# Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Field Survey and Data Collection

#### 2.1.1. Study Area and Field-Experimental-Data Acquirement

**is the number of leaves of severity level i, m is the total number of diseased leaves at the sample point, and M is the total number of leaves observed at the sample point. To avoid the influence of mixed pixels on the monitoring results, the experiment selected a 10 m × 10 m area within a relatively uniform 20 m × 20 m wheat field as the survey sample point. The five-point sampling method was employed to determine the DI for each sample point, an area of 1 m × 1 m was selected at the center, four corners of each survey sample point were taken to record the DI, and then the average value was taken as the DI of the survey sample point. The coordinates of the sample points were measured using a handheld Global Positioning System (GPS) receiver.**

_{i}#### 2.1.2. Meteorological Data and Preprocessing

#### 2.1.3. Remote Sensing Data and Preprocessing

#### 2.2. Remote Sensing Monitoring of Wheat Stripe Rust at a Regional Scale Based on Geographical Detectors

#### 2.2.1. Extraction of Key Phenological Stages in Wheat

#### 2.2.2. Multi-Source Feature Construction for Wheat-Stripe-Rust Monitoring

_{1}represents the vegetation index at the previous moment, and VI

_{2}represents the vegetation index at the subsequent moment.

#### 2.2.3. Feature Selection for Disease and Pest Monitoring Using Geographical Detectors

_{h}and N are the numbers of units in stratum h and the entire population, respectively. σ

_{h}

^{2}and σ

^{2}are the variances of the samples within stratum h and the entire population, respectively. The q-value ranges from 0 to 1, where a higher value indicates a stronger similarity between the spatial distributions of the independent X and dependent variables Y, indicating the greater importance of the independent variable X. It should be noted that factor detection is suitable for handling categorical variables, while the features constructed in this study are continuous variables. To address this, we utilized the Optimal Parameters-Based Geographical Detector Model (OPGD) proposed by Song et al. to discretize the continuous data [63].

#### 2.2.4. Monitoring Modeling

## 3. Results

#### 3.1. Wheat Key-Phenological-Stage Extraction Results

#### 3.2. Feature Importance Analysis

#### 3.3. Monitoring Feature Selection

#### 3.4. Accuracy Validation of Monitoring Results

## 4. Discussion

#### 4.1. Analysis of Spatial-Distribution Differences of Wheat Stripe Rust in the Study Area

#### 4.2. Analysis of the Performance of Spectral Features and Meteorological Features

#### 4.3. Discussion for Next Steps for Research

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**The geographical location of the study area and spatial distribution of sample points. (

**a**) The distribution of wheat fields and survey points within the study area. (

**b**) The approximate location of the study area in Baoji City, Shaanxi Province, China.

**Figure 2.**Methodological framework. The overall framework can be divided into three parts: (1) Construction of monitoring features based on phenological information. (2) Optimal selection of monitoring features using geographic detectors. (3) Monitoring model.

**Figure 3.**(

**Left**) column shows the extracted results of the wheat phenological stages around the Fengxiang agricultural meteorological station. The (

**right**) column displays a statistical analysis of the pixel count in each extraction result. The x-axis represents time, while the y-axis represents the proportion of pixels corresponding to that time in the entire extraction result.

**Figure 4.**Results of the wheat-phenological-stage extraction in the study area. (

**a**–

**c**) represent the extraction results for wheat green-up stage, jointing stage, and heading stage in the study area, respectively.

**Figure 5.**Top 20 ranked features in the geographical detectors’ and ReliefF’s calculation results. (1) Naming rules for the spectral features: xxx_t, xxx represents vegetation index, t represents the initial letter of the phenology, and MSR_H corresponds to the modified simple ratio (MSR) for wheat on the date of heading. It is worth noting that the two-stage normalized vegetation indices and two-stage ratio vegetation indices are named nxxx_t and rxxx_t, respectively. For example, nMSR_JH represents the normalized value of MSR from the jointing to heading stages. (2) Naming rules for the meteorological features: xxx_tm, xxx represents the meteorological variable, t represents the initial letter of the phenology, and m represents the window size or the initial letter of the phenology, e.g., SSD_H15 represents the average sunshine duration in the 15 days before the date of heading. (

**a**) Geographical detectors’ results; and (

**b**) ReliefF results.

**Figure 6.**The mean differences and F-statistic between the healthy and diseased samples for the top 20 ranked features computed using geographic detectors and ReliefF, respectively. (

**a**) Mean differences between the healthy and diseased samples. (

**b**) F-statistic between the healthy and diseased samples.

**Figure 7.**The monitoring accuracy of feature sets at different numbers of features obtained using GD_RFE_RF and R_RFE_RF. For GD_RFE_RF, the highest monitoring accuracy for the RF model was achieved when the feature set contained 11 features. For R_RFE_RF, the RF model exhibited the highest monitoring accuracy when the feature set contained 6 features.

