**1. Introduction**

Wheat is the main grain crop for mankind [1]. Yellow rust (*Puccinia striiformis f.* sp. *tritici Erikss*) is a devastating disease in wheat planting that affects wheat growth, thus seriously affecting the quality and yield of wheat in China [1,2]. The average annual area of wheat yellow rust is 4 million hm2, resulting in a reduction in wheat production of more than 1 billion kg per year [3]. Traditional methods of wheat yellow rust involve manual surveys that are time-consuming, laborious, and inefficient [4]. In recent decades, remote sensing technology has been proved to be an effective tool for monitoring of crop disease and pest, with advantages of large-scale and real time simultaneous monitoring [4,5]. Therefore, timely, effective, and accurate monitoring of wheat yellow rust based on remote

**Citation:** Zheng, Q.; Ye, H.; Huang, W.; Dong, Y.; Jiang, H.; Wang, C.; Li, D.; Wang, L.; Chen, S. Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study. *Remote Sens.* **2021**, *13*, 278. https://doi.org/ 10.3390/rs13020278

Received: 11 December 2020 Accepted: 12 January 2021 Published: 14 January 2021

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sensing technology and multi-source data of disease occurrence is essential to ensure food security and agricultural sustainability in China.

It is well-known that the phenology of vegetation is closely related to the seasonality of meteorological variables [6]. In addition, the integration of meteorological data with satellite hydrological models can improve irrigation scheduling managemen<sup>t</sup> and help farmers to properly make rational irrigation plans [7]. For pathogens, the propagation, spread, and infection of pathogen spores require suitable environmental support; thus, the monitoring of crop pests and diseases is related to environmental conditions [4,8]. Meteorological data, such as temperature, humidity, sunshine, and rainfall are the key factors to determining the occurrence, development, and prevalence of crop diseases [4]. Zhang et al. showed that integrating meteorological data (precipitation, temperature, humidity, and solar radiation) and remote sensing features has significant potential to forecast the occurrence probability of wheat powdery mildew [9]. Papastamati et al. stated that the inoculation concentrations, temperature, and rainfall time were related to light leaf spot epidemics in winter oilseed rape disease and proposed a new model for predicting the disease [10]. Habitat monitoring has grea<sup>t</sup> potential for evaluating the occurrence and distribution of plant diseases on a regional scale [8].

In addition, biophysical parameters of the host have a certain effect on the infection of diseases [11]. In different infestation stages, plants exhibit specific host–pathogen interactions, such as a reduction in leaf area index, pigment content changes, and canopy structure morphology destruction [12]. These changes respond in the visible to near-infrared regions of the spectrum and can even be captured through multi-temporal observations [11–13]. Zhang et al. used single- and multi-temporal images from HJ satellite to monitor wheat powdery mildew, and proved that multi-stage images were superior to single-temporal images for wheat disease monitoring, with monitoring accuracy reaching 78% [14]. Ma et al. developed a multi-temporal vegetation index that indicated crop growth status and habitat characteristics to monitor wheat powdery mildew based on the k-nearest neighbor approach [15]. Therefore, the integration of habitat information and the host growth status related to disease and pest occurrence have grea<sup>t</sup> potential for crop stress monitoring.

Classifiers can learn the characteristics of target classes from training samples and apply this information to unclassified data. Methods, such as decision tree (DT), k-nearest neighbors (kNN) method, support vector machine (SVM), and artificial neural network (ANN), have been developed and applied in objects recognition and species classification in the remote sensing field [16–18]. Among these, the SVM and ANN methods are popular for crop classification and disease remote sensing monitoring [4,16].

Satellites with different spatial resolutions have been used in agricultural disease identification and monitoring [4]. Razz et al. and Yue et al. effectively utilized the high-resolution of PlantScope imagery (3 m) to detect soybean sudden death syndrome and rice diseases, respectively [19,20]. Calderón et al. used high spatial resolution and hyperspectral imagery for Verticillium wilt of olive for early detection [21]. Yuan et al. used high spatial resolution (Worldview 2) and medium-spatial resolution (Landsat 8) satellite images to monitor the distribution of wheat disease and pest, achieving a monitoring accuracy of more than 71.0% [8]. Therefore, satellite imagery has proven to be an efficient tool for monitoring crop disease and pest in farms on large regional scales. The Sentinel-2 multispectral satellite launched in 2015, has a 290 km swath width and a 10 days revisit cycle, although it can reach up to 5 days if two satellites work simultaneously [22]. Sentinel-2 multispectral images have 13 spectral bands with a spectral range that includes visible, near-infrared, and shortwave infrared regions; their spatial resolutions are 10, 20, and 60 m, respectively (Figure 1). The most innovative feature of the Sentinel-2 satellite is that it contains rich red-edge bands information with center wavelengths of 705, 740, and 783 nm, which provides abundant information for vegetation biophysical status monitoring and estimation [23,24]. In summary, Sentinel-2 has an unpreceded spatial, temporal resolution and revisit cycle, which is suitable for monitoring crop growth processes such as crop diseases or pest stress [25].

**Figure 1.** Parameters and information of Sentinel-2 multispectral satellite.

Yellow rust is an air-dispersed pandemic disease [2]; its occurrence is strongly related to habitat conditions, such as humidity, sunshine, and temperature. However, most of the existing remote sensing identification methods for wheat yellow rust depend on spectral data; few studies have considered the habitat characteristics of yellow rust disease [8]. In this study, we considered the characteristics of spectral changes and meteorological factors in the occurrence period of wheat yellow rust; and developed a large-scale and high-precision monitoring method for wheat yellow rust on a regional scale that integrates environment conditions and growth status. The primary aims of this research are: (1) To present a series of two-temporal vegetation indices for monitoring wheat yellow rust using two-stages satellite remote sensing images on a regional scale; (2) to explore the feasibility of combining remote sensing and meteorological information for yellow rust monitoring; and (3) to develop an optimal classification method for monitoring wheat yellow rust utilizing both spectral information and meteorological data on a regional scale.
