub> and DM data (a). The simulated yields with a difference of less than 20% are highlighted in black, while the values overestimated when using LAI<sub>MTVI2</sub> and DM or LAI<sub>σ°C−VV</sub> and DM to control the model are in red or green, respectively. The light and dark green circles correspond to estimates before and after the correction of dew effects on SAR signal. Mean values of estimates lead to the map of sunflower yields over 23 fields (b).</figcaption></figure>

fields where sunflower has just emerged (i.e., sunflower is at its first phenological stages where the vegetation is mainly composed by leaves before the elongation of the principal stem). In that last case, the effect of dew is reduced by accounting the results obtained in [30] and [53], leading to a mean decreasing of 4.2 dB of the initial acquired radar signal. The estimates of sunflower yield are slightly lower, and close or under a 20% of difference (comparison between light and dark green circles in the Fig. 8(a)). Finally, the mean of yields, estimated by the two approaches, is used to produce the map presented in Fig. 8(b).

V. CONCLUSION

This paper investigated the possibilities of combining the LAI derived from optical (MTVI2) or radar backscatter ($\sigma_{X-HH}^0$, $\sigma_{C-HH}^0$, $\sigma_{C-VV}^0$, and $\sigma_{VH}^0$) satellite data and the measured

crop DM trough different strategies into an agrometeorological model in order to estimate the sunflower yield.

The calibration steps have first shown that the possible limitations of using a optical satellite sensors (like nonconvergence or equifinality problems due to a decrease of satellite data during cloud cover periods) can be overcome by considering microwave images. Among the tested satellite configurations, the calibration of the model based on the LAI derived from MTVI2, $\sigma_{C-HH}^0$, or $\sigma_{C-VV}^0$ offers comparable good performances. Moreover, the results have shown that the combination of satellite images with the agrometeorological model leads to accurate crop parameter estimations (i.e., LAI and DM), with $R^2$ upper than 0.84 and rRMSE inferior to 30%.

Moreover, the use of LAI<sub>MTVI2</sub> or LAI<sub>σ°C−VV</sub> to control the agrometeorological model allows an accurate simulation of the temporal features related to the crop calendar (i.e., dates of emergence, early- and late-crop growth behaviors, or dates of harvest), giving access to the interfield variability. The simulated yields, obtained when the model is controlled by LAI<sub>MTVI2</sub> or LAI<sub>σ°C−VV</sub> and DM, are quite similar in most cases (with a difference of less than 20% on 16 of 23 fields and RMSE inferior to 0.54 t·ha<sup>-1</sup>), which demonstrates that microwave data offer the same performances than the optical one, without the inconvenience of being dependant of the cloud cover or of the atmospheric correction, and without any modification of the formalism of the model (already validated for other crops: soybean, wheat, corn). The exceptions are explained by an overestimation of the LAI (which affects optical data) and the presence of dew (which affects SAR signal). Those effects have been successfully reduced by combining optical and SAR data in the yield map processing chain.

Those promising results constitute one demonstrative case showing the possibilities of combining multiconfiguration satellite data and an agrometeorological model. Nevertheless, some limits remain and several steps of test and validation are still required in view of application. The proposed approach needs to be extended to multiyear observations, using the on-going satellite mission (Sentinel 1, Radarsat Constellation, Sentinel 2, Landsat 8, etc.), and has to be evaluated over more fields of sunflower. Moreover, in accordance with farmers requirements, the model must be now improved regarding the crop yield simulation from an agronomic point of view (phenological cycle, crop water content, or allocation of the total mass to the fruit) in order to better take benefit of SAR and optical satellite images.

In perspective, such an approach could be extended to other crops (e.g., sorghum, rapeseed or rice) and to other spatial scales (intrafield, regional, etc.) to test its robustness.

ACKNOWLEDGMENT

The authors would like to thank the farmers Blanquet, Bollati, Brardo, Pavan, and Peres, for their time and discussions, and the people who helped with collecting the ground data. They would also like to thank S. Laburthe for her valuable English proofreading.
REFERENCES

[1] W. G. M. Bastiaanssen, D. J. Molden, and I. W. Makin, "Remote sensing for irrigated agriculture: Examples from research and possible applications," *Agricultural Water Manage.*, vol. 46, no. 2, pp. 137–155, 2000.

[2] S. Kalluri, P. Gilruth, and R. Bergman, "The potential of remote sensing data for decision makers at the state, local and tribal level: Experiences from NASA's synergy program," *Environ. Sci. Policy*, vol. 6, pp. 487–500, 2003.

