A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images

Early-season and refined mapping of winter wheat based on phenology algorithms - a case of Shandong, China

Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of " different objects with the same spectra characteristics" between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding‒tillering, overwintering, reviving, jointing‒heading, and flowering‒maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries‒Matusita (J‒M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing‒heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing‒heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing‒heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation.

Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery

Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing.

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral-temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band's function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China

Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

Status of Phenological Research Using Sentinel-2 Data: A Review

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

NDVI imaging within space exploration plant growth modules - A case study from EDEN ISS Antarctica

The concept of using informative wavelength imagery to monitor plant health and ecosystem stability from space is derived from the deployment of Landsat and the development of the Normalized Difference Vegetative Index, or NDVI. NDVI presents the relative reflectance of the Near IR from plant leaves as a measure of relative plant health in terrestrial habitats and landscapes. However, the use of NDVI and NDVI-like imagery is rapidly evolving toward higher spatial resolution and more localized assessments of plant health, such as the use of drone imagery to monitor outdoor farms, and the use of mounted cameras within indoor growing facilities. With the advancement of plant growth systems in support of human space exploration, especially to the moon and Mars, remote assessment of plant health within exploration habitats becomes a critical element for development. This project examines the deployment of NDVI-like capabilities within a planetary analog greenhouse on the Antarctic ice shelf. The EDEN ISS Antarctica project provides a case study on the practical use of specific wavelength imagery to monitor plant health within space exploration environments. GoPro cameras, modified to dual bandpass capabilities, provided Single Image NDVI analyses for a year within the EDEN ISS Future Exploration Greenhouse at the Neumayer Station III in Antarctica. Images were acquired on site, analyzed remotely, and archived for the entire duration of the deployment through a combination of back-room science activities and operational communications with the Neumayer Station III. The results provide insights into the potential use of specific imaging wavelengths to enhance crop production in space exploration.

Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

The quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.

Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.

Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China

Currently, the main remote sensing-based crop mapping methods are based on spectral-temporal features. However, there has been a lack research on the selection of the multi-temporal images, and most of the methods are based on the use of all the available images during the cycle of crop growth. In this study, in order to explore the optimal temporal window for crop mapping with limited remote sensing data, we tested all possible combinations of temporal windows in an exhaustive manner, and made a comprehensive consideration of the spatial accuracy and statistical accuracy as evaluation indices. We collected all the available cloud-free Sentinel-2 multi-spectral images for the winter wheat and rapeseed growth periods in the study area in southern China, and used the random forest (RF) method as the classifier to identify the optimal temporal window. The spatial and statistical accuracies of all the results were assessed by using ground survey data and local agricultural census data. The optimal temporal window for the mapping of winter wheat and rapeseed in the study area was obtained by identifying the best-performing set of results. In addition, the variable importance (VI) index was used to evaluate the importance of the different bands for crop mapping. The results of the spatial accuracy, statistical accuracy, and the VI showed that the combinations of images from the later stages of crop growth were more suitable for crop mapping.

Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.