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\n
1. Introduction
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With the current challenge to improve the agricultural monitoring, forecast and planning, which are strategic for a country with continental dimensions and great diversity of land uses, the importance of the time series of digital images acquired by low-spatial-resolution satellites (such as the AVHRR/NOAA and MODIS/Terra) to monitor the expansion and production of agricultural crops (such as the sugarcane) in tropical regions (such as the southeastern region of Brazil) that have a huge amount of clouds during the growing season making the operational use of remote sensing data difficult is an essential highlight.
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The AVHRR/NOAA is a meteorological remote sensor that has been widely used also as source of spectral information for environmental and agricultural purposes. Since the sugarcane is cultivated on large and extensive fields, medium- and low-spatial-resolution satellites such as the AVHRR/NOAA can be used to properly monitor this agricultural crop. Sugarcane production has expanded in the last years in southeastern Brazil making this agricultural product strategic for its economy and environment since it is the main renewable source of energy used to replace fossil fuels and reduce the emissions of greenhouse gases that cause the global warming.
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Remote sensing images have been efficient to evaluate important characteristics of the sugarcane cultivation, providing relevant results to the debate of sustainable ethanol production from sugarcane [1]. The accuracy of the thematic mapping of sugarcane through satellite images was assessed [2], and a methodology for contributing in the automation of sugarcane mapping over large areas, with time series of remotely sensed imagery [3], was developed.
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In addition, researchers have conducted studies to assess social and economic impacts in sugarcane cultivation [4], as well as to predict its yield [5]. An alternative masking technique for satellite image time series, called yield-correlation masking, can be used for the development and implementation of regional crop yield forecasting models eliminating the need for a land cover map [6].
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In fact, this agricultural commodity has an increasing economic importance especially due to the increasing demand for ethanol (one of its derivative) used as renewable energy source to replace fossil fuels. Although there is a consensus about the benefits from a temperature increase for the sugarcane production, its expansion to the warmest regions can be negatively impacted whether the water deficit becomes more severe in consequence of climate changing scenarios in those areas. Thus, researchers have been dedicated to more detailed studies regarding expansion and productivity of sugarcane fields to find innovative and optimized methods in order to understand the impact of global warming in this crop production [7].
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Even being more accessible and available nowadays, many users still have difficulties to deal with satellite images due to different and more sophisticated demands as well as the fast-growing quantity and complexity of this kind of data [8]. In this context, knowledge discovery technologies are an important alternative to explore and find relevant information on this huge volume of data. Some initiatives involving data and image mining have been accomplished through different techniques with reasonable results [9–13].
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In this context, we focus on computational methods that allow analysis at regional scale with the purpose of improving agricultural crop monitoring and increasing the sustainable usage of the soil, taking into account that climate changes are in course. Even so, we show a clustering-based approach to analyze time series extracted from multi-temporal NDVI images and visualization. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis through multi-temporal satellite images of low spatial resolution.
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2. Material and methods
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2.1. Study area
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The study area is located in São Paulo, an important state of southeastern macro-region of Brazil (54°00′ to 43°30′W and 25°30′ to 19°30′S), which is responsible for 60% of the national production and 25% of the global production of sugarcane (Figure 1).
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Figure 1.
Location of study area, state of São Paulo in Brazil. The areas shown in gray are sugarcane production area.
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2.2. Proposed approach
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The knowledge discovery process comprehends three main steps: (1) data preparation of satellite image time series, (2) extraction of the NDVI profiles, and (3) clustering analysis. Figure 2 presents a flowchart of the proposed process to assess multi-temporal satellite images.
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Figure 2.
Flowchart with the main steps of proposed approach employed in this chapter.
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2.2.1. Satellite image time series (SITS)
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The database of multi-temporal NDVI/NOAA/AVHRR images used in this chapter is available at the Centre for Meteorological and Climatic Research Applied to Agriculture (Cepagri) at the University of Campinas (Unicamp), Brazil, having AVHRR/NOAA images recorded since April 1995 with approximately 6 terabytes of data. It was used in the analysis AVHRR/NOAA-16 and AVHRR/NOAA-17 images gathered from April 2001 to March 2010.
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It is necessary to preprocess the images, since the AVHRR/NOAA images often have geometric distortions caused by the Earth curvature and rotation, attitude errors and imprecise orbits of the satellite [14]. These distortions must be corrected specially for land applications that require a highly accurate geometric matching, with one pixel accuracy (1.1 km) in the Equidistant Cylindrical Projection. To perform accurate geometric, the maximum cross-correlation (MCC) method is applied. The MCC method compares a target image to a base image (one for each year season), geometrically accurate and cloudless [15]. The first step to be executed corresponds to the image georeferencing process, which is executed in batch mode by the NAVPRO system [16, 17] to accomplish the necessary tasks, such as:
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Conversion from a raw to an intermediary format
Radiometric calibration
Geometric correction
Identification of pixels classified as cloud
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To attenuate the effect of the atmosphere on the images, maximum-value composite (MVC) of NDVI images was generated. Following the recommendations [18], it is important to mask out the inappropriate pixels, such as cloud-contaminated pixels. The georeferencing module allows users to generate NDVI images for a specific region. As the volume of images is huge, it was used the SatImagExplorer system [19]. This system is interactive and allows the user to specify regions of interest (ROIs), using as input basis a satellite image time series. SatImagExplorer extrapolates the region indication for all images in the sequence, generating time series of the ROIs corresponding to that indicated for all available images. This tool allows the user to focus their analysis on strategic points of interest, as well as facilitates the analysis of a long series of data. Time series extracted from multi-temporal images using SatImagExplorer are one of the data to be mined by the clustering method.
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2.2.2. Clustering analysis
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The clustering task is defined as a process of grouping similar objects, following a given criterion [20]. In this step, NDVI time series are analyzed by clustering method implemented in the SatImagExplorer system. We have used the partition-based method named k-means.
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k-Means divide n objects from the input dataset into k partitions. Initially, the algorithm randomly determines k objects as initial centroids and associates each remaining object to the partition represented by the most similar (closest) centroid. In the end of each iteration, centroids that correspond to the average values of the cluster objects are recalculated to define the new order of n objects in the clusters during the next iteration. The k-means algorithm converges when there are no more changes in the clusters. Although simple and computationally efficient (O(nk)), as k-means considers average values, it is more sensitive to errors when noise and outliers appear in time series [21].
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The k-means method uses a distance function to perform similarity search operations to find the series most similar to a given time series that is being analyzed. A distance function or metric can be defined as a similarity measure between two data elements that are, in this case, two time series. The most widely used distance functions are those from the Minkowski family (or Lp norm). The Euclidean distance corresponds to L2, which is commonly used to calculate the distance between multidimensional arrays and vectors. The dynamic time warping (DTW) is a very efficient distance function to compare time series [22]. Its main objective is to keep close time series that have similar behavior but are delayed or distorted along the time axis. Thus, this technique presents a proper way of working to warping, because the comparisons between corresponding points are not rigid. DTW is a tool with two of the main issues raised by high-temporal-resolution satellite image time series, namely, the irregular sampling in the temporal dimension and the need for comparison of pairs of time series having different numbers of samples [23].
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We will show next the three clustering analyses performed:
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First: k-Means used with Euclidean distance, when we considered only monthly NDVI values. These values of sugarcane fields were extracted using geographical coordinates (latitude and longitude) provided by the Canasat/INPE Project (www.canasat.inpe.br). In this approach, each element of the dataset corresponds to one NDVI value, which refers to a month value in a given location (pixel), in order to obtain monthly analysis of the region of interest. Considering similarity among NDVI values, elements were assigned to different clusters. Five clusters were generated for each month of the crop season (2004–2005), being able to follow the development stage of the crop per month. For example, whether crop is in maturing phase, it has already been harvested, and there are not spectral mixing with other crops or vegetation;
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Second: k-Means used with DTW distance function, when we have generated series of NDVI values corresponding to one or more sugarcane crop series. The clustering was determined by five clusters for each crop season (2001–2010) for annual crop monitoring according to the type of planting in each crop season, for example, sugarcane ratoon, sugarcane expansion, sugarcane renewed, sugarcane under renewing and not defined [13, 24].
