*6.1. Hierarchical Classification for Winter Crops Mapping*

In this study, two different classification approaches using Random Forest machine learning methods were performed on a Sentinel-2 high spatial resolution satellite image that was acquired in April 2019, which is the growing season of the winter crops, in order to detect and map winter wheat and winter barley in a fragmented area that was occupied by different land categories. One of the main objectives of this paper was to successfully extract winter crops data with the hierarchical classification that was proposed in this study, which allows an efficient winter crop type mapping for a study area with a complex landscape, and to easily distinguish winter crops from other land cover types, especially arboreal vegetation, shrubs, grassland, and other crop types. The results of the hierarchical classification were evaluated with different accuracy indicators (global or interclass) and were finally compared with the traditional direct extraction approach.

Both classification approaches achieved a good accuracy level despite the complex occupation and small cropland size in the region, with an overall accuracy of 0.866 and 0.932 and a kappa index of 0.789 and 0.888 for classical direct extraction and hierarchical classification, respectively. Even though the classical extraction method worked well for winter crop mapping, the accuracy assessment indicates that the hierarchical classification is clearly more accurate and better suited to our study by turning a complex multi-class classification problem into a series of smaller classifications. According to the results that are presented in Figure 8 and the accuracy indicators that are displayed in Tables 8 and 9, apart from the global accuracy indicators, the hierarchical classification has proven to be reliable, with outstanding performance in the classification of both winter crops classes, particularly the winter wheat.

The hierarchical classification approach is widely used in many different fields, such as for categorization problems [71], biological predictions [72,73], and music genre classification [74,75], meanwhile the concept of solving a complete classification problem step-by-step using agglomerative algorithms plays also an important role in image classification and its efficacy is well known and it is recognized by previous studies [76–79]. In this study, we proposed a hierarchical classification framework that was constituted by three smaller classifiers for extracting winter crop data, and we have clearly demonstrated the superiority of the hierarchical framework over the classical extraction method.

Additionally, both classifications in this study were performed with supervised RF machine learning methods and highly accurate results were acquired, regardless of the approach or the method. Therefore, RF has proven to be a feasible, well-suited classification algorithm for precisely mapping specific winter crop types from a small-sized field in a complex area.

#### *6.2. Comparison of PBC and OBC*

In this work, PBC and OBC were implemented in the two steps of the classification process within the hierarchical classification structure (croplands extraction from all vegetated area, and winter croplands extraction from all croplands). In addition, the two classification models were trained by a similar dataset, and then evaluated using the same test data. The two classification methods are widely known and used, and they are always compared in different fields. OBC provides a method to the satellite image classification, and numerous studies in the remote sensing field it is demonstrated that OBC usually achieves a better classification with different data and in different landscapes over PBC by bringing complementary information other than the spectral signal and turning classification units from pixels to image objects [80–83]. Whiteside et al. in 2011 [23] indicated that OBC has a better potential for extracting land cover information in a spatially heterogeneous land cover

area, while Weih and Riggan in 2010 [24] proposed that OBC produced more homogeneous classes, as the classes that were produced by PBC are more fragmented. Furthermore, many studies have also pointed out that OBC regularly outperforms PBC for crop type mapping and they noted that it has a more efficient calculation time [84–86]. However, OBC is limited by segmentation errors, such as over-segmentation and under-segmentation, which bring negative impacts to the classification; consequently, low segmentation accuracy leads to low classification accuracy [79,86,87]. Furthermore, some studies have also revealed that the difference in accuracy values between the two methods decreases or even disappears when the same classification algorithms are applied, or the spatial resolution of the image is increased [88–90].

In this work, the results illustrate that each method has its advantage in the classification process. OBC slightly outperformed PBC in cropland extraction as the complementary texture, geometry, and shape information are helpful for cropland detecting. On the other hand, PBC reaches a higher accuracy in winter crops extraction, since all croplands have a similar shape, but winter crops can be easily distinguished from other crops with direct spectral information. Additionally, the statistical difference between the results of PBC and OBC is not particularly significant. In conclusion, small differences that are induced by several factors between the two methods can be noticed, yet both methods are equally useful for our classification.

#### *6.3. Potential of Sentinel-1 Data in Crops Phenology Monitoring*

Optical satellite data are well developed and have traditionally been used for different crop phenology monitoring by using vegetation indices time series [91–93], with NDVI being the most used vegetation index for crop phenology mapping [26,94,95]. However, Sakamoto et al. in 2005 [96] proposed rice phenology detection with time-series EVI data with fewer errors between the estimated phenological dates and the statistical data. Dong et al. in 2020 [10] have exploited the potentialities of a newly developed vegetation index, the Normalized Difference Phenology Index (NDPI), to provide more robust vegetation information and to reduce the adverse impacts of soil and snow cover for winter wheat mapping. In recent years, with the emergence of the new generation of high spatial and temporal resolution SAR data, a particular interest in radar data for crop phenology monitoring was found, especially for its "all weather" capacity, which leads directly to an increased role of SAR data in the field [97–99]. This study proved that Sentinel-1 C-band SAR-polarized backscatter time series data has great potential to monitor winter crop phenology in a coastal area that is marked by frequent precipitation, and some important considerations of the behavior of different polarizations in regard to different phenological stages are worth discussing.

Firstly, despite the σ◦ of both polarizations and their ratios being relatively similar, the curves of the VH and VV polarizations are sharper when they are compared to those of the ratio, due to the fact that the ratio is less sensitive to varying conditions like moisture and incidence angle variations. This can be explained by such effects having certain impacts in both polarizations, where the impacts would be reduced in the ratio [1]. As seen in Figure 10, the curves of the ratio VH/VV of winter wheat and winter barley are smoother in comparison with those of the single polarization and they are less impacted by continuous rainfalls or drought due to absence of precipitation.

