A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping
"> Figure 1
<p>Location of study site and 104 sampling points.</p> "> Figure 2
<p>Distribution of yield monitoring datasets for uncleaned and cleaned yield data (data not interpolated).</p> "> Figure 3
<p>Maps showing yield monitoring datasets: Raw data (left), Step1 and Step2 data (center), data cleaned (right). Raw and Steps 1–2 maps are colored according to quartiles of yield distributions (data not interpolated).</p> "> Figure 4
<p>Relationship between observed yield (yield monitor) and NDVI (Landsat-8) for 2013–2014 crop season in (<b>a</b>) 19 March and (<b>b</b>) 20 April.</p> "> Figure 5
<p>Relationship between observed yield (yield monitor) and NDVI (Landsat-8) for 2014–2015 crop season in (<b>a</b>) 14 April and (<b>b</b>) 30 April.</p> "> Figure 6
<p>Relationship between observed yield (yield monitor) and NDVI (Landsat-8) for 2015–2016 crop season in (<b>a</b>) 13 March, (<b>b</b>) 9 April, (<b>c</b>) 18 May and (<b>d</b>) 27 May.</p> "> Figure 7
<p>Relationship between observed yield (yield monitor) and NDVI (Sentinel-2) for 2015–2016 crop season.</p> "> Figure 8
<p>Modelled LAI (green), observed rainfall after heading (blue) and satellite observation (red) for (<b>a</b>) 2013–2014, (<b>b</b>) 2014–2015, (<b>c</b>) 2015–2016 and (<b>d</b>) 2016–2017 seasons.</p> "> Figure 9
<p>Relationship between observed yield (sampled) and NDVI (Landsat-8) for 2016–2017 crop season in (<b>a</b>) 2 March, (<b>b</b>) 12 April and (<b>c</b>) 30 May.</p> "> Figure 10
<p>Relationship between observed yield (sampled) and NDVI (Sentinel-2) for 2016–2017 crop season in (<b>a</b>) 9 March, (<b>b</b>) 29 March, (<b>c</b>) 8 April and (<b>d</b>) 18 May.</p> ">
Abstract
:1. Introduction
- Evaluate the correctness of yield monitoring maps comparing them with hand sampled yield data;
- Evaluate the ability of the most commonly used VI (NDVI) calculated from Landsat-8 and Sentinel-2 satellite platforms to understand within-field variability;
- Understand the optimal time for NDVI acquisition for better yield evaluation;
- Evaluate the relations between NDVI and yield for four durum wheat crop seasons with different climatic conditions and yield performance.
2. Materials and Methods
2.1. Study Site and Field Trial
2.2. Hand Yield Samplings and Yield Map Monitoring
2.3. Satellite Data
2.4. Data Analysis
2.5. Modelling
3. Results and Discussion
3.1. Yield Map and Yield Sample
3.2. Yield and NDVI
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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LANDSAT-8 | SENTINEL-2 | |
---|---|---|
2014 | 19 March, 20 April | NA |
2015 | 14 April, 30 April | NA |
2016 | 13 March, 9 April, 18 May, 27 May | 23 May |
2017 | 2 March, 12 April, 30 May | 9 March, 29 March, 8 April, 18 May |
Year | Number of Yield Monitoring Data | Skewness | Yield Monitoring vs. Yield Sampling | |||||
---|---|---|---|---|---|---|---|---|
RAW | Step1 | Step2 | RAW | Step1 | Step2 | RAW | Step1 + Step2 | |
2014 | 6369 | 4703 −26.2% | 4641 −1.32% | 17.72 | 2.72 | 2.71 | r = 0.11 p-value = 0.25 RMSE = 1.14 t/ha | r = 0.40 p-value < 0.0001 RMSE = 1.05 t/ha |
2015 | 6351 | 4737 −25.4% | 4648 −1.88% | 21.58 | 0.65 | 0.44 | r = 0.37 p-value < 0.0001 RMSE = 0.68 t/ha | r = 0.50 p-value < 0.0001 RMSE = 0.59 t/ha |
2016 | 6699 | 5084 −24.1% | 4967 −2.30% | 23.94 | 0.39 | 0.38 | r = 0.43 p-value < 0.0001 RMSE = 0.84 t/ha | r = 0.49 p-value < 0.0001 RMSE = 0.82 t/ha |
2014 | 2015 | 2016 | 2017 | |||||
---|---|---|---|---|---|---|---|---|
Sample(t/ha) | Monitor(t/ha) | Sample(t/ha) | Monitor(t/ha) | Sample(t/ha) | Monitor(t/ha) | Sample(t/ha) | Monitor(t/ha) | |
Mean | 3.07 | 2.65 | 2.31 | 2.11 | 3.88 | 3.90 | 5.00 | N/A |
Min | 0.42 | 0.18 | 0.87 | 0.15 | 2.15 | 0.12 | 3.23 | N/A |
Max | 5.69 | 10.48 | 3.64 | 6.90 | 6.01 | 11.62 | 7.43 | N/A |
Std | 1.01 | 1.12 | 0.57 | 0.53 | 0.71 | 0.91 | 0.94 | N/A |
Number of Days | Total Rainfall (mm) | Air Temperature (°C) | LAI Rate of Change | ||
---|---|---|---|---|---|
2014 | H to M A to M | 79 57 | 125.4 89.6 | 20.51 22.33 | −0.099 |
2015 | H to M A to M | 59 41 | 64.0 64.0 | 21.34 21.64 | −0.142 |
2016 | H to M A to M | 83 64 | 129.2 81.8 | 18.88 19.62 | −0.085 |
2017 | H to M A to M | 67 46 | 104.6 62.4 | 19.46 20.58 | −0.131 |
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Toscano, P.; Castrignanò, A.; Di Gennaro, S.F.; Vonella, A.V.; Ventrella, D.; Matese, A. A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping. Agronomy 2019, 9, 437. https://doi.org/10.3390/agronomy9080437
Toscano P, Castrignanò A, Di Gennaro SF, Vonella AV, Ventrella D, Matese A. A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping. Agronomy. 2019; 9(8):437. https://doi.org/10.3390/agronomy9080437
Chicago/Turabian StyleToscano, Piero, Annamaria Castrignanò, Salvatore Filippo Di Gennaro, Alessandro Vittorio Vonella, Domenico Ventrella, and Alessandro Matese. 2019. "A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping" Agronomy 9, no. 8: 437. https://doi.org/10.3390/agronomy9080437