Early season mapping of winter wheat in China based on Landsat and Sentinel images

. Early season crop identification is of great importance for monitoring crop growth and predicting yield for decision-makers and private sectors. As one of the largest producers of winter wheat worldwide, China outputs more than 18% of the 15 global production of winter wheat. However, there are no distribution maps of winter wheat over a large spatial extent with high spatial resolution. In this study, we applied a phenology-based approach to distinguish winter wheat from other crops by comparing the similarity of the seasonal changes of satellite-based vegetation index over all croplands with a standard seasonal change derived from known winter wheat fields. Especially, this study examined the potential of early season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for three years (2016-2018) 20 over eleven provinces, which produce more than 98% of the winter wheat in China. A comprehensive assessment based on survey samples revealed producer’ and user’ accuracies higher than 89.30% and 90.59%, respectively. The estimated winter wheat area exhibited good correlations with the agricultural statistical area data at the municipal and county levels. In addition, the earliest identifiable time of the geographical location of winter wheat was achieved by the end of March, giving a lead time of approximately three months before harvest, and the optimal identifiable time of winter wheat was at the end of April with 25 an overall accuracy of 89.88%. These results are expected to aid in the timely monitoring of crop growth. The 30 m winter wheat maps in China are available via an open-data repository (DOI: http://doi.org/10.6084/m9.figshare.12003990. Dong et al., 2020).

of wheat, winter wheat dominates the wheat production in many countries including China, United States, France, Russia, Ukraine, Argentina, and Australia (National Bureau of Statistics of China, 2018;USDA-ERS, 2018). It accounts for more than 70% of the total wheat production in the United States (USDA-ERS, 2018). Quickly acquiring the detailed location and 35 planting area of winter wheat provides the basis for forecasting winter wheat yield, understanding winter wheat management, and assessing food security (Franch et al., 2015(Franch et al., , 2019Huang et al., 2015;Wang et al., 2019;Zhang et al., 2019;Zhuo et al., 2019).
Satellite-based methods are an effective and quick tool for crops mapping owing to their great spatial coverage and temporal continuity (Belgiu and Csillik, 2017;Griffiths et al., 2019;Jin et al., 2019). Most studies have used supervised classification 40 methods, such as decision tree classification (Brown and Pervez, 2014;Wardlow and Egbert, 2008), and supervised machine learning methods , such as random forests Yin et al., 2020), support vector machines , and neural networks (Cai et al., 2018;Zhong et al., 2019) to distinguish crop types. However, these methods strongly depend on the selection of the training samples, which is time-consuming and labor-intensive (Skakun et al., 2017b). For instance, 30 m-resolution Cropland Data Layer (CDL) product generated by the USDA National Agricultural 45 Statistics Service (NASS), classified more than 100 types of crops grown in the United States using the decision tree classification method (Boryan et al., 2011). The CDL product uses a large volume of USDA Common Land Unit (CLU) data as training samples, which are renewed every year. In Nebraska alone, more than 250,000 CLU polygon records were used to train and validate the CDL product (Boryan et al., 2011). Such large volumes of CLU data can only be acquired with government supports and are usually confidential (Boryan et al., 2011). Therefore, the accuracy of national and sub-national 50 crop classification products based on supervised classification algorithms is limited because of the lack of training datasets (Petitjean et al., 2012).
As an alternative approach, several studies have used phenological characteristics as a metric for identifying geographic locations of winter wheat (Qiu et al., 2017;Skakun et al., 2017b;Wardlow et al., 2007). The common method differentiates winter wheat from other crops based on the differences in key phenological phases (e.g., tillering, heading, and harvesting) in 55 combination with spectral signatures (Pan et al., 2012;Skakun et al., 2017a). Some studies integrate accumulated Growing Degree Day (GDD) to consider the phenology difference to reduce phenology variability due to different climatic conditions (Franch et al., 2015;Skakun et al., 2017b;Zhong et al., 2014). Other methods like Dynamic Time Warping (DTW), has been proven to be an effective solution for mapping crop distribution, e.g., for identifying rice paddy fields (Guan et al., 2016) and classifying vegetables types (Li and Bijker, 2019). DTW was initially designed for speech recognition (Sakoe and Chiba, 60 1978). Maus et al. (2016) proposed a time-weighted version of the DTW method, namely Time-Weighted Dynamic Time Warping (TWDTW), which accounts for seasonality in crop types, thus further improving the classification accuracy. Unlike supervised classification methods, these methods require very low volumes of training data, thus substantially reducing the need for field surveys (Belgiu and Csillik, 2017).
winter wheat constituting 95% of the total wheat production in China (National Bureau of Statistics of China, 2018). Numerous studies have been conducted to identify the cultivation map of winter wheat at county (Pan et al., 2012), province (He et al., 2019) and regional scale (Wu et al., 2007). Significant efforts have been made to generate a planting area map of winter wheat over the large regions of China. Based on MODIS surface reflectance products, Qiu et al. (2017) used the differences in Enhanced Vegetation Index before and after heading dates to develop two indicators to map winter wheat in the major winter 70 wheat producing regions of China. A recent study generated a 30 m-resolution distribution map of winter crop, instead of winter wheat over the main producing areas in China using the decision tree classification method . However, several limitations in existing winter wheat maps remain. First, previous studies showed that MODIS dataset failed to identify the planting areas of winter wheat because of the relatively low spatial resolution . In China, because of the large population and implementation of household responsibilities, farmers have the freedom to select the type of crop they 75 wish to plant. The planting areas per household is only 1.37 ha on average (Guo, 2008), which accounts for 5% of a 500-m MODIS pixel. Therefore, identification methods with low spatial resolution data (e.g., MODIS dataset) will result in large misclassifications (Qiu et al., 2017). Second, identifications based on high spatial resolution satellite datasets still show large uncertainty in several regions. For example, based on the Landsat-7, -8 and Sentinel-2 images with a spatial resolution of 30 m, Tian et al., (2019) found a relative error greater than 50% in identifying the planting areas compared to statistical data for 80 Hubei and Shanxi provinces.
Especially, identifying the geographic location and areas of winter wheat as earlier as possible is important for monitoring crop growth, simulating crop water use, and meeting the timeliness requirement of yield predictions (Chipanshi et al., 2015;Song et al., 2017a). Under the background of climate change, the frequencies of extreme weather events and natural disasters are expected to increase (Trenberth et al., 2014;Zambrano et al., 2018). Therefore, early mapping of crop distribution is 85 urgently necessary for policy-makers to reduce economic loss and assess food security . Identifying the crop distribution at the early season is more challenging than that by the end of growing season, because of the limited input information.
In this research, we used a phenology-based method to identify the geographic locations of winter wheat in China and produced a 30 m-resolution winter wheat map for the period of 2016-2018. Moreover, we explored the potential for early season mapping 90 of the planting areas of winter wheat and determined the earliest identifiable time and optimal identifiable time. The identification accuracy was assessed based on field surveys, visual interpretation results of very high-spatial resolution images, and agricultural statistical data. The proposed method can generate winter wheat maps that can be updated annually, proving a useful tool for crop management and policy making.

