Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models
<p>Maps showing the location of the study site in (<b>A</b>) southwestern Ontario within (<b>B</b>) the Lake Erie basin. (<b>C</b>) The study site with a topographic overview, adapted from [<a href="#B19-remotesensing-13-04152" class="html-bibr">19</a>].</p> "> Figure 2
<p>Map illustrating high and low yielding regions for 2017 crop year (<b>a</b>). The yields are expressed from a standardized anomaly (Equation (1)) from a mean of 3.04 and standard deviation of 0.46 tonnes/ha. In (<b>b</b>) the yield data removed (based on the criteria described in <a href="#sec2dot2-remotesensing-13-04152" class="html-sec">Section 2.2</a>) are shown in gray.</p> "> Figure 3
<p>Correlation coefficients between study period average crop yield and (<b>a</b>) relative topographic position; (<b>b</b>) slope; (<b>c</b>) Wetness Index; (<b>d</b>) upslope flow path length; (<b>e</b>) Downslope Index; (<b>f</b>) CVA.</p> "> Figure 4
<p>Spatial patterns of R<sup>2</sup> for (<b>a</b>) all crops; (<b>b</b>) corn; (<b>c</b>) winter wheat; (<b>d</b>) soybeans.</p> "> Figure 5
<p>Spatially plotted predicted crop yield residuals and included explanatory variables for (<b>a</b>) average of all crops; (<b>b</b>) corn; (<b>c</b>) winter wheat; (<b>d</b>) soybeans.</p> "> Figure 6
<p>Normalized observed crop yield data for (<b>a</b>) study period average; (<b>b</b>) corn; (<b>c</b>) winter wheat; (<b>d</b>) soybeans. Black outline polygons indicate GWR predicted lowest 10 percent of crop yield.</p> ">
Abstract
:1. Introduction
2. Study Area and Methods
2.1. Site Description
2.2. Processing Crop Yield Data
2.3. LiDAR Dataset Acquisition and Processing
2.4. Statistical Analysis
2.4.1. Field-Wide Statistical Analysis
2.4.2. Local Statistical Analysis
2.4.3. Geographic Weighted Regression
3. Results
3.1. Field Scale Assessment
3.2. Localized Correlation Analysis
3.3. Crop Yield Predictions
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Year | Crop |
---|---|
2011 | Soybeans |
2013 | Soybeans |
2014 | Corn |
2015 | Soybeans |
2016 | Winter Wheat |
2017 | Soybeans |
2018 | Corn |
Topographic Attribute | Description | Source |
---|---|---|
Circular variance of aspect | Local slope aspect variance | [32] |
Depression depth | Vertical depth of depressions | [33] |
Downslope Index | Slope gradient between a cell and downslope cell | [34] |
Catchment area with divergent flow (upslope area) | Multi-directional flow algorithm | [35] |
Impoundment size index | Resulting upslope impoundment from placement of dam | [36] |
Plan curvature | Curvature perpendicular to dominant slope angle | [37] |
Profile curvature | Curvature parallel to dominant slope angle | [37] |
Relative topographic position | Cell elevation relative to surrounding cells | [38] |
Slope | Surface gradient | [37] |
Tangential curvature | Plan curvature related to slope | [37] |
Topographic wetness index | Steady state soil moisture index based slope contributing area | [39] |
Total curvature | Curvature across a surface | [37] |
Upslope flow path length | Average length of each cell’s upslope flow paths | [31] |
Topographic Parameter | R2 |
---|---|
Circular Variance of Aspect | 0.08 |
Depression Depth | 0.00 |
Downslope Index | 0.18 |
Upslope Area | 0.08 |
Impound Size Index | 0.03 |
Plan Curvature | 0.00 |
Profile Curvature | 0.01 |
Relative Topographic Index | 0.27 |
Slope | 0.32 |
Tangential Curvature | 0.02 |
Topographic Wetness Index | 0.24 |
Total Curvature | 0.03 |
Upslope Flow Path Length | 0.21 |
Topographic Attribute | GWR Frequency | Models Used |
---|---|---|
Relative Topographic Position | 4 | All Models |
Slope | 4 | All Models |
Circular variance of aspect | 2 | Corn, Soybeans |
Upslope Flow Path Length | 2 | Wheat, Average |
Downslope Index | 1 | Soybeans |
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Eyre, R.; Lindsay, J.; Laamrani, A.; Berg, A. Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models. Remote Sens. 2021, 13, 4152. https://doi.org/10.3390/rs13204152
Eyre R, Lindsay J, Laamrani A, Berg A. Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models. Remote Sensing. 2021; 13(20):4152. https://doi.org/10.3390/rs13204152
Chicago/Turabian StyleEyre, Riley, John Lindsay, Ahmed Laamrani, and Aaron Berg. 2021. "Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models" Remote Sensing 13, no. 20: 4152. https://doi.org/10.3390/rs13204152