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.

References

1	ZHANG J, WANG N, YUAN L, et al. Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements[J]. Biosystems Engineering, 2017, 162: 20-29.
2	FENG W, SHEN W Y, HE L, et al. Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices[J]. Precision Agriculture, 2016, 17(5): 608-627.
3	GALLEGO F J, KUSSUL N, SKAKUN S, et al. Efficiency assessment of using satellite data for crop area estimation in Ukraine[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 29: 22-30.
4	SETHY P K, BARPANDA N K, RATH A K, et al. Image processing techniques for diagnosing rice plant disease: A survey[J]. Procedia Computer Science, 2020, 167: 516-530.
5	YANG C. Remote sensing and precision agriculture technologies for crop disease detection and management with a practical application example[J]. Engineering, 2020, 6(5): 528-532.
6	ZHENG Q, YE H, HUANG W, et al. Integrating spectral information and meteorological data to monitor wheat yellow rust at a regional scale: A case study[J]. Remote Sensing, 2021, 13(2): ID 278.
7	HUANG W J, LAMB D W, NIU Z, et al. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging[J]. Precision Agriculture, 2007, 8(4-5): 187-197.
8	ZHANG J C, PU R L, WANG J H, et al. Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements[J]. Computers and Electronics in Agriculture, 2012, 85: 13-23.
9	LUO J H, ZHAO C J, HUANG W J, et al. Discriminating wheat aphid damage degree using 2-dimensional feature space derived from Landsat 5 TM[J]. Sensor Letters, 2012, 10(1-2): 608-614.
10	ZHENG Q, HUANG W, CUI X, et al. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery[J]. Sensors, 2018, 18(3): ID 868.
11	HE L, QI S, DUAN J, et al. Monitoring of Wheat powdery mildew disease severity using multiangle hyperspectral remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(2): 979-990.
12	ZHAO J, FANG Y, CHU G, et al. Identification of leaf-scale wheat powdery mildew (Blumeria graminis f. sp. Tritici) combining hyperspectral imaging and an SVM classifier[J]. Plants, 2020, 9(8): ID 936.
13	KHAN I H, LIU H, LI W, et al. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat[J]. Remote Sensing, 2021, 13(18): ID 3612.
14	ZHAO J, XU C, XU J, et al. Forecasting the wheat powdery mildew (Blumeria graminis f. Sp. tritici) using a remote sensing-based decision-tree classification at a provincial scale[J]. Australasian Plant Pathology, 2018, 47(1): 53-61.
15	LIANG W, CARBERRY P, WANG G, et al. Quantifying the yield gap in wheat-maize cropping systems of the Hebei Plain, China[J]. Field Crops Research, 2011, 124(2): 180-185.
16	CHEN H, ZHANG H, LI Y. Review on research of meteorological conditions and prediction methods of crop disease and insect pest[J]. Chinese Journal of Agrometeorology, 2007, 28(2): 212-216.
17	YUAN L, PU R L, ZHANG J C, et al. Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale[J]. Precision Agriculture, 2016, 17(3): 332-348.
18	LOVELAND T R, IRONS J R. Landsat 8: The plans, the reality, and the legacy[J]. Remote Sensing of Environment, 2016, 185: 1-6.
19	WANG W, LIANG S, MEYERS T. Validating MODIS land surface temperature products using long-term nighttime ground measurements[J]. Remote Sensing of Environment, 2008, 112(3): 623-635.
20	HUANG L, JIANG J, HUANG W, et al. Wheat yellow rust monitoring based on Sentinel-2 Image and BPNN model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(17): 178-185.
21	LIU R, ZAHNG S, JIA R. Application of feature selection method in building information extracting from high resolution remote sensing image[J]. Bulletin of Surveying and Mapping, 2018, (2):126-130.
22	BAO W, ZHAO J, HU G, et al. Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning[J]. Sustainable Computing: Informatics and Systems, 2021, 30: ID 100526.
23	QIN F, LIU D, SUN B, et al. Identification of alfalfa leaf diseases using image recognition technology[J]. PLoS One, 2016, 11(12): ID e0168274.
24	ROBNIK-?IKONJA M, KONONENKO I. Theoretical and empirical analysis of ReliefF and RReliefF[J]. Machine Learning, 2003, 53(1): 23-69.
25	JORDAN C F. Derivation of leaf‐area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4): 663-666.
26	ROUSE J W, HAAS R H, SCHELL J A, et al. Monitoring vegetation systems in the great plains with ERTS[C]// In Third ERTS Symposium Volume 1: Technical Presentations. Washington, DC, USA: NASA, 1973: 309-317.
27	GAMON J A, PENUELAS J, FIELD C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency[J]. Remote Sensing of Environment, 1992, 41: 35-44.
28	HUETE A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25(3): 295-309.
29	LIU H, HUETE A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33 (2): 457-465.
30	BROGE N H, LEBLANC E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment, 2000, 76(2): 156-172.
31	RICHARDSON A J, WIEGAND C L. Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing, 1977, 43(12): 1541-1552.
32	PENUELAS J, BARET F, FILELLA I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance[J]. Photosynthetica, 1995, 31(2): 221-230.
33	DU C, REN H, QIN Q, et al. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data[J]. Remote Sensing, 2015, 7(1): 647-665.
34	REN H, DU C, LIU R, et al. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(5): 1723-1738.
35	GAO F, MASEK J, SCHWALLER M, et al. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2207-2218.
36	OLATOMIWA L, MEKHILEF S, SHAMSHIRBAND S, et al. A support vector machine-firefly algorithm-based model for global solar radiation prediction[J]. Solar Energy, 2015, 115: 632-644.
37	HUANG L, LIU W, HUANG W, et al. Remote sensing monitoring of winter wheat powdery mildew based on wavelet analysis and support vector machine[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(14): 188-195.
38	MA H, HUANG W, JING Y. Wheat powdery mildew forecasting in filling stage based on remote sensing and meteorological data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(9): 165-172.
39	XU H. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8 satellite[J]. Chinese Journal of Geophysics-Chinese Edition, 2015, 58(3): 741-747.
40	SHARMA A K, SHARMA R K, BABU K S, et al. Effect of planting options and irrigation schedules on development of powdery mildew and yield of wheat in the North Western plains of India[J]. Crop Protection, 2004, 23(3): 249-253.

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