Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier

doi:10.12133/j.smartag.SA202202009

Abstract

Abstract:

Since powdery mildew (Blumeria graminis f. sp. tritici) mainly infects the foliar of wheat, satellite remote sensing technology can be used to monitor and assess it on a large scale. In this study, multisource and multitemporal satellite images were used to monitor the disease and improve the classification accuracy. Specifically, four Landsat-8 thermal infrared sensor (TIRS) and twenty MODerate-resolution imaging spectroradiometer (MODIS) temperature product (MOD11A1) were used to retrieve the land surface temperature (LST), and four Chinese Gaofen-1 (GF-1) wide field of view (WFV) images was used to identify the wheat-growing areas and calculate the vegetation indices (VIs). ReliefF algorithm was first used to optimally select the vegetation index (VIs) sensitive to wheat powdery mildew, spatial-temporal fusion between Landsat-8 LST and MOD11A1 data was performed using the spatial and temporal adaptive reflectance fusion model (STARFM). The Z-score standardization method was then used to unify the VIs and LST data. Four monitoring models were then constructed through a single Landsat-8 LST, multitemporal Landsat-8 LSTs (SLST), cumulative MODIS LST (MLST) and the combination of cumulative Landsat-8 and MODIS LST (SMLST) using the Support Vector Machine (SVM) classifier, that were LST-SVM, SLST-SVM, MLST-SVM and SMLST-SVM. Four assessment indicators including user accuracy, producer accuracy, overall accuracy and Kappa coefficient were used to compare the four models. The results showed that, the proposed SMLST-SVM obtained the best identification accuracies. The overall accuracy and Kappa coefficient of the SMLST-SVM model had the highest values of 81.2% and 0.67, respectively, while they were respectively 76.8% and 0.59 for the SLST-SVM model. Consequently, multisource and multitemporal LSTs can considerably improve the differentiation accuracies of wheat powdery mildew.

Key words: wheat powdery mildew, GF-1, MODIS, Landsat-8, land surface temperature, support vector machine

CLC Number:

S435.121.4

ZHAO Jinling, DU Shizhou, HUANG Linsheng. Monitoring Wheat Powdery Mildew (Blumeria graminis f. sp. tritici) Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier[J]. Smart Agriculture, 2022, 4(1): 17-28.

Figures/Tables 6

Fig. 1

Table 1

Table 2

Selected vegetation indices in this study

Vegetation index	Abbreviation	Formula	Reference
Simple Ratio Index	SRI	SRI= $R N I R / R R e d$ （4）	［25］
Normalized Difference Vegetation Index	NDVI	NDVI = $R N I R - R R e d / R N I R + R R e d$ （5）	［26］
Normalized Difference Greenness Index	NDGI	NDGI = $R N I R - R G r e e n / R N I R + R G r e e n$ （6）	［27］
Soil-Adjusted Vegetation Index	SAVI	SAVI = $1.5 R N I R - R R e d / R N I R + R R e d + 0.5$ （7）	［28］
Enhanced Vegetation Index	EVI	EVI = $2.5 R N I R - R R e d / R N I R + 6 R R e d - 7.5 R B l u e + 1$ （8）	［29］
Triangular Vegetation Index	TVI	$T V I = 0.5 120 R N I R - R G r e e n - 200 R R e d - R G r e e n$ （9）	［30］
Differential Vegetation Index	DVI	DVI= $R N I R - R R e d$ （10）	［31］
Structure Insensitive Pigment Index	SIPI	$S I P I = R N I R - R B l u e / R N I R + R B l u e ?$ （11）	［32］

Table 2

Fig. 2

Table 3

Fig. 3

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Model	Confusion matrix				Accuracy assessment indicator
Model		Healthy	Mild	Severe	User accuracy/%	Producer accuracy/%	Overall accuracy/%	k
LST-SVM	Healthy	14	7	0	66.7	63.6	63.8	0.38
	Mild	8	25	4	83.8	65.8
	Severe	0	6	5	45.5	55.6
SLST-SVM	Healthy	14	7	0	66.7	82.4	76.8	0.59
	Mild	3	32	2	86.5	74.4
	Severe	0	4	7	63.6	77.8
MLST-SVM	Healthy	15	6	0	71.4	88.2	73.9	0.54
	Mild	2	32	3	86.5	71.1
	Severe	0	7	4	36.4	57.1
SMLST-SVM	Healthy	16	5	0	76.2	84.2	81.2	0.67
	Mild	3	33	1	89.2	78.6
	Severe	0	4	7	63.6	87.5

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