Study Area
This study identified planting areas of winter wheat for the period of 2016-2018 in eleven provinces covering an area of 390

Method
The methodological workflow consists of the following steps: (1) image pre-processing to construct monthly maximum composite NDVI images, and extract that of cropland based on FROM-GLC product (see section 2.3 for more details); (2) 105 data processing, which produces standard seasonal change of NDVI for winter wheat for each province based on the winter wheat samples; (3) winter wheat identification, where TWDTW is used to measure the similarity of seasonal changes of NDVI for known winter wheat fields with investigated fields, and area statistical data at province-level are used to determine the thresholds of similarity measurements; (4) evaluation, for assessing the classification accuracies ( Figure 2). <<Figure 2>> 110

Time-weighted Dynamic Time Warping
In this study, we used the Time-Weighted Dynamic Time Warping (TWDTW) method to identify the planting locations and areas of winter wheat. The TWDTW is an improved version of the DTW algorithm (Petitjean et al., 2012;Sakoe and Chiba, 1978). In the DTW algorithm, the distance (i.e., cost) (Figure 3a) between two time series, namely series X of known winter wheat field and series Y of unknown land cover, is calculated by warping the series Y via stretching or shortening the time 115 dimension (Figures 3b and c), in order to find the optimal warping path, which is the minimum distance between the two series.
Compared to other similarity-based methods, such as the Euclidean distance, the DTW is more advantageous in that it can flexibly deal with the temporal distortions associated with seasonal change, such as amplitude, time scaling, or shifting (Lhermitte et al., 2011). Taking the seasonal change in land cover types into consideration, Maus et al., (2016) added a timeconstraint to the DTW (i.e., TWDTW) to balance shape matching and phenological change, thus further increasing 120 identification reliability contrast with the DTW method.