**Figure 8.**Spatial-distribution maps of the regression coefficients for each feature in the optimal monitoring feature set obtained using GD_RFE_RF.

Correlation | Vegetation Indices | Formula |
---|---|---|

Water content | Moisture Stress index, MSI [41] | ${{\displaystyle R}}_{SWIR}/{{\displaystyle R}}_{\mathrm{NI}R}$ |

Disease Water Stress Index, DSWI [42] | $({{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{G})/({{\displaystyle R}}_{SWIR}+{{\displaystyle R}}_{R})$ | |

Shortwave Infrared Water Stress Index, SIWSI [43] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{SWIR})/({{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{SWIR})$ | |

Pigment content | Green Leaf Index, GLI [44] | $({{\displaystyle 2R}}_{G}-{{\displaystyle R}}_{R}-{{\displaystyle R}}_{\mathrm{B}})/({{\displaystyle 2R}}_{G}+{{\displaystyle R}}_{R}+{{\displaystyle R}}_{\mathrm{B}})$ |

Greenness Ratio Vegetation index, GRVI [45] | ${{\displaystyle R}}_{\mathrm{G}}/{{\displaystyle R}}_{R}$ | |

Modified Chlorophyll-Absorption-Ratio Index, MCARIn [46] | $(({{\displaystyle R}}_{r\mathrm{en}}-{{\displaystyle R}}_{\mathrm{R}})-0.2({{\displaystyle R}}_{r\mathrm{en}}-{{\displaystyle R}}_{\mathrm{G}}))\ast ({{\displaystyle R}}_{r\mathrm{en}}/{{\displaystyle R}}_{\mathrm{R}})$ | |

Red–Green–Blue Vegetation Index, RGBVI [47] | $({{\displaystyle \mathrm{R}}}_{\mathrm{G}}^{2}-{{\displaystyle R}}_{B}\ast {{\displaystyle R}}_{\mathrm{R}})/({{\displaystyle \mathrm{R}}}_{\mathrm{G}}^{2}+{{\displaystyle R}}_{B}\ast {{\displaystyle R}}_{\mathrm{R}})$ | |

Structure-Independent Pigment Index, SIPI [48] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{B})/({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{R})$ | |

Normalized Difference Vegetation Index, NDVI [49] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{\mathrm{R}})/({{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{R})$ | |

Green-Normalized Difference Vegetation Index, GNDVI [50] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{G})/({{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{G})$ | |

Excessive Green Index, ExG [51] | $2{{\displaystyle R}}_{G}-{{\displaystyle R}}_{R}-{{\displaystyle R}}_{\mathrm{B}}$ | |

Vegetation coverage | Atmospherically Resistant Vegetation Index, ARVI [52] | $({{\displaystyle R}}_{NIR}-{{\displaystyle 2R}}_{R}+{{\displaystyle R}}_{\mathrm{B}})/({{\displaystyle R}}_{NIR}+{{\displaystyle 2R}}_{R}-{{\displaystyle R}}_{\mathrm{B}})$ |

Difference Vegetation Index, DVI [53] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{R})$ | |

Enhanced Vegetation Index, EVI [50] | $2.5({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{R})/({{\displaystyle R}}_{NIR}+6{{\displaystyle R}}_{R}-7.5{{\displaystyle R}}_{B}+1)$ | |

Modified Simple Ratio Index, MSR [54] | $({{\displaystyle R}}_{NIR}/{{\displaystyle R}}_{R}-1)/(\sqrt{{{\displaystyle R}}_{NIR}/{{\displaystyle R}}_{R}}+1)$ | |

Optimized Soil-Adjusted Vegetation Index, OSAVI [55] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{R})/({{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{R}+0.16)$ | |

Renormalized Difference Vegetation Index, RDVI [56] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{R})/(\sqrt{{{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{R}})$ | |

Simple Ratio Index, SR [57] | ${{\displaystyle R}}_{NIR}/{{\displaystyle R}}_{R}$ | |

Stress status | Normalized Difference Vegetation Index Red Edge, NDVIreln [49] | $({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{\mathrm{r}en})/({{\displaystyle R}}_{NIR}+{{\displaystyle R}}_{ren})$ |

Normalized Red-edge 1 Index, NREDI1 [58] | $({{\displaystyle R}}_{\mathrm{r}e2}-{{\displaystyle R}}_{re1})/({{\displaystyle R}}_{re2}+{{\displaystyle R}}_{re1})$ | |

Normalized Red-edge 2 Index, NREDI2 [58] | $({{\displaystyle R}}_{\mathrm{r}e3}-{{\displaystyle R}}_{re1})/({{\displaystyle R}}_{re3}+{{\displaystyle R}}_{re1})$ | |

Normalized Red-edge 3 Index, NREDI3 [58] | $({{\displaystyle R}}_{\mathrm{r}e3}-{{\displaystyle R}}_{re2})/({{\displaystyle R}}_{re3}+{{\displaystyle R}}_{re2})$ | |

