Estimation of Corn Yield by Assimilating SAR and Optical Time Series Into a Simplified Agro-Meteorological Model: From Diagnostic to Forecast

Ameline, Maël; Fieuzal, Rémy; Betbeder, Julie; Berthoumieu, Jean-François; Baup, Frédéric

doi:10.1109/jstars.2018.2878502

Cited by 24 publications

(26 citation statements)

References 36 publications

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“…GAI SAR are empirically derived from the sensitivity of the SAR signals to the GAI, from a non-linear regression (Equation 8 saturated and remained at a high value until harvest, whereas the GAI decreased during senescence, as shown in the literature [35].…”

Section: Methodsmentioning

confidence: 99%

“…The S1-A images were pre-processed (radiometric calibration, thermal 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 spacing) from the Google Earth engine website, so as to obtain the sigma-naught ratio (σ 0 VH/VV ), the best index for monitoring the vegetative part of corn, from the Sentinel-1 data [35,37].…”

Section: Satellite Data Acquisition Calendarmentioning

confidence: 99%

“…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. 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].…”

Section: The Agro-meteorological Modelmentioning

confidence: 99%

“…This approach is thus robust enough to be not significantly affected by the difference in GAI retrieval until flowering (1000 • C day), contrary to empirical approaches in which a small change in the backscattering coefficient induces a proportional change in the GAI retrieval [45][46][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 sufficiently sensitive to senescence [35,37,48,49]. Figure 7 shows 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).…”

Section: Model Calibrationmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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Section: Methodsmentioning

confidence: 99%