Secondly, the timing of the phenological stages or growing periods of the crops based on the field knowledge are in agreement with the observations of the results. Based on prior knowledge, the sowing takes place between October and November, and winter barley is usually planted earlier than winter wheat is, and the germination occurs 3–4 weeks after sowing. This period can be confirmed by noting the large variations of the curves in the beginning which are induced by the interaction between the bare soil and the vegetation that is caused by the stem-ground double scattering [6], afterwards the germination is represented by the first peak of the curves, and this is especially well demonstrated in the polarization ratio for winter wheat and in the single polarizations for winter barley. After the overwintering period, the stem elongation, which begins in spring, can be recognized on the curves as a decreasing period that is caused by the attenuation of the signal when the vegetation cover occurs. Thereafter, the heading stage, where the crops attain their maximum height, occurs in early summer. This stage was confirmed with a minimum value on the curves at around 1 May, which can be well observed in the polarization ratio for winter wheat and in single polarization for winter barley. After heading, the volume backscattering was increased due to the increase of the plant biomass [1], and the winter barley is harvested in early summer, and the winter wheat is harvested in midsummer. This is illustrated by the curves in all polarizations decreasing as expected with large variations post-harvesting, depending on the soil conditions.

This leads to the conclusion that it is feasible to map crop phenology with high accuracy by using SAR data, which is highly sensitive to the phenology of agriculture crops. In addition, unlike many methods which exclusively use the single polarization or the ratio [31,100,101], our study shows that the combination of both is able to provide a better observation of agriculture phenology. Further studies can investigate the feasibility and performance of combining SAR and optical data for crop phenology monitoring.

#### *6.4. Limitations and Perspectives*

Some limitations were revealed during the process of result analyzing. Despite the fact that the hierarchical classification approach acquired a better accuracy (0.099 in kappa and 0.066 in OA), this classification approach required more complicated processing steps and was more costly when one is comparing it to the direct extraction, for a slight enhancement in the results. Moreover, the confusion between winter barley and grassland was nonnegligible. For increasing classification accuracy, extra data such as SAR or Sentinel-2 time series data can be applied. Additionally, even though the three main phenological statuses were successfully extracted from Sentinel-1 backscatter time series, more field research and expert knowledge is required for identifying some others important phenological stages (e.g., tillering, flowering, soft dough and hard dough).

#### **7. Conclusions**

Three issues surrounding winter crops have been studied and discussed in this paper. Firstly, two types of winter crops (winter wheat and winter barley) were mapped by using a Sentinel-2 high-resolution image, and two different classification approaches were performed. Both the hierarchical classification, which turns a complex classification problem into a series of smaller classifications, and the classical direct extraction, which extracts the winter crops directly from the original satellite image, were carried out. The hierarchical classification was composed of three smaller classifications: vegetation extraction from the original image, cropland extraction from the vegetation, and finally the winter crop extraction from other crops. Additionally, PBC and OBC were both performed in the last two steps and evaluated in order to keep the most accurate classification for further processing and analysis. Subsequently, crop phenology monitoring was performed, based on the results of the previous step by using Sentinel-1 C-band SAR time series data, and the three important phenological stages (germination, heading, and ripening (harvesting)) and the main growing periods were identified as well.

To respond to the objectives of the study and as the contribution of this paper, our results showed that winter crops in a fragmented landscape with heterogeneous land cover were successfully detected with high accuracy by using a Sentinel-2 image and the classification approaches that have been proposed. In particular, the hierarchical classification framework significantly improved the classification accuracy (0.1 and 0.06 increase in the kappa and OA, respectively, against classical direct extraction), moreover the classification of winter barley is also enhanced by reducing the confusion between winter barley and grassland with the hierarchical classification framework (0.094 increase in the F-score). Within the hierarchical classification, each classification method has its advantage; OBC slightly outperformed PBC in cropland extraction, yet PBC achieved higher accuracy in

winter crops mapping. Although some small differences can be noticed, however there is no significant statistical divergence between the two classification methods.

The results also lead to the conclusion that Sentinel-1 C-band SAR-polarized backscatter time series has great potential to monitor winter agriculture phenology in a coastal area with frequent rainfall. Three phenological stages and main growing periods could be easily identified from the time series in a single polarization or from the ratio, and furthermore the timing of the stages and the growing periods of the crops that are observed in the results highly conform to the field knowledge.

Although very satisfactory results were acquired in this study, some recommendations can be made for further studies, such as applying Sentinel-2 time series or SAR data for crop mapping in order to increase the classification accuracy, and in particular to reduce the confusion between winter barley and grasslands or other crop types. Exploring the potential of the combination of SAR and optical data for identifying more phenological stages and growth periods from the time series is advocated by us.

**Author Contributions:** G.X.: Conceptualization, methodology, software, investigation, resources, data curation, writing—original draft preparation; S.N.: Conceptualization, validation, formal analysis, writing—review and editing, visualization, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Fondation de France and the French Space Agency (CNES).

**Data Availability Statement:** Publicly available datasets were analyzed in this study. The data can be found here: theia.cnes.fr/ (accessed on 31 July 2022) for Sentinel-2 image, earthengine.google.com for Sentinel-1 image, www.geoportail.gouv.fr/ (accessed on 31 July 2022) for RPG 2018 data.

**Acknowledgments:** We would like to thank the French Space Agency (CNES) and the project CNES/Tosca for funding the publication.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

