Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.)
"> Figure 1
<p>Asparagus crop growth and production cycles. Every season after harvest, new asparagus stems emerge to begin a new production cycle</p> "> Figure 2
<p>Test site. (<b>Left</b>): Sentinel 2 RGB image acquired the 29/09/2018. (<b>Center</b>): Sentinel-1 RGB image acquired the 30/09/2018. (<b>Right</b>): Location of test site in Peru</p> "> Figure 3
<p>Average temperature and solar radiation conditions in the test site.</p> "> Figure 4
<p>Number of asparagus stems in each of the crop stages. The solid lines represent the averages of all the ground measurements collected in 442 plots during the first 8 months of 2019. The shaded regions represent plus and minus two standard deviations.</p> "> Figure 5
<p>Sentinel-1 temporal backscatter evolution. This chart shows the mean backscatter plus/minus two standard deviations of 442 parcels backscatter time series.</p> "> Figure 6
<p>Images taken in the fields the 28/05/2019 (red vertical line) when a Sentinel-1 image was also acquired. The time series correspond to the VH backscatter. Each pair of image and time series correspond to a different parcel. The parcels are at different growth stages taking advantage of the local climate.</p> "> Figure 7
<p>VH polarisation and ground truth observations for a typical parcel during four consecutive campaigns. Both the backscatter and the ground truth show a seasonal behaviour. The green and red vertical lines represent the start and end of the cultivation period respectively. The campaigns 1 and 3 correspond to summer season while the campaigns 2 and 4 represent winter campaigns.</p> "> Figure 8
<p>SAR backscatter of two consecutive campaigns aligned as function of cultivation days (DaS) and accumulated temperature (GDD). The blue line corresponds to the campaign one that grew during the colder season (“winter”) and red line corresponds to one in the warmer season (“summer”). The plots at the bottom show the cultivation period length measured as a function of time (<b>left</b>) and temperature (<b>right</b>).</p> "> Figure 9
<p>Campaign length measured in degrees Celsius (accumulated temperature) as a function of the production cycle starting month. As an example, if a campaign starts in January it normally accumulates around 600 degrees more than a campaign that starts in July. A total 442 campaigns were considered to generate this plot.</p> "> Figure 10
<p>Observed vs. Predicted number of asparagus stems per stage, with the corresponding overall coefficient of determination and root mean squared error, using features of scenario C3 to train the model.</p> "> Figure 11
<p>Predicted (red) vs. test (blue) number of asparagus stems per stage, using the cultivation days associated with the testing data-points as x-axis.</p> "> Figure 12
<p>Multi-task regression performance metrics as a function of the number of images used to train the model for each of the scenarios of <a href="#remotesensing-12-01993-t002" class="html-table">Table 2</a>.</p> "> Figure 13
<p>Number of asparagus stems estimated for each of the crop stages for the 2018/10/12 Sentinel-1 image (Same as <a href="#remotesensing-12-01993-f002" class="html-fig">Figure 2</a> and intermediate plot of <a href="#remotesensing-12-01993-f014" class="html-fig">Figure 14</a>).</p> "> Figure 14
<p>RGB composites of the estimated crop stage. Red: (Ramification+Aperture+flowering), Green: Maturation, Blue: Emergence.</p> ">
Abstract
:1. Introduction
1.1. Related Work
1.2. Objectives of the Study
- To analyse the SAR response to the asparagus crop evolution.
- To present examples of how the seasonal climatological conditions influence the crop development in the test site (tropical conditions).
- To present the implementation of a data-driven methodology that captures the recurrent patterns in the SAR response and the temperature to provide an approximation of the crop development at every new SAR acquisition. It consists of a Multi-output machine learning regression algorithm in which each output estimates the number of asparagus stems that are present in each of the predefined phenological stages at a given date.