Plant Senescence Reflectance Index, PSRIn [59] | $({{\displaystyle R}}_{R}-{{\displaystyle R}}_{G})/{{\displaystyle R}}_{\mathrm{r}e\mathrm{n}}$ | |

Red-edge Disease Stress Index, REDSI [13] | $((705-665)({{\displaystyle R}}_{\mathrm{Re}3}-{{\displaystyle R}}_{R})-(783-665)({{\displaystyle R}}_{\mathrm{Re}1}-{{\displaystyle R}}_{R}))/(2{{\displaystyle R}}_{R})$ | |

Red-edge Inflection Point, REIP [60] | $705+35(({{\displaystyle R}}_{R}+{{\displaystyle R}}_{\mathrm{Re}2})/2-{{\displaystyle R}}_{NIR})/({{\displaystyle R}}_{\mathrm{Re}1}-{{\displaystyle R}}_{NIR})$ | |

Triangular Vegetation Index, TVI [61] | $0.5(120({{\displaystyle R}}_{NIR}-{{\displaystyle R}}_{G})-200({{\displaystyle R}}_{R}-{{\displaystyle R}}_{G}))$ | |

Band | ${{\displaystyle R}}_{B},{{\displaystyle R}}_{G},{{\displaystyle R}}_{R},{{\displaystyle R}}_{NIR},{{\displaystyle R}}_{\mathrm{re}1},{{\displaystyle R}}_{\mathrm{re}2},{{\displaystyle R}}_{\mathrm{re}3},{{\displaystyle R}}_{\mathrm{SWIR}}$ |

Method | Number | Feature |
---|---|---|

Geographical detectors | 11 | HTEM_H21, LTEM_H7, HTEM_GJ, HTEM_J21, PRE_GJ, RHU_H15, WIN_J7, WIN_G21, NREDI2, SIPI, MCARI2 |

ReliefF | 6 | SSD_H21, PRE_H15, HTEM_J21, PRE_GJ, SIPI, REDSI |

Method | Parameters of RF | Parameters of XGBoost | Parameters of SVM | |||
---|---|---|---|---|---|---|

n_Estimators | max_Depth | n_Estimators | max_Depth | C | Gamma | |

Geographic Detector | 23 | 5 | 14 | 2 | 2 | 0.05 |

ReliefF | 15 | 3 | 11 | 3 | 1 | 0.05 |

Method | Model | Healthy | Infected | Sum | UA | OA | Kappa | |
---|---|---|---|---|---|---|---|---|

Geographic Detectors | RF | Healthy | 45 | 5 | 50 | 90.0% | 87.2% | 0.743 |

Infected | 7 | 37 | 44 | 84.9% | ||||

Sum | 52 | 42 | 94 | |||||

PA | 86.5% | 88.1% | ||||||

XGBoost | Healthy | 40 | 10 | 50 | 80.0% | 80.9% | 0.614 | |

Infected | 8 | 36 | 44 | 81.8% | ||||

Sum | 48 | 46 | 94 | |||||

PA | 83.3% | 78.3% | ||||||

SVM | Healthy | 40 | 10 | 50 | 80.0% | 74.5% | 0.484 | |

Infected | 14 | 30 | 44 | 68.2% | ||||

Sum | 54 | 40 | 94 | |||||

PA | 74.1% | 75.0% | ||||||

ReliefF | RF | Healthy | 43 | 7 | 50 | 86.0% | 84.0% | 0.679 |

Infected | 8 | 36 | 44 | 81.8% | ||||

Sum | 51 | 43 | 94 | |||||

PA | 84.3% | 83.7% | ||||||

XGBoost | Healthy | 42 | 8 | 50 | 84.0% | 78.7% | 0.570 | |

Infected | 12 | 32 | 44 | 72.7% | ||||

Sum | 54 | 40 | 94 | |||||

PA | 77.8% | 80.0% | ||||||

SVM | Healthy | 39 | 11 | 50 | 78.0% | 70.2% | 0.397 | |

Infected | 17 | 27 | 44 | 61.4% | ||||

Sum | 56 | 38 | 94 | |||||

PA | 69.6% | 71.1% |

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## Share and Cite

**MDPI and ACS Style**

Zhao, M.; Dong, Y.; Huang, W.; Ruan, C.; Guo, J.
Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors. *Remote Sens.* **2023**, *15*, 4631.
https://doi.org/10.3390/rs15184631

**AMA Style**

Zhao M, Dong Y, Huang W, Ruan C, Guo J.
Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors. *Remote Sensing*. 2023; 15(18):4631.
https://doi.org/10.3390/rs15184631

**Chicago/Turabian Style**

Zhao, Mingxian, Yingying Dong, Wenjiang Huang, Chao Ruan, and Jing Guo.
2023. "Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors" *Remote Sensing* 15, no. 18: 4631.
https://doi.org/10.3390/rs15184631