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Third: k-Means used with DTW distance function of three dimensional (multivariate) time series database, extracted from 324 monthly images of NDVI, albedo and surface temperature. Since DTW calculates the distance between pairs of data points using Euclidean distance, DTW method can be applied to multivariate time series. The whole dataset had 220,238 data series, being each observation a triplet of NDVI, albedo and surface temperature values of study area in a given month, with 108 values per time series [25].
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3. Results and discussion
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In this section, we present the results and discuss the three analyses performed in this chapter described above.
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3.1. k-Means used with Euclidean distance
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In this section we present how results of appliance of k-means clustering with Euclidean distance function over NDVI monthly values extracted from the study area can assist the monitoring of sugarcane fields.
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Months from December to May correspond to the period of maximum vegetative growth of sugarcane. In Figure 3J, L and B, pixels that appear in yellow and red colors correspond to the maximum NDVI values, being included in the clusters 3 and 4, respectively. On the other hand, months of August, September and October correspond to harvest season. In these months (Figure 3F), pixels in magenta and blue, with minimum NDVI values, correspond to clusters 0 and 1, respectively. Cluster 2 (green) corresponds to sugarcane intermediate stage of growth.
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Figure 3.
Monthly MVC NDVI images and clustering of NDVI (five clusters) of sugarcane planting area in the state of São Paulo for months from April 2004 to march 2005. (A) NDVI/NOAA 2004 April; (B) Clustering 2004 April; (C) NDVI/NOAA 2004 July; (D) Clustering 2004 July; (E) NDVI/NOAA 2004 September; (F) Clustering 2004 September; (G) NDVI/NOAA 2004 November; (H) Clustering 2004 November; (I) NDVI/NOAA 2005 January; (J) Clustering 2005 January; (K) NDVI/NOAA 2005 March; (L) Clustering 2005 March.
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These clusters can be validated in the MVC NDVI images. The black squares over the satellite images in the left correspond to the main sugarcane planting areas. Analyzing the MVC NDVI images in the northeastern region of São Paulo, the evolution of the sugarcane vegetative growth cycle can be seen (Figure 3). Planting begins in August represented in the images by pixels in shades of green and blue located in the northeastern region of the state. These colors represent low NDVI values (around 0.2) characterizing areas with exposed soil and sparse vegetation. Similar pattern also occurs in the months from September to November. From December, when sugarcane begins to grow up and acquire more biomass, these regions are shades of yellow, orange and red. Months from January to May show shades of dark red, when sugarcane reaches the highest stage of growth with maximum NDVI values (between 0.7 and 0.8). The dark areas in images represent pixels covered by clouds and water.
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There is no predominance of one or two clusters in all producing regions if we consider all months of the crop season. As we can observe, both plant and ratoon sugarcane are grown throughout the state, and the five clusters appear in all months. There is a higher percentage of pixels in the clusters with higher NDVI during some months. However, in other months, the largest number of pixels is included in clusters with lower NDVI (Figure 3).
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Figure 4 has the temporal profile of clusters showing dynamics of crop planting and harvesting throughout the growing season. Analyzing the temporal profile of Figure 4, we can observe that in months from December to May, the NDVI values are higher and represent a larger percentage of pixels for clusters 2, 3 and 4 (from 20 to 40% of the pixels). For the months from August to November, the NDVI values are lower, representing higher percentages for clusters 0 and 1 (around 30% of the pixels). Each month features a sugarcane planting area at a certain stage of growth, appearing in clusters 0 or 1 (harvested or bare soil) and in clusters 2, 3 and 4 (in growth or ready to be harvested) (Figure 3).
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Figure 4.
Temporal profile of five NDVI clusters of sugarcane fields for the months from April 2004 to March 2005.
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Although the k-means method is simpler and more widely used, their application in satellite image time series of low spatial resolution allows the regional study of crop, even with the difficulty in the analysis due to the possibility of spectral mixing in pixels.
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3.2. k-Means was used with DTW distance
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Results of the MVC NDVI image time series analysis about the period 2001–2010 for the state of São Paulo are presented hereafter. Maps and temporal profiles correspond to results of clusters (k-means with DTW distance function), pixels with NDVI values from year to year. In general, clusters that were identified as sugarcane may be (i) related to the type of planting carried out each year, for example, identifying areas of sugarcane ratoon (the sugarcane available for harvest after one or more cuts), sugarcane expansion (the sugarcane planted in new areas that will be harvested for the first time), sugarcane renewed (the year-and-half sugarcane plant that has undergone renovation during the previous crop year and will be available for harvest in the current crop year), sugarcane under renewing (the sugarcane area is not harvested due to renovation, not available for that specific crop year) and not defined area, and (ii) related to the quantity produced. Clusters, which were determined by clustering analysis, do not remain constant from year to year as the sugarcane planting is dynamic along the time series.
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Thus, applying the k-means clustering analysis, we can verify sugarcane planting type from the years analyzed. Cluster 4 (red) indicates the maximum NDVI values in the month, corresponding to areas with higher biomass. Cluster 0 (magenta) shows the lower NDVI values, corresponding to bare soil. The k-means method showed more homogeneous temporal profiles (Figure 5). Low peaks in NDVI profiles during the months of December and January (Figure 5) match NDVI values related to clouds, because this period of year is the rainy season in the state.
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Figure 5.
Temporal profiles of each cluster for each crop season in the period 2001–2002 to 2009–2010.
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Analyzing every year, we found that each cluster corresponds to different types of sugarcane planting (Table 1). For example, in crop season 2001–2002, 2003–2004, 2006–2007 and 2008–2009, cluster 2 (green; Figure 6A, C, F and H) corresponds to the type of sugarcane ratoon, and this cluster (29–47% of the pixels) is correlated (between R = 0.74 and R = 0.87) with the crop production (Figure 7). In crop seasons 2002–2003 and 2009–2010 (Figure 6B and I), sugarcane ratoon corresponds to cluster 1 (blue), with a correlation of R = 0.84 and R = 0.73 with the production and 36 and 33% of the sugarcane pixels (Figure 7). Crop season 2004–2005 (Figure 6D) corresponds to cluster 3 (yellow), with correlation index R = 0.81 and 32% of the sugarcane pixels (Figure 7). In most crop seasons, sugarcane ratoon is strongly correlated with the sugarcane production. Only in crop seasons 2005–2006 and 2007–2008 (Figure 6E and G), the sugarcane expansion is correlated with crop production.
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Cluster
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2001–2002
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2002–2003
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2003–2004
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2004–2005
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2005–2006
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2006–2007
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2007–2008
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2008–2009
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2009–2010
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0
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Expansion 9%
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Under renewing 18%
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Expansion 7%
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Expansion 4%
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Renovated 3%
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Not defined 14%
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Under renewing 12%
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Renewed 11%
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Renewed 21%
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1
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Renewed 17%
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Ratoon 36%
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Under renewing 27%
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Not defined 17%
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Ratoon 21%
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Expansion 20%
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Not defined 7%
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Under renewing 11%
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Ratoon 33%
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2
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Ratoon 29%
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Expansion 13%
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Ratoon 41%
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Under renewing 20%
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Under renewing 18%
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Ratoon 29%
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Renewed 21%
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Ratoon 47%
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Expansion 18%
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3
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Not defined 19%
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Renewed 15%
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Not defined 13%
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Ratoon 32%
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Expansion 35%
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Under renewing 21%
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Expansion 28%
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Expansion 22%
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Under renewing 19%
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4
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Under renewing 24%
\n
Not defined 15%
\n
Renewed 9%
\n
Renewed 24%
\n
Not defined 20%
\n
Renewed 14%
\n
Ratoon 29%
\n
Not defined 7%
\n
Not defined 6%
\n
\n\n
Table 1.