<<Figure 3>>
In order to use the TWDTW method, first, the standard seasonal change curve of NDVI of winter wheat retrieved at some known winter wheat fields is required (Figure 4). Taken each province as a unit, the dissimilarity values can then be calculated by comparing the seasonal change in NDVI of each investigated pixel with the standard seasonal curve of winter wheat in a 125 given province. The pixels with low dissimilarity values have a higher probability of being winter wheat. In this research, we employ the area statistical data of winter wheat at the province level to determine the thresholds of dissimilarity. The pixels (Nth) having the lowest dissimilarity values are considered winter wheat in a given province, and the total area of all N pixels should be equal to the statistical area of winter wheat in the investigated province.
This study used satellite-based NDVI extracted from Sentinel-2 and Landsat composite imageries to indicate the seasonal 130 change in the vegetation. The standard seasonal curve of winter wheat was generated by averaging the NDVI with 20% of the winter wheat pixels randomly selected from field surveys in each province (see Section 2.3). The winter wheat over all the eleven provinces has similar seasonal changes ( Figure 4). Generally, winter wheat reaches the maximum growth period during March to June and is harvested during May to June. We assumed that the seasonal change of winter wheat for each province does not vary from year to year. We used the standard seasonal curves derived from NDVI measurements taken in 2018 to 135 identify the planting area of winter wheat for the period of 2016-2018 to further examine the applicability of the method.

<<Figure 4>>
To determine the earliest identifiable time, we employed incremental time windows by setting October 1 of the previous year as the start and extending it with an increment of one month until next June, to compare the seasonal changes with different lengths. In other words, we started to identify the planting areas from previous October, and subsequently, at each month, a 140 new image is acquired to compose longer time series and generate a new identification. The influence of seasonal change length on identification accuracies was assessed based on these classification accuracies.

Removing the Disturbances of Winter Rapeseed
Three winter crops are grown over the whole study area, including winter wheat, winter rapeseed, and winter garlic. The first two crops constitute 91 and 8% of the planting area of winter crops, respectively (National Bureau of Statistics of China, 2018); 145 winter rapeseed may affect the identification of winter wheat. Relying solely on optical imagery to discriminate them would be a challenge because of their similar spectral characteristics and phenological stages (Veloso et al., 2017). Widely planted in HuB province, winter rapeseeds cover an area of 0.97 million ha, nearly equal to that of winter wheat with 1.1 million ha reported in 2017 (Hubei Statistical Bureau, 2018). In addition, winter rapeseed is grown in AH and JS provinces, and its total area is 0.78 million ha, whereas the total area of winter wheat grown here is 4.57 million ha (Anhui Statistical Bureau, 2018;150 Jiangsu Statistical Bureau, 2018). Winter garlic is mainly distributed in SD, HN, JS, and HB provinces. Compared with winter wheat, the planting area of winter garlic is very small. For example, as the largest garlic producer, SD province plants 0.15 million ha of garlic, accounting for only 3.8% of winter wheat in 2017 (Shandong Statistical Bureau, 2018). Therefore, this study ignored the impact of garlic when identifying the planting areas of winter wheat.
Fortunately, the difference in the plant structure between winter wheat and winter rapeseed makes it possible to differentiate 155 them based on radar data (Veloso et al., 2017). Therefore, we used radar data to exclude the interference from winter rapeseed in this study. By investigating the survey samples in HuB province, we found that the VH backscatter values in April are a good indicator to differentiate winter wheat from winter rapeseed. The VH backscatter values in April for winter wheat were lower than −15.5 whereas they were higher for winter rapeseed (Figure 5), which meant the pixels (with VH values greater than −15.5) had less possibility to plant winter wheat. Accordingly, by assigning a higher dissimilarity to these pixels, this 160 study distinguished winter wheat and rapeseed in HuB, JS, and AH provinces. <<Figure 5>>

Classification Accuracy Assessment
The identification accuracy of winter wheat was evaluated based on two methods: 1) validation using the ground truth samples at the field level, including ground surveys and visual interpretation of very high-resolution images from Google Earth, and 2) 165 comparisons with agricultural statistical data at administrative units. Eighty percent of the winter wheat samples and all nonwinter wheat samples were selected to obtain the confusion matrix of the winter wheat map for each province (see Section 3 for more details). The overall accuracy (OA) was measured to investigate the overall effectiveness of the method. The producer's accuracy (PA) shows the proportion of ground truth samples properly judged as the target class, and the user's accuracy (UA) shows the proportion of samples judged as the target class on the classification map that are actually present 170 on the ground. In addition, the planting area of winter wheat identified in this study were compared with those obtained from agricultural statistical data at the county and municipal levels through Pearson's correlation coefficient. Other statistical indicators, including the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE), were also used to evaluate the performance.