[3] S. K. Seelan, S. Laguette, G. M. Casady, and G. A. Seielstad, "Remote sensing applications for precision agriculture: A learning community approach," *Remote Sens. Environ.*, vol. 88, pp. 157–169, 2003.

[4] H. McNair, J. Shang, X. Jiao, and C. Champagne, "The contribution of ALOS PALSAR multipolarization and polarimetric data to crop classification," *IEEE Trans. Geosci. Remote Sens.*, vol. 47, no. 12, pp. 3981–3992, Dec. 2009.

[5] R. Fieuzal et al., "Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops," *Hydrol. Earth Syst. Sci.*, vol. 15, pp. 1117–1129, 2011.

[6] R. Hadria et al., "Combined use of optical and radar satellite data for the detection of tillage and irrigation operations: Case study in central Morocco," *Agricultural Water Manage.*, vol. 96, pp. 1120–1127, 2009.

[7] C. Marais Sicre, F. Baup, and R. Fieuzal, "Determination of the crop row orientations from formosat-2 multi-temporal and panchromatic images," *ISPRS J. Photogramm. Remote Sens.*, vol. 94, pp. 127–142, 2014.

[8] H. Yang et al., "Temporal polarimetric behavior of oilseed rape (Brassica napus L.) at C-band for early season sowing date monitoring," *Remote Sens.*, vol. 6, pp. 10375–10394, 2014.

[9] J. M. Lopez-Sanchez, S. R. Cloude, and J. D. Ballester-Berman, "Rice phenology monitoring by means of SAR polarimetry at X-band," *IEEE Trans. Geosci. Remote Sens.*, vol. 50, no. 7, pp. 2695–2709, Jul. 2012.

[10] J. Betbeder, R. Fieuzal, Y. Philippets, L. Ferro-Famil, and F. Baup, "Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops," *J. Appl. Remote Sens.*, vol. 10, no. 2, pp. 026020, 2016.

[11] H. Yang et al., "Wheat lodging monitoring using polarimetric index from RADARSAT-2 data," *Int. J. Appl. Earth Obs. Geoinf.*, vol. 34, pp. 157–166, 2015.

[12] M. Launay and M. Guerif, "Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications," *Agriculture, Ecosyst. Environ.*, vol. 111, pp. 321–339, 2005.

[13] M. Claverie et al., "Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data," *Remote Sens. Environ.*, vol. 124, pp. 844–857, 2012.

[14] D. Courault et al., "Assessing the potentialities of FORMOSAT-2 data for water and crop monitoring at small regional scale in south-eastern France," *Sensors*, vol. 8, pp. 3460–3481, 2008.

[15] B. Duchemin et al., "Impact of sowing date on yield and water-use-efficiency of wheat analyzed through spatial modeling and FORMOSAT-2 images," *Remote Sens.*, vol. 7, pp. 5951–5979, 2015.

[16] D. B. Lobell, G. P. Asner, J. I. Ortiz-Monasterio, and T. L. Benning, "Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties," *Agriculture, Ecosyst. Environ.*, vol. 94, pp. 205–220, 2003.

[17] M. S. Moran, Y. Inoue, and E. M. Barnes, "Opportunities and limitations for image-based remote sensing in precision crop management," *Remote Sens. Environ.*, vol. 61, pp. 319–346, 1997.

[18] R. A. Fischer, "Number of kernels in wheat crops and the influence of solar radiation and temperature," *J. Agricultural Sci.*, vol. 105, pp. 447–461, 1985.

[19] R. C. Muchow, T. R. Sinclair, and J. M. Bennett, "Temperature and solar radiation effects on potential maize yield across locations," *Agronomy J.*, vol. 82, pp. 338–343, 1990.

[20] CETIOM. *TOURNESOL: Les Techniques Culturales, Le Contexte Economique*. Paris, France: CESTIOM, 2004, pp. 1–36.

[21] N. Baghdadi, N. Boyer, P. Todoroff, M. El Hajj, and A. Bégué, "Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on reunion island," *Remote Sens. Environ.*, vol. 113, pp. 1724–1738, 2009.

[22] R. Fieuzal, F. Baup, and C. Marais Sicre, "Monitoring wheat and rapeseed by using synchronous optical and radar satellite data—From temporal signatures to crop parameters estimation," *Adv. Remote Sens.*, vol. 2, pp. 162–180, 2013.