Type of sugarcane planting in each crop season and pixels number percentage for each cluster by k-means with DTW.
\n
Figure 6.
k-Means clustering with DTW distance function for each crop season in the period 2001–2002 to 2009–2010. (A) Clustering 2001–2002; (B) Clustering 2002–2003; (C) Clustering 2003–2004; (D) Clustering 2004–2005; (E) Clustering 2005–2006; (F) Clustering 2006–2007; (G) Clustering 2007–2008; (H) Clustering 2008–2009; (I) Clustering 2009–2010.
\n
Figure 7.
Graph of pixels’ number percentage for each cluster regarding each crop seasons in the period 2001–2002 to 2009–2010. Correlation values of the clusters with the sugarcane production.
\n
\n
\n
3.3. k-Means was used with DTW distance function of three dimensional (multivariate) time series database
\n
Dataset with more than 220,000 series in the state of São Paulo were clustered into five clusters (0–4) by k-means method with DTW distance function. Each cluster was formed according to the characteristics of NDVI, surface temperature and albedo extracted from AVHRR/NOAA images in the period 2001–2010. The identified areas were cluster 0 (magenta), which corresponds to water; cluster 1 (blue), which to the urban area and areas where the soil is exposed or have low vegetation and pasture; cluster 2 (green), which represents areas of agricultural crops; cluster 3 (yellow), which corresponds to sugarcane; and cluster 4 (red), which represents forest areas (Figure 8A and B).
\n
Figure 8.
Geographic spatial of 2001–2002 (A) and 2009–2010 (B) of clustering results; yellow represents sugarcane.
\n
NDVI was useful to separate vegetation areas from other targets, for example, forests present high values of NDVI during the whole season (have high concentration of vegetation and biomass), and these areas are normally shown by red-colored representative time series, in profile visualization (Figure 9A). On the other hand, albedo variable was useful to separate water areas from other targets, but was not enough to distinguish areas having different levels of vegetation cover (Figure 9B). The water represented by cluster 0 was well clustered, since the NDVI values and especially the albedo values were different from other clusters, as shown in the temporal profile of NDVI (Figure 9A) and albedo (Figure 9B). The albedo and NDVI values are lower (less than 0.1), since there is no presence of vegetation in the water or when there is minimal.
\n
Figure 9.
Profile visualization (2001–2010) of NDVI (A), albedo (B) and surface temperature (C) of clustering results.
\n
Clustering results for agricultural crops and grassland were less accurate, probably because different crops present similar NDVI values in some phenological phase during vegetative crop cycle, but are useful to separate agricultural from nonagricultural areas, such as water, urban areas and forest. Clustering of these areas was defined mainly by surface temperature, being higher for targets with lower canopy, such as urban areas and exposed soil, and lower for woodland (Figure 9A and C). For example, the forest areas represented by cluster 4, in Figure 8A and B, have high NDVI values (Figure 9A) and lower surface temperature values (Figure 9C), as they are very shady and dense vegetation coverage areas.
\n
However, sugarcane fields were well clustered over the crop seasons because the sugarcane has a typical behavior (long seasonal cycle) than other crops. In Figure 8A and B, it is possible to observe the dynamic of this agricultural crop, represented by cluster 3 (yellow), throughout the decade in which in the crop years 2001–2002 the acreage was low, with higher production, and planted in the northeast area of the state, and in the end of the crop years 2009–2010, there was a significant increase in the planted area toward the western of the state. This technique of clustering in three dimensional (multivariate) time series database was efficient to perform temporal analysis of land use, indicating that this methodology can be used to identify and analyze the dynamics of land use and cover.
\n
\n
\n
\n
4. Conclusions
\n
This chapter presented a new approach to boost the agricultural monitoring including the expansion of crops to different regions, through techniques of time series mining. We used clustering analysis associated with the Euclidean and the DTW distance functions. We demonstrated that it is possible to take advantage of off-the-shelf computational methods to support agricultural monitoring as well as to automatically determine sugarcane fields’ expansion that is a valuable contribution of this work.
\n
Moreover, we also showed the potential use of time series of satellite images with low spatial resolution in agricultural monitoring although spectral mixtures can occur. The main advantage of this approach is the high temporal resolution, low cost and global coverage of the remote sensing system used (AVHRR/NOAA). The performance analysis of a simple clustering technique based on a time series of satellite images is in providing a further step in the researches on the use of renewable energy sources, such as the sugarcane ethanol. The impact of such approach becomes even stronger, and it increases the need for researching on new ways to reduce greenhouse gas emissions, mainly in the trail of the recent occurrences of extreme events in different locations of the planet.
\n
\n
Acknowledgments
\n
The authors thank FAPESP/AlcScens and CNPq for funding and Cepagri/Unicamp for the database of remote sensing imagery.
\n
\n',keywords:"time series, AVHRR/NOAA, NDVI, k-means, sugarcane",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/57354.pdf",chapterXML:"https://mts.intechopen.com/source/xml/57354.xml",downloadPdfUrl:"/chapter/pdf-download/57354",previewPdfUrl:"/chapter/pdf-preview/57354",totalDownloads:838,totalViews:246,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,dateSubmitted:"April 24th 2017",dateReviewed:"September 21st 2017",datePrePublished:null,datePublished:"January 24th 2018",dateFinished:null,readingETA:"0",abstract:"The remote sensing images are more accessible nowadays and there are proper technologies to receive, distribute, manipulate and process long satellite image time series that can be used to improve traditional methods for harvest monitoring and forecasting. The potential of the satellite multi-temporal images to support research of agricultural monitoring has increased according to improvements in technological development, especially in analysis of large volume of data available for knowledge discovery. In Brazil, sugarcane is cultivated on extensive fields and is the main agriculture crop used to produce ethanol. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis with multi-temporal satellite images having low spatial resolution. A large database of this kind of image and specific software were used to perform the image pre-processing phase, extract time series, apply clustering method and enable the data visualization on several steps during the whole analysis process. According to the analysis done, our methodology allows to identify land areas with similar development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution can indeed be satisfactorily used in agricultural crop monitoring in regional scale.