Satellite Data
The methodology in this study mainly relied on the similarity measurement between the NDVI seasonal change in an investigated pixel and a known seasonal change of winter wheat. Two different data sources were used to calculate the NDVI: the constellation of Landsat-7, 8 and Sentinel-2 satellites. The NDVI was derived from the Surface Reflectance (SR) products produced by the United States Geological Survey (USGS), which have been processed for atmospheric corrections. The quality 180 bands provided by the SR products were used to remove pixels contaminated by clouds. The study also used the NDVI obtained from the Multi-Spectral Instrument (MSI) sensor onboard Sentinel-2. The SR products generated from Level-2A products by running Sen2Cor provided by ESA (https://github.com/senbox-org) were used. We employed the QA60 band to mask clouds from the Sentinel-2 images (ESA, n.d.). As a result, the study region with an area of 390 million ha corresponded to 4.3 billion 30-m pixels covering the entire winter wheat growing season (October to July) during the period of 2016-2018. Monthly 185 cloud-free image frequencies from October to July at each pixel are visualized in Figure 6.
To differentiate winter wheat from other winter crops (i.e., winter rapeseed), this study used the synthetic aperture radar (SAR) (i.e., Ground Range Detected, Level-1, GRD) product from Sentinel-1. It had a dual-polarized vertical transmission with VV (vertical transmit/vertical receive) and VH (vertical transmit/horizontal receive) bands. We processed each image and acquired the backscatter coefficient (σ°) in decibels (dB) on the platform of Google Earth Engine (GEE) (as operated by the ), comprising thermal noise removal, radiometric calibration, and terrain correction (orthorectification). Even with standard noise-reduction techniques applied, SAR images contained a speckle noise due to the interferences between adjacent backscatter returns. In this study, we chose the refined Lee filter, as described in (Abramov et al., 2017), to further correct the SAR images for speckle noise.
In this study, the VH and NDVI data are both composited into their corresponding monthly maximum images, respectively, 195 for the period between October 1, 2015 and July 31, 2018 on the platform of GEE. The operations were run on GEE in pixels: within a month, we obtained NDVI values of all available clean pixels, and got the maximum for the monthly composite. The pixels of the monthly composite imageries had the highest quality and represented the whole month. Whereas a small number of pixels had no values. The reason for this is that imageries from Landsat 7, 8 and Sentinel had several pixels with bad quality owing to clouds, cloud shadows, and/or no data acquisition (e.g., failure of Landsat 7) ( Figure 6). 200 <<Figure 6>>

Field Data
To obtain the standard seasonal change curve of winter wheat and validate how the proposed method performs, we collected survey samples from the following three sources. First, thirty-eight sites (red triangles in Figure 1 Moreover, the total number of field sites, survey samples, and Google Earth samples for each province are showed in Table 1.  (2018), where the planting areas were inferred 220 based on weighting of the sampling croplands. The area statistical data are the most reliable data with a high accuracy (Franch et al., 2015). The municipality-level statistical data of winter wheat can be found in only eight provinces and county-level data in only six provinces.

Results
To examine the potential for early season identification of winter wheat and explore how early we could produce the 225 distribution maps before the harvest, we investigated the method with shorter time windows and assessed its performance based on all the survey samples collected, which correspond to 33776 pixels in total. We compared the producer's accuracy (PA), user's accuracy (UA), and overall accuracy (OA) for different seasonal change lengths starting from October, with monthly increments thereafter (Figure 7). The identification accuracy increases with seasonal change length until March with an overall accuracy of 87.3%. From April onward, the identification results reach saturation in terms of the accuracy, with an 230 overall accuracy close to maximum, 89.88%. This indicates that the method can identify the planting area of winter wheat three months before harvest (i.e., March), with stable performance until April.