[23] W. Koppe et al., "Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data," *Int. J. Appl. Earth Obs. Geoinf.*, vol. 21, pp. 568–576, 2013.

[24] M. Rinaldi et al., "Assimilation of COSMO-SkyMed-derived LAI maps into the AQUATER crop growth simulation model. Capitanata (Southern Italy) case study," *Eur. J. Remote Sens.*, vol. 46, pp. 891–908, 2013.

[25] L. Dente, G. Satalino, F. Mattia, and M. Rinaldi, "Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield," *Remote Sens. Environ.*, vol. 112, pp. 1395–1407, 2008.

[26] J. Betbeder, R. Fieuzal, and F. Baup, "Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield," *IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens.*, vol. 9, no. 6, pp. 2540–2553, Jun. 2016.

[27] S. Paloscia, G. Macelloni, P. Pampaloni, and S. Sigismondi, "The potential of C- and L-band SAR in estimating vegetation biomass: The ERS-1 and JERS-1 experiments," *IEEE Trans. Geosci. Remote Sens.*, vol. 37, no. 4, pp. 2107–2110, Jul. 1999.

[28] G. Macelloni, S. Paloscia, P. Pampaloni, F. Marliani, and M. Gai, "The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops," *IEEE Trans. Geosci. Remote Sens.*, vol. 39, no. 4, pp. 873–884, Apr. 2001.

[29] E. Santi et al., "The retrieval and monitoring of vegetation parameters from COSMO-SkyMed images," in *Proc. Geosci. Remote Sens. Symp.*, 2012, pp. 7031–7034.

[30] R. Fieuzal and F. Baup, "Estimation of leaf area index and crop height of sunflowers using multi-temporal optical and SAR satellite data," *Int. J. Remote Sens.*, vol. 37, pp. 2780–2809, 2016.

[31] F. Baup et al., "MCM'10: An experiment for satellite multi-sensors crop monitoring. From high to low resolution observations," in *Proc. Geosci. Remote Sens. Symp.*, 2012, pp. 4849–4852.

[32] Terresinovia. [Online]. Available: http://www.terresinovia.fr/; Accessed on: Sep. 8, 2016.

[33] H. Breit, T. Fritz, U. Balss, M. Lachaise, A. Niedermeier, and M. Vonavka, "TerraSAR-X SAR processing and products," *IEEE Trans. Geosci. Remote Sens.*, vol. 48, no. 2, pp. 727–740, Feb. 2010.

[34] L. C. Morena, K. V. James, and J. Beck, "An introduction to the RADARSAT-2 mission," *Can. J. Remote Sens.*, vol. 30, pp. 221–234, 2004.

[35] NEST. *Next ESA SAR Toolbox*. [Online]. Available: http://nest.array.ca/web/nest; Accessed on: Nov. 26, 2015.

[36] J. S. Chern, J. Ling, and S. L. Weng, "Taiwan's second remote sensing satellite," *Acta Astronaut.*, vol. 63, pp. 1305–1311, 2008.

[37] M. Arnaud and M. Leroy, "SPOT 4: A new generation of SPOT satellites," *ISPRS J. Photogramm. Remote Sens.*, vol. 46, pp. 205–215, 1991.

[38] O. Hagolle, G. Dedieu, B. Mougenot, V. Debaecker, B. Duchemin, and A. Meygret, "Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to formosat-2 images," *Remote Sens. Environ.*, vol. 112, pp. 1689–1701, 2008.

[39] J. W. Rouse, R. H. Haas, J. A. Schell, D. W. Deering, and J. C. Harlan, "Monitoring the vernal advancements and retrogradation of natural vegetation," NASA/GSFC, Greenbelt, MD, USA, Final Rep. E74-10676, NASA-CR-139243, 1974, pp. 1–137.

[40] D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, "Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture," *Remote Sens. Environ.*, vol. 90, pp. 337–352, 2004.

[41] J. Liu et al., "Quantifying crop biomass accumulation using multi-temporal optical remote sensing observations," in *Proc. 30th Can. Symp. Remote Sens./30e Symp. Canadien sur la télédétection*, Lethbridge, AB, Canada, Jun. 22–25, 2009, pp. 10.

[42] J. L. Monteith and C. J. Moss, "Climate and the Efficiency of Crop Production in Britain," *Philosoph. Trans. Roy. Soc. London, Ser. B, Biol. Sci.*, vol. 281, pp. 277–294, 1977.

