1. Introduction
Rice is one of the most important crops in the world and provides the main source of energy for more than half of the world population [
1]. Additionally, the seasonally flooded rice fields contribute about 5%–19% of total global methane emission, an important greenhouse gas source, to the atmosphere [
2,
3]. China produced about one third of the world’s rice on about one fifth of the world’s paddy rice land [
4]. During the past two decades, the arable land in China declined at a speed of 0.25 million hectares per year [
5]. The trend was more obvious in the eastern plain region of China, where intensified human activities have changed the land use and land cover (LULC) patterns dramatically in the last decades. This region has long been one of the major rice growing areas in China, and the cultivar is dominated by the single-cropped rice (SCR). From the food safety, ecological and policy making points of view, a timely and efficient monitoring and mapping of rice cropping area is critical [
6,
7]. Conventionally, the local government usually estimates the cropping area of rice by field survey; however, it is time-consuming and costly. As a powerful alternative, remote sensing has proved its effectiveness in estimating rice cropping areas from regional to global scales [
8,
9,
10].
In the literature, many different kinds of optical remote sensing data, e.g., the Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT VEGETATION and Landsat-MSS, and techniques have been applied in rice cropping area estimating practices [
7,
8,
11,
12,
13]. The data mentioned above have demonstrated advantages in rice monitoring at regional to global scales due to wide range of coverage and relative long data archiving. However, coarse resolution satellite data is not suitable for precise rice crop mapping in the eastern plain region of China because the rice fields in this region are relatively small, irregular, and fragmented by well-developed roads and dense water networks, and generally mixed with other land cover types. As a consequence, the mixed-pixel problem is prominent and induces temporal uncertainty in discriminating the spectral signatures of rice and the other land cover types [
6].
Middle to high spatial resolution satellite data, e.g., Landsat TM/ETM+/OLI, SPOT and China Brazil Earth Resources Satellite (CBERS), are promising in capturing small patches of crop fields [
14,
15]. However, the cost and relatively long revisit cycles partially offset their advantages in spatial resolution. Specifically, the cloud cover during monsoon season, which is partially overlapped with the major growing season of the SCR, makes it more difficult to obtain qualified remote sensing imageries [
16,
17]. For applications where the rice phenology information is critically needed, the satellite data with acceptable spatial resolution and more frequent revisit cycle should be more desirable.
The small sun-synchronous satellites for environment and disaster monitoring and forecasting (HJ-1A/B) of China were launched in 2008. HJ-1A/B satellites have a spatial resolution of 30 m and a revisit cycle of four days (the revisit cycle of the constellation is 2 days), with imaging swath of 700 km. The CCD camera onboard HJ-1A/B includes four bands,
i.e., blue, green, red and near-infrared, and the spectral range is 0.43–0.90 μm. HJ-1A/B CCD data have been applied in rice area estimation [
18,
19] and yield prediction [
20]. In this study, however, it is our interest to explore the potential of using HJ-1A/B data to extract small, irregular SCR growing area in the eastern plain region of China, where the mixed-pixel problem is serious as mentioned above. Specifically, it is our interest to take advantage of its high revisit feature of the HJ-1A/B data to capture the key phenology spectral signatures of the SCR to facilitate the classification.
The unique physical feature of rice fields and the phenology of the SCR may provide valuable information for remote sensing classification. The rice grows on flooded soils, and the rice fields are a mixture of rice plant and open water during the transplanting and early period of the growing season [
21]. As new leaves and tillers emerged, there is an accelerated increase in canopy height and leaf area of the rice. About 50 to 60 days after transplanting, the rice canopy would cover most of the surface area [
22], but the leaf area is still increased till the heading stage. After that, the leaf area of rice starts to decease and the leaf color turns to yellow until the ripening and harvest stages. By using time series remote sensing images, the combined field and phenology features of rice, which differentiate the rice field from the other land cover types, may increase the classification accuracy.
To minimize the interference of external environmental factors, various vegetation indices (VIs) are commonly used in practice [
23,
24,
25]. For example, the well-recognized normalized difference vegetation index (NDVI) [
26] has been testified to be closely correlated with leaf area, biomass, percent ground cover and crop productivity [
27,
28,
29,
30]. Due to the saturation effect, however, NDVI may fail to capture the difference in well-vegetated areas, compared with the enhanced vegetation index (EVI) [
31]. In practice, the time series signatures of NDVI and EVI derived from the MODIS and SPOT data had been used to map the area, species (single, early, and late), and key phenologies of rice [
13,
18,
32]. Recently, a novel VI,
i.e., the 2-band EVI (EVI2), has been proposed and testified to be comparable with the traditional EVI, and more importantly, it may achieve greater consistencies across sensors because only 2 bands are involved, as compared with 3 bands in EVI [
33,
34].
In addition to the data used, it is of critical importance to select appropriate classification method to properly mapping the rice fields. It is our interest to compare the classification efficiencies of the commonly used parametric and nonparametric classification algorithms,
i.e., the maximum likelihood classifier (MLC) and support vector machines (SVM), with a two-step classification method proposed in this study and specifically designed to classify the rice fields from the other land cover types. The MLC is one of the most commonly used classification techniques [
35,
36,
37]. It is a parametric classification algorithm with the assumption that the class signatures are normally distributed. The SVM is a nonparametric classifier, which projects the training data in the input space into a high dimensional space using a kernel function where the classes are linearly separable [
38]. The SVMs have no limitation about the probability distribution forms of the class signature, but its performance largely depends on the kernel used, the parameter choice for the specific kernel, and the method used to generate the SVM [
39,
40,
41].
