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.
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.
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).
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).
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.
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.
Figure 4.
Temporal evolution of the climatic data used as the input of the agro-meteorological model (Rg, ET0, rainfall, and Tmean).
Figure 4.
Temporal evolution of the climatic data used as the input of the agro-meteorological model (Rg, ET0, rainfall, and Tmean).
Figure 5.
Methodology used to estimate TDM, PDM, and EDM from a combination of satellite images, the SAFY-WB model, and ground data.
Figure 5.
Methodology used to estimate TDM, PDM, and EDM from a combination of satellite images, the SAFY-WB model, and ground data.
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.
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.
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).
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).
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.
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.
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.
Figure 10.
Comparison between the dates of the four simulated and observed phenological stages of corn (four to five leaves, flowering, ripening, and harvest).
Figure 10.
Comparison between the dates of the four simulated and observed phenological stages of corn (four to five leaves, flowering, ripening, and harvest).
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).
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).
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 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 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).
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).
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.
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.
Figure 15.
Temporal evolution of the GAIopt (a) and backscattering ratio (b) according to the presence of intercrops (beans) inside the corn plot.
Figure 15.
Temporal evolution of the GAIopt (a) and backscattering ratio (b) according to the presence of intercrops (beans) inside the corn plot.
Figure 16.
Impact of the presence of an intercrop on the empirical relationship estimated between and the GAI of corn.
Figure 16.
Impact of the presence of an intercrop on the empirical relationship estimated between and the GAI of corn.
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.
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) |
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 (D0 and ELUE).
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 (D0 and ELUE).
Parameter | Definition | Domain of Variation | Unit |
---|
PlA | Partition-to-leaf function | 0.05–0.5 | - |
PlB | Partition-to-leaf function | 10−5–10−2 | - |
Stt | Temperature sum for senescence | 0–2000 | °C day |
Rs | Rate of senescence | 0–105 | °C |
D0 | Day0 | 90–250 | day |
ELUE | Effective light-use efficiency | 0.5–6 | g.MJ−1 |
Table 3.
Values of the crop-specific parameters (PlA, PlB, Stt, and Rs) and the field-specific parameters (D0 and ELUE) derived from the calibration step.
Table 3.
Values of the crop-specific parameters (PlA, PlB, Stt, and Rs) and the field-specific parameters (D0 and ELUE) derived from the calibration step.
Parameter | Value |
---|
PlA | 0.08 |
PlB | 3.0 × 10−3 |
Stt | 1361 |
Rs | 3569 |
D0 | 142 |
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.
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 | R² | a | 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 |
Table 5.
Values of the field-specific parameters (D0 and ELUE) derived from the validation step.
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.MJ−1) | 4.32 | 4.40 | 3.93 | 3.79 | 3.14 | 3.67 | 3.73 | 4.12 |
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.
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.
| | n | R² | 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 |