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/57354",risUrl:"/chapter/ris/57354",book:{slug:"time-series-analysis-and-applications"},signatures:"Renata Ribeiro do Valle Gonçalves, Jurandir Zullo Junior, Bruno\nFerraz do Amaral, Elaine Parros Machado Sousa and Luciana Alvim\nSantos Romani",authors:[{id:"209756",title:"Ph.D.",name:"Renata",middleName:null,surname:"Ribeiro Do Valle Goncalves",fullName:"Renata Ribeiro Do Valle Goncalves",slug:"renata-ribeiro-do-valle-goncalves",email:"renata@cpa.unicamp.br",position:null,institution:{name:"State University of Campinas",institutionURL:null,country:{name:"Brazil"}}},{id:"209881",title:"Dr.",name:"Jurandir",middleName:null,surname:"Zullo Junior",fullName:"Jurandir Zullo Junior",slug:"jurandir-zullo-junior",email:"jurandir@cpa.unicamp.br",position:null,institution:null},{id:"209882",title:"MSc.",name:"Bruno",middleName:null,surname:"Ferraz Do Amaral",fullName:"Bruno Ferraz Do Amaral",slug:"bruno-ferraz-do-amaral",email:"lpsbruno@yahoo.com.br",position:null,institution:null},{id:"209883",title:"Dr.",name:"Elaine",middleName:null,surname:"Parros Machado Sousa",fullName:"Elaine Parros Machado Sousa",slug:"elaine-parros-machado-sousa",email:"eparros@gmail.com",position:null,institution:null},{id:"209884",title:"Dr.",name:"Luciana",middleName:null,surname:"Alvim Santos Romani",fullName:"Luciana Alvim Santos Romani",slug:"luciana-alvim-santos-romani",email:"luciana.romani@embrapa.br",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Material and methods",level:"1"},{id:"sec_2_2",title:"2.1. Study area",level:"2"},{id:"sec_3_2",title:"2.2. Proposed approach",level:"2"},{id:"sec_3_3",title:"2.2.1. Satellite image time series (SITS)",level:"3"},{id:"sec_4_3",title:"2.2.2. Clustering analysis",level:"3"},{id:"sec_7",title:"3. Results and discussion",level:"1"},{id:"sec_7_2",title:"3.1. k-Means used with Euclidean distance",level:"2"},{id:"sec_8_2",title:"3.2. k-Means was used with DTW distance",level:"2"},{id:"sec_9_2",title:"3.3. k-Means was used with DTW distance function of three dimensional (multivariate) time series database",level:"2"},{id:"sec_11",title:"4. Conclusions",level:"1"},{id:"sec_12",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Rudorff BFT, Aguiar DA, Silva WF, Sugawara LM, Adami M, Moreira MA. Studies on the rapid expansion of sugarcane for ethanol production in São Paulo State (Brazil) using Landsat data. Remote sensing. 2010;2:1057-1076. DOI: 10.3390/rs2041057\n'},{id:"B2",body:'Adami M, Mello MP, Aguiar DA, Rudorff BFT, Souza AF. A web platform development to perform thematic accuracy assessment of sugarcane mapping in South-Central Brazil. Remote Sensing. 2012;4:3201-3214. DOI: 10.3390/rs4103201\n'},{id:"B3",body:'Vieira MA, Formaggio AR, Rennó CD, Atzberger C, Aguiar AA, Mello MP. Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas. Remote Sensing of Environment. 2012;123:553-562. DOI: 10.1016/j.rse.2012.04.011\n'},{id:"B4",body:'Martinelli LA, Filoso S. Expansion of sugarcane ethanol production in brazil: Environmental and social challenges. Ecological Applications. 2008;18:885-898. DOI: 10.1890/07-1813.1\n'},{id:"B5",body:'Nascimento CR, Gonçalves RRV, Zullo J Jr, Romani LAS. Estimation of sugar cane productivity using a time series of AVHRR/NOAA-17 images and a phenology-spectral model. In: MultiTemp 2009 – The Fifth International Workshop on the Analysis of Multi-Temporal Remote Sensing Images; Connecticut, Groton. 2009. p. 365-372\n'},{id:"B6",body:'Kastens JH, Kastens TL, Kastens DLA, Price KP, Martinko EA, Lee RY. Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sensing of Environment. 2005;99:341-356. DOI: 10.1016/j.rse.2005.09.010\n'},{id:"B7",body:'Loarie SR, Lobell DB, Asner GP, Field CB. Direct impacts on local climate of sugar-cane expansion in Brazil. Nature Climate Change Letter. Vol 1, pp. 105-109. 2011. DOI: 10.1038/nclimate1067\n'},{id:"B8",body:'Datcu M, Pelizzari A, Daschiel H, Quartulli M, Seidel K. Advanced value adding to metric resolution SAR data: Information mining. In 4th European Conference on Synthetic Aperture Radar (EUSAR 2002), Cologne, Germany; 2002. p. 1-14\n'},{id:"B9",body:'Datcu M, Daschiel H, Pelizzari A, Quartulli M, Galoppo A, Colapicchioni A, Pastori M, Seidel K, Marchetti PG, D’Elia S. Information mining in remote sensing image archives: System concepts. IEEE Transactions on Geoscience and Remote Sensing. 2003;41:2923-2936. DOI: 10.1109/TGRS.2003.817197\n'},{id:"B10",body:'Li J, Narayanan RM. Integrated spectral and spatial information mining in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing. 2004;42:673-685. DOI: 10.1109/TGRS.2004.824221\n'},{id:"B11",body:'Daschiel H, Datcu M. Information mining in remote sensing image archives: System evaluation. IEEE Transactions on Geoscience and Remote Sensing. 2005;43:188-199. DOI: 10.1109/TGRS.2004.838374\n'},{id:"B12",body:'Romani LAS, Avila AMH, Zullo J Jr, Chbeir R, Traina C Jr, Traina AJM. Clearminer: A new algorithm for mining association patterns on heterogeneous time series from climate data. In: Association for Computing Machinery (ACM) Symposium on Applied Computing – SAC’ 2010; Sierre, Switzerland. 2010. p. 900-905. DOI: 10.1145/1774088.1774275\n'},{id:"B13",body:'Romani LAS, Gonçalves RRV, Amaral BF, Chino DYT, Zullo J Jr, Traina C Jr, Sousa EPM, Traina AJM. Clustering analysis applied to NDVI/NOAA multitemporal images to improve the monitoring process of sugarcane crops. In: MultiTemp 2011 – The Sixth International Workshop on the Analysis of Multi-Temporal Remote Sensing Images; Trento, Italy. 2011. p. 33-36. DOI: 10.1109/Multi-Temp.2011.6005040\n'},{id:"B14",body:'Rosborough DGEWJ, Baldwin GW. Precise AVHRR image navigation. IEEE Transactions on Geoscience and Remote Sensing. 1994;32:644-657. DOI: 10.1109/36.297982\n'},{id:"B15",body:'Emery W, Baldwin DG, Matthews D. Maximum cross correlation automatic satellite image navigation and attitude corrections for open ocean image navigation. IEEE Transactions on Geoscience and Remote Sensing. 2003;41:33-42. DOI: 10.1109/TGRS.2002.808061\n'},{id:"B16",body:'Emery WJ, Brown J, Novak ZP. AVHRR image navigation: Summary and review. 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Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, Wiley Online Library; 1990. p. 342. DOI: 10.1002/9780470316801\n'},{id:"B22",body:'Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In: Proceedings of the Knowledge Discovery in Databases – KDD Workshop (KDD’ 1994) Seattle, Washington, USA. 1994. p. 359-370\n'},{id:"B23",body:'Petitjean F, Inglada J, Gançarski P. Satellite image time series analysis under time warping. IEEE Transactions on Geoscience and Remote Sensing. 2012;50:3081-3095. DOI: 10.1109/TGRS.2011.2179050\n'},{id:"B24",body:'Gonçalves RRV, Zullo J Jr, Romani LAS, Amaral BF, Sousa EPM. Agricultural monitoring using clustering techniques on satellite image time series of low spatial resolution. In: MultiTemp 2017 – The Ninth International Workshop on the Analysis of Multi-Temporal Remote Sensing Images; Bruges, Belgium. 2017. p. 1-4\n'},{id:"B25",body:'Gonçalves RRV, Zullo J Jr, Amaral BF, Coltri PP, Sousa EPM, Romani LAS. Land use temporal analysis through clustering techniques on satellite image time series. In: IGARSS 2014 – IEEE International Geoscience and Remote Sensing Symposium; Quebec, Canada. 2014. p. 2173-2176.\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Renata Ribeiro do Valle Gonçalves",address:"renata@cpa.unicamp.br",affiliation:'
Center of Meteorological and Climate Researches Applied to Agriculture (Cepagri), University of Campinas (Unicamp), Cidade Universitária Zeferino Vaz, Brazil
Center of Meteorological and Climate Researches Applied to Agriculture (Cepagri), University of Campinas (Unicamp), Cidade Universitária Zeferino Vaz, Brazil