2. Materials and Methods
2.1. Asparagus Crop Development and Production Cycles
2.2. Test Site
2.3. Climatological Conditions
2.4. Ground Truth
2.5. Sar Datasets
2.6. Methodology for Estimating Asparagus Stems Per Stage
2.6.1. Sar Sensitivity to Crop Evolution
2.6.2. Impact of Temperature on the Crop and the Sar Response
2.7. Estimation of Number of Asparagus Stems in Each Crop Stage
2.7.1. Model Development
2.7.2. Inputs
2.7.3. Outputs
2.7.4. Training and Testing Data
2.7.5. Model Hyper Parameters
2.7.6. Accuracy Metrics
3. Results
3.1. Single-Sar Image Results
3.2. Multi-Temporal Sar Results
3.3. Growth Stage Estimation Maps
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Pass Direction | Relative Orbit | Inc. Angle | Acquisition Time |
---|---|---|---|
Descending | 142 | 35 | 10:54 |
Ascending | 18 | 31 | 23:34 |
Ascending | 91 | 45 | 23:42 |
Category | Scenario | Input | Description |
---|---|---|---|
A | A1 | DaS | Only number of days after cultivation started |
A | A2 | DaS, DoY | Days after cultivation started, Day of year when cultivation started |
A | A3 | DaS, DoY, AGDD | Days after cultivation started, Day of year when cultivation started, accumulated temperature |
B | B1 | VH | Only VH polarisation |
B | B2 | VH, VV, VH/VV | VH, VV polarisations and the VH/VV ratio |
C | C1 | VH, VV, VH/VV, DaS | VH, VV, Ratio, Days after cultivation started |
C | C2 | VH, VV, VH/VV, DaS, DoY | VH, VV, Ratio, Days after cultivation started, Day of year when cultivation started |
C | C3 | VH, VV, VH/VV, DaS, DoY, AGDD | All previous features |
Hyperparameter | Selected |
---|---|
Bootstrap | True |
The number of trees in the forest | 800 (a) |
Split funciton | Mahalanobis Distance |
max-depth | 30 (a) |
Stage | A1 | A2 | A3 | B1 | B2 | C1 | C2 | C3 |
---|---|---|---|---|---|---|---|---|
Emergence | 0.72 | 0.83 | 0.88 | 0.54 | 0.68 | 0.78 | 0.83 | 0.84 |
Aperture | 0.65 | 0.9 | 0.92 | 0.04 | 0.27 | 0.71 | 0.87 | 0.9 |
Ramification | 0.74 | 0.9 | 0.9 | −0.22 | 0.11 | 0.81 | 0.9 | 0.9 |
Flowering | 0.41 | 0.79 | 0.82 | −0.36 | −0.06 | 0.49 | 0.7 | 0.76 |
Maturation | 0.79 | 0.91 | 0.94 | 0.36 | 0.52 | 0.82 | 0.89 | 0.9 |
Overall | 0.66 | 0.87 | 0.89 | 0.07 | 0.3 | 0.72 | 0.84 | 0.86 |
Stage | A1 | A2 | A3 | B1 | B2 | C1 | C2 | C3 |
---|---|---|---|---|---|---|---|---|
Emergence | 4.16 | 3.23 | 2.72 | 5.36 | 4.42 | 3.69 | 3.25 | 3.13 |
Aperture | 3.18 | 1.67 | 1.54 | 5.26 | 4.61 | 2.87 | 1.96 | 1.66 |
Ramification | 2.2 | 1.34 | 1.34 | 4.78 | 4.09 | 1.87 | 1.36 | 1.35 |
Flowering | 2.91 | 1.74 | 1.61 | 4.43 | 3.92 | 2.71 | 2.08 | 1.85 |
Maturation | 6.48 | 4.34 | 3.48 | 11.39 | 9.88 | 6.08 | 4.63 | 4.36 |
Overall | 4.07 | 2.72 | 2.3 | 6.77 | 5.84 | 3.73 | 2.9 | 2.72 |
User Case | Season Start Date | Temperature Data | Scenario |
---|---|---|---|
Small farm interested in estimating occurrence of key dates for planning | known from ground truth | known from ground station | A * |
Medium or large size farms | known from ground truth | known from ground station | C2 and C3 ** |
Large scale monitoring (regional or national level) without temperature | Automatically detected | - | B2 and C2 *** |
Large scale monitoring (regional or national level) and satellite measurements of land surface temperature | Automatically detected | known from satellite measurements of land surface temperature | C3 **** |
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Silva-Perez, C.; Marino, A.; Cameron, I. Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.). Remote Sens. 2020, 12, 1993. https://doi.org/10.3390/rs12121993
Silva-Perez C, Marino A, Cameron I. Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.). Remote Sensing. 2020; 12(12):1993. https://doi.org/10.3390/rs12121993
Chicago/Turabian StyleSilva-Perez, Cristian, Armando Marino, and Iain Cameron. 2020. "Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.)" Remote Sensing 12, no. 12: 1993. https://doi.org/10.3390/rs12121993
APA StyleSilva-Perez, C., Marino, A., & Cameron, I. (2020). Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.). Remote Sensing, 12(12), 1993. https://doi.org/10.3390/rs12121993