Our study aimed to investigate the capability of EVI2 in SCR growth monitoring, and to test the feasibility of using HJ-1A/B CCD data to estimate the SCR growing area in the eastern plain region of China. For this purpose, we proposed a simple but effective classification method, which makes use of the time series HJ-1A/B imageries and the specific signatures of EVI2 (including its 1st derivative) at key phenology stages of the SCR. An extensive field campaign was carried out for verification simultaneously. We compared the effectiveness of this method with the parametric and nonparametric classification algorithms, namely MLC and SVM. We also discussed the influence of the mixed-pixel which was typical in the study area and may affect the classification accuracy.
4. Discussion
It is generally acknowledged that using a single-temporal image to well discriminate a specific kind of crop at various phenology stages from the other vegetation (or land cover types) is an enormous challenge [
19,
60,
61]. However, using the spectral characteristics (or vegetation indices) determined by the key phenologies of a specific crop species,
i.e., multi-temporal remote sensing imageries, is a promising way to improve the classification accuracy [
62,
63]. To effectively discriminate the rice field in eastern plain region of China, where the rice field is generally fragmented and irregular due to the topography and widely distributed water bodies and road networks, a specifically designed stepwise remote sensing classification strategy was applied in this study.
The time-series EVI2 data for the major land cover types in the study area were built from the HJ-1 A/B CCD imageries and the S-G filters was applied to smooth the EVI2 time-series. With the reference field campaign data, the EVI2 showed efficient discriminating capability in capturing the spectral differences between SCR and the other land cover types during the key SCR phenology stages (
Figure 5). It is prominent that the EVI2 of SCR increased rapidly during the transplanting and ear differentiation (including early heading) stages, and the temporal resolution of HJ-1 A/B CCD data was testified to be suitable to capture these features. The stepwise classification algorithm proposed in this study can be seen as an exemplar of the decision tree classification category, and it outperformed the parametric (MLC) and nonparametric (SVM) classification algorithms, respectively (
Table 3 and
Table 4). By using EVI2 instead of the reflectance data, the classification accuracies improved to certain extents for both of MLC and SVM. The results also implied that by treating the satellite-derived vegetation classification information hierarchically, the mixtures among spectral feature spaces can be effectively alleviated. For MLC and SVM, total six scenes during SCR transplanting to early reproductive stages (from 2012/06/29 to 2012/09/02) were used, including the transplanting, vegetative to reproductive transition phases. However, it is noteworthy that time-series EVI2 of SCR during this period increased rapidly and intersected with the EVI2s of all the other land cover types, except water bodies (
Figure 5). Therefore, the classification accuracies of MLC and SVM should unavoidably be decreased, because both of the methods treated the spectral signatures contained in the six scenes collectively.
The influence of the mixed-pixel is a primary concern in remote sensing classification practices. We used five ground-truth sites as an example and analyzed the relationship between the purity of pixels (measured as the area proportion of rice field in a specific cell) and the corresponding recognition ratios. The mixed-pixel analysis showed that the recognition ratio was positively correlated with the rice field area proportion at each ground-truth site (
Table 5 and
Figure 8). The sites
D and
E showed the best recognition ratio of SCR among the five ground-truth sites, and it is in accordance with the fragmentation statuses indicated by the landscape indices (
Table 5). It is not unexpectedly that as the area proportion of rice field increased in each cell, the possibility of misclassification decreased consequently, especially for the grade of 75%–100% (refer in particular to rice field).
As large part of the classification error can be attributed to the influence of mixed-pixels where the area proportion of rice field was less than 75%, and most of the mixed-pixels concentrated at the boundaries of the rice fields (
Table 6 and
Figure 9). We further analyzed the classification error caused by the commission and omission errors due to the mixed-pixels and boundary effects, respectively. The results showed that the ratio of the edge pixels to the total rice pixel number correlated with the fragmentation states of each site,
i.e., the number of the edge pixel was positively correlated with land fragmentation states of each site due to the increased rice field perimeter. As a consequence, the classification errors of sites
D–
E were less than sites
A–
C as shown in
Table 6. For rice fields, the misclassification caused by the commission errors was more common, compared with the omission errors (
i.e., cells in which rice field area was less than 50% but was classified as rice field, see
Figure 8).
However, it should be noted that due to the existence of spatial autocorrelation, the classification accuracy reported in this study may be overestimated [
64]. Spatial autocorrelation might be present due to large pixel size [
65] or points sampled in close proximity [
66]. To avoid the artificially increased classification accuracy caused by the random cross-validation using autocorrelated dataset, more than one permanent training/test dataset should be utilized in accuracy assessment [
64]. In this study, five ground-truth sites with different land cover percentages were selected for classification accuracy assessment, however the authors acknowledged that the autocorrelation may still unavoidable and quantitative evaluation of its influence is still a challenge. Further studies should be focused on field data collection, with subsampling and cross-validation like
k-fold method [
64] to improve the classification accuracy assessment.
The extrapolation of the findings in this study must be cautious due to various changes in the environmental factors (e.g., dry or wet) and vegetation status in different regions and years. The aim of this study was to provide a general methodology in the classification of single-cropped rice. However, when applying it to another region or year, the VI thresholds, which are used to distinguish different land cover types, must be decided according to the specific time series satellite images, i.e., the VI thresholds and the timestamps (according to the key phenologies) are variable.