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Liebana-Cabanillas",slug:"francisco-liebana-cabanillas"}]},{id:"56113",title:"Smartphone: The Ultimate IoT and IoE Device",slug:"smartphone-the-ultimate-iot-and-ioe-device",signatures:"Mehdia Ajana El Khaddar and Mohammed Boulmalf",authors:[{id:"26677",title:"Dr.",name:"Mehdia",middleName:null,surname:"Ajana El Khaddar",fullName:"Mehdia Ajana El Khaddar",slug:"mehdia-ajana-el-khaddar"},{id:"209424",title:"Dr.",name:"Mohammed",middleName:null,surname:"Boulmalf",fullName:"Mohammed Boulmalf",slug:"mohammed-boulmalf"}]},{id:"56310",title:"Positioning Techniques with Smartphone Technology: Performances and Methodologies in Outdoor and Indoor Scenarios",slug:"positioning-techniques-with-smartphone-technology-performances-and-methodologies-in-outdoor-and-indo",signatures:"Paolo Dabove, Vincenzo Di Pietra and Andrea Maria Lingua",authors:[{id:"86028",title:"Dr.",name:"Paolo",middleName:null,surname:"Dabove",fullName:"Paolo 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Escamilla-Ambrosio and\nMoisés Salinas-Rosales",authors:[{id:"203558",title:"Dr.",name:"Abraham",middleName:null,surname:"Rodríguez-Mota",fullName:"Abraham Rodríguez-Mota",slug:"abraham-rodriguez-mota"},{id:"204121",title:"Dr.",name:"Ponciano",middleName:"Jorge",surname:"Escamilla-Ambrosio",fullName:"Ponciano Escamilla-Ambrosio",slug:"ponciano-escamilla-ambrosio"},{id:"204123",title:"Dr.",name:"Moisés",middleName:null,surname:"Salinas Rosales",fullName:"Moisés Salinas Rosales",slug:"moises-salinas-rosales"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"68608",title:"Electro-Optical Manipulation Based on Dielectric Nanoparticles",doi:"10.5772/intechopen.88616",slug:"electro-optical-manipulation-based-on-dielectric-nanoparticles",body:'
1. Introduction
Dynamically controlling the optical responses from plasmonic or Mie resonators is significant for future optical signal processing [1, 2]. Among different active tuning methods, electrical tuning is one of the most effective one owing to high switching speed and large tuning ranges [3, 4, 5]. Recently, electrical tuning on metamaterials based on plasmonic nanostructures has been reported, and the control mechanisms rely on semiconductor layers [6, 7, 8], graphene [9, 10, 11, 12, 13], or electromechanical deformation [14, 15]. Nevertheless, there are few works about the optoelectronic modulation on nanoscale devices up to now. Furthermore, how to realize the electrical tuning on single nanoparticles is still a challenge.
Combining optical nanoantennas with atomically thin WS2 may be another method to realize dynamic optical responses. Atomically thin WS2 (monolayer or bilayer) exhibits intriguing electrical and optical properties [16, 17, 18]. Monolayer WS2 shows strong excitonic emission peak at visible wavelengths; however, ultrathin thickness hinders further enhancement of excitonic emission. Near-field enhancements at excitation wavelengths can enhance the light absorbance, while that at emission wavelengths would boost the emission rate, so those two factors both enhance the excitonic emission from WS2. Based on it, many efforts have been made to realize field enhancements via plasmonic nanostructures and photonic crystals at both excitation and emission wavelengths.
Silicon nanoantennas as a typical dielectric Mie resonator have wide application prospect in building metasurfaces [19, 20, 21], nonlinear optics [22], and biosensing [23]. They may be better choice than plasmonic structures and photonic crystals in building electrically controlled devices. The Mie resonances in silicon nanoantennas can be modulated through changing the sizes [24, 25] or crystallographic phases [20] passively. However, how to realize active control based on the Mie resonances in silicon nanocavities is still a challenge. Besides changing the optical properties of Mie resonators intrinsically, active tuning may also be realized via coupling with 2D materials. Neshev et al. have theoretically demonstrated the PL modulation of 2D materials based on the directional emission caused by Mie resonators [26]. Recently, the first experimental work has been done, in which a forward-to-backward emission ratio of 20 was realized because of the interaction between MoS2 monolayer and Mie resonators [27]. However, both of them were analyzed on passive control.
2. Electrically controlled optical responses of silicon-based nanoantennas
In this chapter, we will discuss the applications of silicon-based Mie resonators into electro-optical modulation. This chapter can be divided into two parts:
First, we demonstrated the electrically tunable scattering of a single silicon nanoparticle at visible wavelengths. To build the nanoantennas, gold interdigital electrodes with separation distances between 100 and 200 nm were fabricated using photolithography and focused ion beam (FIB) milling. After trapping silicon nanoparticles with different sizes between adjacent two electrodes, the scattering spectra under different voltages can be measured. Interestingly, the scattering experiences blueshift and obvious intensity attenuation when increasing the applied voltages from 0 to 1.5 V. In theory, MIS (metal-insulator-semiconductor) junctions can be formed at Au-SiO2-Si interfaces [28]. Once the bias voltage increases, the inversion and accumulation effect would produce much more free carriers at interfaces [29, 30, 31] and then change the permittivity based on Drude model [32, 33]. The proposed hybrid nanoantennas represent a new method to build optoelectronic devices based on Mie resonators.
Second, we combined silicon nanostripes, a typical Mie resonator, with WS2 to realize active PL manipulation. In the proposed electro-optical modulator, suspended monolayer and bilayer WS2 are covered on a Si nanostripe. The Si nanostripe not only acts as a nanoscale gate electrode but also a Mie resonator. For both monolayer and bilayer WS2, the PL intensities on the nanostripes are much stronger than those of the suspended one. After applying gate voltages, both the electrostatic doping and strain come into effect. This new tuning mechanism leads to abnormal control of exciton emission from WS2, which is clearly different from that in previous works [34, 35, 36]. Considerable PL tuning can also be observed in bilayer WS2 gated by Si nanostripes. Based on the modulation capability, we believe the proposed electro-optical modulator will bring new possibilities for future nanophotonic devices.
3. Hybrid nanoantennas based on silicon nanoparticles and nanostripes
3.1 Electrically driven scattering in a hybrid dielectric-plasmonic nanoantenna
In order to build an electrically controlled silicon nanoantenna, the biggest issue is how to apply voltage on a single nanoparticle and collect the electrically modulated signals with low noise. The design of electrically tunable silicon nanoantenna is shown in Figure 1a. First, maskless laser lithography and electron-beam deposition were used to fabricate Au electrodes with the thickness of 100 nm on the Si/SiO2 substrate, and the thickness of SiO2 layer is 300 nm. In our design, several large Au electrodes (200 × 400 μm) are deposited with a row of holes in the center. Second, the connected area in the center was nano-patterned using FIB milling to form nanoscale interdigital electrode structure. The separation distance between adjacent nano-electrodes is adjusted from 100 to 200 nm to match the size distributions of silicon nanoparticles, since the silicon nanoparticles fabricated through femtosecond laser ablation in liquid (fs-LAL) have a wide size distribution. Finally, during the evaporation process, the silicon nanoparticles in colloid have a certain probability to be trapped in the gaps.
Figure 1.
Optical properties of the silicon nanoantenna. (a) A schematic diagram explains the fabrication of Au electrode-loaded Si nanoparticles. (b) The schematic shows different plasmon resonant modes of two types Au electrodes. (c) The scanning electron microscope (SEM) image of Au interdigital electrodes with a silicon NP trapped among them. Inset is the high magnification SEM image with a scale bar of 100 nm. (d) The dark-field scattering image of the sample in (c). The white circle reveals the location of Si nanoparticles. (e, f) Measured scattering spectrum of a 180 nm Si nanoparticle (e) and the corresponding simulated scattering spectrum (f). (g) The electric and magnetic field distributions at 675 nm, which represent the hybrid modes coupling between localized plasmon and magnetic dipole.