<<Figure 7>>
We used the time window from October to April to compare the similarity between the seasonal change of investigated fields and that of known winter wheat field; thus, we produced winter wheat distribution maps (Figure 8). Our method shows good 235 performance in identifying the planting areas of winter wheat over all the eleven provinces. Based on winter wheat and nonwinter wheat survey samples, the overall identification accuracy varies among the eleven provinces, ranging from 84.97% to 95.85% (Table 2). The user's accuracy (UA) and producer's accuracy (PA) are high in most provinces. For SC and GS provinces, the same approach produced the lowest PA of winter wheat, 72.78 and 73.08%, respectively (Table 2).
<< Table 2>> 240 << Figure 8>> In addition, this method accurately estimates the areas of winter wheat compared to the available agricultural statistical data at the municipal and county levels (Figure 9). The correlation coefficient (R 2 values) between the identified and agricultural statistical areas ranges from 0.85 to 0.99 at the municipal level ( Figure 9Ⅰ,a-h), indicating a strong correlation. At the county level, the method performs a little worse, with correlation coefficient (R 2 values) ranging from 0.7 to 0.88 ( Figure 9Ⅱ, a-h). 245 Considering the MAE and the RMSE, JS, HN, and AH show higher error at the municipal and county level.
<< Figure 9>> Finally, we examined the capability of the method for extending the standard seasonal change of NDVI acquired from a single year to apply it in other years (i.e., 2016 and 2017). We used the same seasonal change of NDVI of winter wheat for each province derived from field samples obtained from 2018 to compare the dissimilarity with that of unknown fields for 2016-250 2017. We then compared the estimated winter wheat areas with agricultural statistical area for the two years ( Figure 10). R 2 and slope for the period of 2016-2018 changed little in most provinces, except for JS and HN provinces at the county level.

Discussion
Winter wheat is one of the most important crops in the world, and information on its spatial extent is critical for making 255 economic and grain subsidy policies (FAOSTAT, 2018). To our knowledge, there are currently no distribution maps for winter wheat over China on a large scale with a spatial resolution of 30 m. Previous studies have made efforts to generate the distribution map of winter wheat over the major producing areas in China based on moderate spatial resolution satellite data (i.e., MODIS) (Qiu et al., 2017). However, owing to small plot sizes for crops, the distribution map with moderate resolution may lead to large uncertainties because of mixed pixels, further restricting the classification accuracy . 260 Machine learning methods, such as random forests and support vector machine, have been proven to be effective in identifying the spatial distribution of various crops (Cai et al., 2018;Liu et al., 2018); these methods, however, strongly depend on the number of training samples, thus restricting the large-area crop mapping because of the lack of data (Belgiu and Csillik, 2017;Millard and Richardson, 2015;Valero et al., 2016).
In this study, we generated winter wheat distribution maps with a spatial resolution of 30 m for the period of 2016-2018 based 265 on the TWDTW method using Landsat and Sentinel-derived monthly maximum composite NDVI. The results obtained based on field surveys and statistical data indicate that the proposed method can accurately identify the winter wheat planting areas over all the eleven provinces. Compared to machine learning methods, our method performs well even if with only a few training samples, which is a significant advantage for large-scale crop identification given the lack of survey samples available (King et al., 2017). In addition, the performance is ideal even when using the same standard seasonal change of the winter 270 wheat for each province for the years when ground surveys are lacking ( Figure 10). Therefore, the proposed method can identify winter wheat quickly with a few training samples and can be extended for years when training samples are scarce (Maus et al., 2016). A recent research suggested that the TWDTW method is more robust contrast to other identification techniques, such as the random forests, when there are only a small number of training samples (Belgiu and Csillik, 2017).
More importantly, this method can identify planting areas of winter wheat before three months of harvesting (i.e., March) and 275 can achieve a stable performance in April, which are significant for early and continuous winter wheat production predictions (Franch et al., 2015;McNairn et al., 2014). Therefore, understanding where crops are distributed, especially during early within-season, is a top priority in predicting total production and monitoring trends in production (Shao et al., 2015;Skakun et al., 2017b). Existing agricultural estimates on crop area or mapping of crop distribution are usually available at the end of the season or after crop harvest (Boryan et al., 2011;Zhong et al., 2019), and the limited input information makes early 280 identification of winter wheat distribution a challenge (Kontgis et al., 2015;Song et al., 2017b). For example, machine learning methods strongly depend on field survey data and time-series features as input; this increases the difficulty in early identification because collecting field data during the season is time-consuming and laborious, especially over large areas (Skakun et al., 2017b;Song et al., 2017a). Moreover, the time-series input features are generally obtained for the entire growing season, making early mapping more challenging (Johnson, 2016). In this study, our results indicate that early-season 285 identification of winter wheat planting area is feasible up to three months before harvesting with limited imageries and time information.
Some potential uncertainties could affect the identification accuracy. First, the quantity of cloud-free satellite data substantially determines effectiveness of retrieving the seasonal change of crop growth; this can influence the identification quality (Dong et al., 2020). In this study, we used all the available satellite data of Landsat and Sentinel and composited multi-temporal 290 monthly maximum NDVI images, in order to avoid cloud contamination as much as possible. However, there are large differences in the available images among various provinces; it remains a challenge to acquire cloud-free images in cloudy and rainy southern areas, such as in SC, HuB and JS provinces (Song et al., 2017b). The low identification accuracy at these provinces is likely due to the relatively poor data quality of satellite data . Second, although the seasonal change of winter wheat is relatively consistent in most provinces (i.e., a low peak in NDVI in winter and a high peak in NDVI 295 in spring), there is an inter-class difference in winter wheat in each province, such as wheat variety, sowing time, and irrigation conditions. Some winter wheat fields may have an earlier sowing time, showing a pattern deviation from standard average pattern of this province, and therefore, may lead to some omission errors. Besides, there are some specialness in the NDVI seasonal change curves of SC and HB provinces, where NDVI shows increasing trend from October to April. This is different from the typical seasonal change curves with two NDVI peaks during the growing season and this may make it difficult to 300 differentiate winter wheat from other crops. That maybe the reason for relatively lower identification accuracy. So, the identification of winter crops in warmer regions should be paid more attention.