[43] S. J. Maas, "Parameterized model of gramineous crop growth. 1. Leaf-area and dry mass simulation," *Agronomy J.*, vol. 85, pp. 348–353, 1993.

[44] C. A Chimenti and A. J Hall, "Grain number responses to temperature during floret differentiation in sunflower," *Field Crops Res.*, vol. 72, no. 3, pp. 177–184, 2001.

[45] B. Duchemin, P. Maisongrande, G. Boulet, and I. Benhadj, "A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index," *Environ. Model. Softw.*, vol. 23, pp. 876–892, 2008.
[46] M. Weiss, F. Baret, G. J. Smith, I. Jonckheere, and P. Coppin, "Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling," *Agricultural Forest Meteorol.*, vol. 121, pp. 37–53, 2004.

[47] J. R. Kiniry, C. A. Jones, J. C. O'toole, R. Blanchet, M. Cabelguenne, and D. A. Spanel, "Radiation-use efficiency in biomass accumulation prior to grain-filling for five grain-crop species," *Field Crops Res.*, vol. 20, pp. 51–64, 1989.

[48] P. Garofalo and M. Rinaldi, "Leaf gas exchange and radiation use efficiency of sunflower (Helianthus Annuus L.) in response to different deficit irrigation strategies: From solar radiation to plant growth analysis," *Eur. J. Agronomy*, vol. 64, pp. 88–97, 2015.

[49] M. A. Soriano, F. Orgaz, F. J. Villalobos, and E. Fereres, "Efficiency of water use of early plantings of sunflower," *Eur. J. Agronomy*, vol. 21, pp. 465–476, 2004.

[50] A. J. de la Vega and A. J. Hall, "Effects of planting date, genotype, and their interactions on sunflower yield," *Crop Sci.*, vol. 42, pp. 1191–1201, 2002.

[51] M. Herold, C. Pathe, and C. C. Schmullius, "The effect of free vegetation water on the multi-frequency and polarimetric radar backscatter—First results from the TerraDew 2000 campaign," in *Proc. Geosci. Remote Sens. Symp.*, 2001, pp. 2445–2447.

[52] S. C. M. Brown, S. Quegan, K. Morrison, J. C. Bennett, and G. Cook-martin, "High-resolution measurements of scattering in wheat canopies-implications for crop parameter retrieval," *IEEE Trans. Geosci. Remote Sens.*, vol. 41, no. 7, pp. 1602–1610, Jul. 2003.

[53] M. Zribi, S. Le Hégarat-Mascle, C. Ottlé, B. Kammoun, and C. Guerin, "Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS wind scatterometer and SAR data," *Remote Sens. Environ.*, vol. 86, pp. 30–41, 2003.

**Remy Fieuzal** received the M.S. degree in ecology, biostatistics, and modeling from the University Paul Sabatier, Toulouse, France, in 2007, where he also received the Ph.D. degree in remote sensing and agronomy, for his work on the contributions of satellite radar data for estimating biophysical parameters of agricultural land, in 2013.

He is working on image processing and analysis of satellite data acquired in optical and microwave domains, with the Centre d'Etudes Spatiales de la BIOsphère, Toulouse. His research interests include mapping of different surface parameters (leaf area index, biomass, irrigation, or top soil moisture) and agrosystems modeling.

**Claire Marais Sicre** received the M.S. degree in social history from the University Toulouse, in 2006. Since 2008, she has been an Engineer in remote sensing working on images acquired in optical and microwave domains with the Centre d'Etudes Spatiales de la BIOsphère Laboratory, Toulouse, France.

She is working on mapping of land use and land cover and different surfaces parameters. Her research interests include remote sensing and ground survey to understand the functioning and monitor agricultural surfaces.

**Frederic Baup** received the M.S. degree in microwaves and optical telecommunications and the Ph.D. degree in SAR remote sensing from the University Paul Sabatier, Toulouse, France, in 2003 and 2007, respectively.

Since 2008, he has been a Researcher in microwave remote sensing with the Centre d'Etudes Spatiales de la BIOsphère Laboratory, Toulouse. He has authored international journals. He is a Reviewer for journals and conference proceedings. His education interests are focused on physics and remote-sensing sciences. His research interests include microwave remote sensing applied to land surfaces and SAR image analysis to monitor spatio-temporal variations of soil (moisture and roughness) and vegetation (biomass) properties over agricultural or natural areas.

Dr. Baup is a Principal Investigator of projects supported by the European Space Agency, the Canadian Space Agency, the Japan Aerospace Exploration Agency, and the German Aerospace Center agencies.