Before studying the optical properties of Si-Au hybrid nanoantennas, we should study the Au electrode platform first. For the fabricated Au grating, due to the incident light that comes from a dark-field circle in the objective, wave vectors with different directions at x-y plane cannot launch surface plasmon polariton efficiently. In addition, the plasmon energy mainly decays nonradiatively through near-field coupling between adjacent Au electrodes, so Au gratings cannot show bright scattering as shown in Figure 1b. However, if only two electrodes left (see Figure 1b), localized surface plasmon can be formed between two Au electrodes. Strong scattering light can be generated from the plasmonic field enhancement in the gap. Therefore, we use Au grating in experiment whose scattering can be ignored compared with Si nanoparticles. Typical Au electrode-loaded Si nanoparticles are shown in Figure 1c and d, where a bright dot can be seen in dark-field image which means the scattering from the Si nanoantenna. In spectral measurement, through moving the scattering spot into the center of slit and only extracting the data from the location of nanoparticle, the exact scattering from the Si nanoparticle can be obtained.
For isolated Si nanoparticles, the resonant modes depend on particle sizes and particle numbers according to Mie theory. While for Au electrode-loaded Si nanoparticles, the mode coupling between nanoparticles and Au electrodes also needs to be considered. The hybrid nanoantennas may exhibit different scattering spectra at visible wavelengths. Therefore, it is necessary to study the scattering spectra without applied voltage first. Although self-assembled process is random, desirable and representative nanoparticles can be found through matching and positioning. Figure 1e shows 180 nm Si nanoparticles between two Au electrodes with a spacing slightly less than 180 nm. The measured scattering spectra exhibits a single broad peak around λ=650nm. As shown in Figure 1f, the simulated spectrum is very similar to experimental spectrum. Corresponding electric and magnetic field distributions in Figure 1g demonstrate the existence of circular magnetic field distributions and strong electric field enhancement at interfaces, which means the scattering peak is generated from the interaction between the Mie-type magnetic dipole mode in Si nanoparticles and the localized surface plasmon resonances (LSPR) at Au-Si interfaces.
The electrical properties of the Si-Au hybrid devices were measured using a semiconductor parameter analyzer. The measured I-V curve is shown in Figure 2a, and we can conclude that the Si-Au interfaces can be regarded as Schottky junctions. From 0 to 1.5 V, the current increases nonlinearly with the voltage. For the fabricated Si nanoparticles, thin oxide (1–2 nm) shells will be formed inevitably in the air. Therefore, the interfaces are MIS junctions whose current is generated through tunnel effect and plasmon hot electron injection. For MIS junctions, the band bends upward at interfaces when no voltage applies as the schematic diagram shown in Figure 2b. Depletion region forms at the interfaces and free carriers move away from interfaces based on the band bending [29]. With applied bias, surface potential at two interfaces increases. The carrier concentration at the MIS junction under lower potential was greatly increased because the downward energy band realizes the accumulation of electrons. The other MIS junction under higher potential could form an inversion layer if the applied voltage is high enough. When the intrinsic energy level crosses the Fermi level [29, 30, 31], the hole density would greatly increase under the inversion state. The charge densities at surface at different applied voltages can be estimated by the following equations [29]
Figure 2.
Analysis on the voltage-induced carrier injection. (a) The I-V curve of a fabricated Si-Au hybrid structure. (b) Schemes for the band bending and carrier distribution with and without applied voltage. (c) The Raman spectrum of a loaded nanoparticle before and after applied voltage (1.5 V).
Qinv=−CoxVG−VT∝expβΨS2E1
Qacc=CoxVG−VFB∝exp−βΨS2E2
where Qacc and Qinv are the carrier densities at accumulation and inversion regions. Cox is the capacitance of thin insulator layer. VG is the applied bias. VFB and VT are accumulation and inversion effects related to voltages. β=q/kBT is a constant. ΨS is the surface potential at interfaces. Based on the electric field we applied and above calculation, the electron or hole concentrations can be increased by more than three orders of magnitude in accumulation or inversion regions, respectively [29]. The Raman signals under different voltages are presented in Figure 2c. With 1.5 V applied bias, the resonant peak of silicon just red shifts slightly, and this weak shift means the temperature variation is less than 100 K [37]. Therefore, we can exclude the influence of thermal effect on the refractive index since the refractive index of silicon only increases 3.85×10−4 per degree [38].
To examine whether the mechanism discussed above could affect the optical properties significantly, the scattering spectra of the typical silicon nanoantenna with applied bias were presented in Figure 3a. To ensure stability, all scattering data were collected in 1 min during the increase of voltage from 0 to 1.5 V. Because the interfaces of the fabricated hybrid nanoantenna are symmetric, we only need to collect the scattering spectra under forward bias which is enough to embody the properties of hybrid nanoantennas. For a typical hybrid nanoantenna as shown in Figure 3a with a 180 nm Si nanoparticle, we can observe the suppression of hybrid plasmon-Mie resonant peaks when increasing the voltages. The magnetic dipole peak was dominated when no voltage applies. However, when applied voltage reaches 1.5 V, the electric dipole peak at shorter wavelength becomes the more prominent one.
Figure 3.
Electrically controlled scattering. (a) Scattering spectra of the 180 nm Si nanoparticles when applied voltages equal to 0, 0.3, 0.6, 0.9, 1.2, and 1.5 V. (b) The variation trend of the real part of permittivity at Au-SiO2-Si interfaces when increasing the carrier concentrations. (c) The calculated scattering spectra of the 180 nm Si nanoparticle under different carrier concentrations at interfaces.
As discussed above, different applied voltages result in different free carrier concentrations of Si nanoparticles. Further, we should clarify how carrier injection influences the dielectric function of silicon. The modulation mechanism is based on free carrier-induced refractive index change. Although electric field cannot change the refractive index of bulk silicon or whole silicon nanostructures significantly as previous works reported [39], obvious refractive index modification can be realized at accumulation and inversion interfaces. From the field profiles, one can understand the refractive index change on surface is enough to change optical responses because field enhancements and radiative decays mainly come from interfaces. How free carriers contribute to the refractive index change at interfaces can be described by the Drude model
Δε\'=−ε∞ωp2τ21+ω2τ2E3
Δε\'\'=ε∞ωp2τω1+ω2τ2E4
where ωp is the plasma frequency which is defined as ωp=Ne2/mCε∞ε0. τ is the damping time equals to μmC/e where e is the charge of an electron. mC is the effective mass, ε0 is the vacuum permittivity, and ε∞ is the permittivity of silicon at visible band. N is the concentration of free carrier which determines the changes of permittivity. Using the Drude model discussed above, we can calculate how free carriers influence the dielectric function of silicon as shown in Figure 3b. Putting different carrier concentrations (1017to2.0×1020cm−3) into Drude model, one can see the real part of permittivity decreases gradually especially at longer wavelengths from 600 to 900 nm. Because the accumulation and inversion layers are less than 5 nm at interfaces, we only used the free carrier-induced dielectric functions at interfaces for the numerical simulation. As shown in Figure 3c, the simulated scattering spectra under different carrier concentrations are very similar to the corresponding measured spectra under different applied bias. For the 180 nm Si nanoparticle (see Figure 3c), the hybrid resonant peak experiences blueshift and intensity attenuation when increasing the carrier concentrations in sequence. The attenuation trend of resonant peaks is very similar to the experimental spectra in Figure 3a. Our proposed structures provide an opportunity to collect the electrically controlled scattering signals on single-particle level.