Data availability
The derived winter wheat maps in China for three years (2016)(2017)(2018) are available at http://doi.org/10.6084/m9.figshare.12003990 (Dong et al., 2020). 305 To help the readers to reproduce this work, Table 3 summarizes the data source and platform information of datasets and processing steps in this study. The input datasets came from three parts including: GEE platform, our group, and free access websites. Specifically, the four satellite datasets in section 2.3.1 were available at GEE platform. The survey samples were collected by our group from the three sources, which has been introduced in detail in section 2.3.2. The land cover product (i.e., FROM-GLC product) in section 2.3.3 was downloaded from the free website from Tsinghua University, and the 310 agricultural statistical area data in section 2.3.3 was downloaded from the National Bureau of Statistics of China.
In addition, the process of monthly maximum NDVI composition was implemented on the GEE platform. TWDTW algorithm, the exclusion of disturbances of winter rapeseed, and classification accuracy assessment were operated on the localhost platform.
<< Information on the geographical location and distribution of crops at global, national and regional scales is valuable for many applications. To our knowledge, there are no published distribution maps for winter wheat over China on a large scale with a spatial resolution of 30 m. Based on the available Landsat and Sentinel imageries and a time-weighted dynamic time warping (TWDTW) method, this study produced an unprecedented 30 m-spatial resolution winter wheat distribution map of China for 320 the period of 2016-2018. The method performed well over the eleven provinces that produce more than 98% of the winter wheat in China. When validated with 33776 survey samples, the overall accuracy was 89.88%, and the producer's and user's accuracies reached 89.30% and 90.59%, respectively. The resultant planting areas of winter wheat were spatially consistent with the agricultural statistical area, and the method explained 78% of the spatial variabilities in the planting areas at the county level averaged over six provinces. More importantly, this method is effective in identifying the planting areas of winter wheat 325 three months prior to harvest, which is beneficial for early yield estimation. In general, this paper produced a 30 m-spatial resolution winter wheat map of China, which are expected to contribute to the timely monitoring of winter wheat growth. In the future work, the main goal to be achieved is to improve the method and apply to other staple crop (e.g., corn and rice), and complete the staple crops maps at national scales eventually.       and wheat class are correctly classified, respectively. UA of non-winter wheat and UA of winter wheat indicate the ratio of the total quantity of pixels correctly classified into the objective class (i.e., non-winter wheat and winter wheat) to the total quantity of pixels classified into the objective class using proposed method.