3.2 PL enhancements enabled by silicon nanostripes
Owing to the unique properties of dielectric Mie resonators, researchers are trying to use Mie resonators as an important building block to form new-generation electro-optical modulators. One strategy is to combine Mie resonators with 2D materials as the schematic shown in Figure 4a. WS2 monolayers and bilayers were obtained by mechanical exfoliation and all-dry transfer technique. WS2 layers and Si nanostripes were aligned and contacted under an optical microscope. Si nanostripes were fabricated by FIB milling onto SiO2 coated silicon-on-insulator (SOI) wafers. Gold electrodes were patterned and deposited on WS2 and bare silicon to build source, drain, and gate. A simple cross-section schematic of the substrate in Figure 4a shows that there is an insulator layer between the Si substrate and the top Si film. Therefore, the scattering from Si nanostripes is not only pure Mie effect but the Mie resonance combined with the Fabry-Perot effect. In our case, the thickness of the insulator layer (h) is 375 nm. The dark-field scattering spectrum and the corresponding optical image in Figure 1b indicate that Si nanostripes have a broadband resonant peak. Dominant peaks are located around 700 nm, and two small peaks can also be distinguished below λ=600nm. Figure 4c is a typical WS2-Si nanostripe hybrid nanostructure. Corresponding SEM images are shown in Figure 4d. From SEM images, we can see the width of Si nanostripe is around 650 nm surrounded by two 10×30μm etched regions. Wrinkles and missing regions are inevitably formed during the transfer and lift-off processes. Fortunately, these regions can be avoided in the following measurements.
Figure 4.
Experimental design for the WS2-Si nanostripe hybrid structure. (a) Schematic illustration of the electrically controlled device and the cross section of the SiO2-coated SOI substrate. (b) Dark-field backward scattering of the fabricated Si nanostripe. Inset: The dark-field optical image. (c) The bright-field optical image of a typical device. Monolayer and bilayer regions are labeled as 1L and 2L. (d) The corresponding SEM image.
Figure 5a indicates different locations we measured on bilayer and monolayer WS2. From the PL emission spectra of monolayer WS2 as shown in Figure 5b, one can conclude Si nanostripes can increase the PL intensities but less than threefold compared with that in the suspended region. It should be noticed that the unpatterned region can also enhance the PL intensity and the enhancement performance is better than that on the Si nanostripe. For bilayer WS2, the PL enhancement is much more significant as shown in Figure 5c. The bilayer WS2 on the Si nanostripe possesses nearly 10 times larger PL intensities than the suspended area. Interestingly, this PL enhancement was only observed at λ=625nm where direct bandgap transition happens. However, for unpatterned area, PL intensities at both direct and indirect transition wavelengths (λ=635&750nm) are enhanced. The performances of PL enhancement for direct transition are comparable for unpatterned area and the Si nanostripe. Moreover, unpatterned area can strengthen the indirect transition more than 10 times. Besides the differences of PL intensities, the line shapes also change in Figure 5b and c which reveals the conversion between exciton (A) and trion (A−). The locations of exciton and trion emission are labeled in Figure 5b and c. Further peak fitting demonstrates the decreased trend of trion emission from suspended area and nanostripes to the unpatterned region. Substrate effect and the optical resonant modes may be two reasons that lead to the change of line shapes. The Fabry-Perot mode in the unpatterned substrate and Fabry-Perot mode assisted by Mie resonances in fabricated nanostripes can both influence the PL line shapes.
Figure 5.
PL enhancements in monolayer and bilayer WS2. (a) The optical image showing the detection points on monolayer (M) and bilayer (B) WS2. (b, c) PL spectra of different positions marked in (a). The locations of exciton (A) and trion (A−) states are labeled, along with the range of Mie resonant modes (marked by red stripe).
3.3 PL manipulation of monolayer and bilayer WS2 gated by silicon nanostripes
Realizing the electrical tuning is crucial for further application. To examine the electrical tuning performance, first, PL intensities of monolayer WS2 under different voltages were measured as shown in Figure 6a. When applying negative gate voltages from 0 to −10 V, the maximum PL intensity increases by 50%. On the contrary, the intensity of the PL peak decreases to half under positive gate voltages from 0 to 10 V. Compared with the normal WS2 monolayer gated by the flat gate [34, 35, 36], the PL enhancement effect is weaker, while the reduction effect is more obvious. This phenomenon indicates that the tuning effect is not pure electrostatic doping and there should be a new mechanism. PL changes of the bilayer WS2 under different voltages were also measured as shown in Figure 6b and c. Unexpectedly, the variation trends under positive and negative gate voltages are almost the same. The maximum PL intensity doubled when increasing the gate voltage from 0 to 10 V. Several groups have studied the electrically controlled PL of bilayer WS2, while no obvious effect has been observed [34, 35, 36]. Therefore, the obvious PL enhancement we observed may not arise from pure electrostatic doping. The gate voltage dependent intensity of excitonic peak is plotted in Figure 6d. The PL intensity of monolayer WS2 increases linearly with gate voltage, while the PL intensity of bilayer WS2 and the gate voltage follow a parabolic relationship.
Figure 6.
Electrically controlled PL. (a) PL spectra of the monolayer WS2 on the Si nanostripe at different gate voltages. (b, c) PL spectra of the bilayer WS2 on the Si nanostripe at different gate voltages. (d) The gate voltage dependence of PL intensities.
How to explain the abnormal PL manipulation in the proposed hybrid nanoantennas? The schematic shown in Figure 7a may give a better understanding. When placing WS2 flakes on the Si nanostripe, there are three regions that experience different forces. The first part is WS2 on the Si nanostripe, the second part is WS2 near the edges of Si nanostripes, and the third part is fully suspended WS2. If the applied voltage is high enough, a great number of holes and electrons will be produced at WS2 layers and bottom Si nanostructures, respectively. Besides the electrostatic doping, the static electric field can also produce attractive forces. As shown in Figure 7a, there are three types of attractive forces F1, F2, and F3 which depend on different distances and capacitances. F1 is the attractive force between adherent WS2 and Si nanostripe through the 30 nm insulator layer. F2 is the attractive force between suspended WS2 around edges and the Si nanostripe. F2 at edges equals to F1 and decreases gradually away from the Si nanostripe, so this force will let WS2 at edges be curved and exert a large uniaxial tensile strain on WS2. The electrostatic attraction between the suspended WS2 and bottom Si (F3) is much weaker which can be ignored because of a larger distance and smaller capacitance. The deflection of few layer WS2Δl under electrostatic force can be calculated by equation [40]:
Figure 7.
Mechanism of electrical tuning. (a) Schematic setup showing how tensile strain can be generated by electrostatic gating. F1, F2, and F3 represent three types of electrostatic forces. (b) Schematic diagram showing the changes of band structure and dominated transitions before and after applying strain.
PL2=8T0tΔl+643EtL21−υ2Δl3E5
where t is the thickness that equals to 0.8 or 1.6 nm, E is the Young’s modulus of WS2, and L is the effective length of strained WS2. The effective length is very small because only WS2 at edges experiences significant attractive forces. P=C2Vg22ε0 is the electrostatic pressure, where C is the capacitance per unit area and ε0 is the permittivity of vacuum. After considering the relationship between strain and the deflection, the strain can be estimated by [40]:
χ=2Δl2/L2=23/64PL/Et2/3E6
Based on the calculation above, the strain χ within effective area is larger than 2.8%. Such high strain is able to change the band structure of WS2 and influence the excitonic emission [41, 42, 43]. Therefore, in Figure 7b, we combine electrostatic doping and strain effect together to analyze the change of band structure and PL intensity. For 1L-WS2, if only electrostatic doping comes into effect, negative gating would enhance the PL intensity contributed by direct transition. However, in our case, larger strain generated from attractive forces changes the band gap of monolayer WS2. Without applied bias, the locations of valence-band maximum and conduction-band minimum are overlapped at the point Κ. Once strain is applied, the valence-band maximum will shift from Κ to Γ point, and indirect transition will happen. Therefore, the strain effect will weaken the PL intensity, which has opposite effect compared with electrostatic doping under negative gating. As a consequence, the PL enhancement is weaker under negative gating and more obvious under positive gating compared with previous works. For 2L-WS2, electrostatic effect can be ignored, and the strain effect is dominated. Without applied bias, the PL emission of bilayer WS2 contains both direct transitions and indirect transitions. If the strain becomes larger, valence-band maximum at the Κ point reduces, which promotes the direct transition along the Κ−Κ direction [43, 44].
4. Dielectric nanoparticles for bionanosensing
From the above analysis, we know that Mie resonators such as Si nanoparticles can combine with plasmonic nano-electrodes to obtain electrically controlled optical responses, and Mie resonators such as Si nanostripes can also interact with WS2 layers to realize abnormal electro-optical modulation based on electrostatic doping and strain effect. Further, it is necessary to utilize the unique properties of Mie resonators and analyze their application prospect in biosensing.
As we know, plasmonic nanostructures have been widely used in biosensing. Plasmon resonances experience redshift when increasing the surrounding refractive index, which is the most basic mechanism of biosensing. Dielectric Mie resonators have low-loss feature and strong directional scattering which also have a potential as biosensing nanoantennas. However, based on current reports and our experiments, we found the optical responses of single silicon nanostructures such as Si nanoparticles cannot exhibit obvious change when changing the surrounding refractive index. Therefore, the biosensor based on a single Si nanoparticle is insensitive.
Fortunately, we found the scattering spectra become very sensitive to surrounding refractive index if single Si nanoparticles combine to dimers or other oligomers. Based on our theoretical analysis, touching Si nanoparticles can produce strong electric field enhancement in the gap. This gap electric mode is a key factor for sensitive spectral change, because the gap electric mode would enhance and experience redshift with the increase of surrounding refractive index. As talked above, 1–2 nm silica layer is naturally grown on Si nanoparticles. Based on the mature biomarker technique, we can easily modify the silica surface with specific functional groups and realize the detection of many kinds of biomolecules. Furtherly, Si nanoparticles can be injected into living cells to realize the sensing in vivo. Finally, we can combine the biosensing and optoelectronic property of Mie resonators to build new type biosensors. On the one hand, biomolecules can change the electrical properties of dielectric nanostructures and then influence the optical signals. On the other hand, biomolecules can change the optical properties of nanoantennas and furtherly influence the electrical readout.
5. Conclusions
In this chapter, we have introduced the electrically controlled scattering of individual Mie resonators and PL from the WS2-Mie resonator hybrid system. The strong magnetic responses and low-loss feature make silicon-based Mie resonators become important building blocks in nanophotonics. Combining top-down and bottom-up fabrication methods, plasmon-Mie hybrid nanostructures and WS2-Mie hybrid nanostructures are fabricated, respectively. These structures give us an opportunity to apply voltages at nanoscale and collect the optical signals at single points. Interfaces are important in those hybrid nanodevices. The interfaces between plasmonic structures and Mie resonators bring new mechanism on carrier injection and changes of refractive index, while the contact between WS2 and Mie resonator generates unique PL active tuning arising from the synergistic effect between electrical doping and tensile strain under gate voltages. In the emerging applications based on dielectric Mie resonators, our findings provide an important and feasible method to build optoelectronic functional devices that can transfer electrical signal to optical signal. Furthermore, the excellent biosensing performance will expand the applications of Mie resonator-based optoelectronic devices.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 11774135, 11874183, and 61827822).
Conflict of interest
\n',keywords:"silicon nanoparticles, silicon nanostripes, WS2, active control, photoluminescence manipulation",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68608.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68608.xml",downloadPdfUrl:"/chapter/pdf-download/68608",previewPdfUrl:"/chapter/pdf-preview/68608",totalDownloads:334,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 4th 2019",dateReviewed:"July 16th 2019",datePrePublished:"August 30th 2019",datePublished:null,dateFinished:null,readingETA:"0",abstract:"The ability to dynamically modulate plasmon resonances or Mie resonances is crucial for practical application. Electrical tuning as one of the most efficiently active tuning methods has high switching speed and large modulation depth. Silicon as a typical high refractive index dielectric material can generate strong Mie resonances, which have shown comparable performances with plasmonic nanostructures in spectral tailoring and phase modulation. However, it is still unclear whether the optical response of single silicon nanoantenna can be electrically controlled effectively. In this chapter, we introduce two types of optoelectronic devices based on Mie resonances in silicon nanoantennas. First, we observe obvious blueshift and intensity attenuation of the plasmon-dielectric hybrid resonant peaks when applying bias voltages. Second, photoluminescence (PL) enhancement and modulation are achieved together in the WS2-Mie resonator hybrid system.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68608",risUrl:"/chapter/ris/68608",signatures:"Jiahao Yan and Yuchao Li",book:{id:"9012",title:"Applications of Nanobiotechnology",subtitle:null,fullTitle:"Applications of Nanobiotechnology",slug:"applications-of-nanobiotechnology",publishedDate:"July 8th 2020",bookSignature:"Margarita Stoytcheva and Roumen Zlatev",coverURL:"https://cdn.intechopen.com/books/images_new/9012.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"170080",title:"Dr.",name:"Margarita",middleName:null,surname:"Stoytcheva",slug:"margarita-stoytcheva",fullName:"Margarita Stoytcheva"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Electrically controlled optical responses of silicon-based nanoantennas",level:"1"},{id:"sec_3",title:"3. Hybrid nanoantennas based on silicon nanoparticles and nanostripes",level:"1"},{id:"sec_3_2",title:"3.1 Electrically driven scattering in a hybrid dielectric-plasmonic nanoantenna",level:"2"},{id:"sec_4_2",title:"3.2 PL enhancements enabled by silicon nanostripes",level:"2"},{id:"sec_5_2",title:"3.3 PL manipulation of monolayer and bilayer WS2 gated by silicon nanostripes",level:"2"},{id:"sec_7",title:"4. Dielectric nanoparticles for bionanosensing",level:"1"},{id:"sec_8",title:"5. Conclusions",level:"1"},{id:"sec_9",title:"Acknowledgments",level:"1"},{id:"sec_11",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Lee SH, Choi M, Kim T-T, Lee S, Liu M, Yin X, et al. Switching terahertz waves with gate-controlled active graphene metamaterials. Nature Materials. 2012;11:936-941. 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Exceptional tunability of band energy in a compressively strained trilayer MoS2 sheet. ACS Nano. 2013;7:7126-7131. DOI: 10.1021/nn4024834'},{id:"B43",body:'Dhakal KP, Roy S, Jang H, Chen X, Yun WS, Kim H, et al. Local strain induced band gap modulation and photoluminescence enhancement of multilayer transition metal dichalcogenides. Chemistry of Materials. 2017;29:5124-5133. DOI: 10.1021/acs.chemmater.7b00453'},{id:"B44",body:'Roldán R, Castellanos-Gomez A, Cappelluti E, Guinea F. Strain engineering in semiconducting two-dimensional crystals. Journal of Physics. Condensed Matter. 2015;27:313201. DOI: 10.1088/0953-8984/27/31/313201'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Jiahao Yan",address:null,affiliation:'
Institute of Nanophotonics, Jinan University, Guangzhou, China
Institute of Nanophotonics, Jinan University, Guangzhou, China
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Open Access publishing helps remove barriers and allows everyone to access valuable information, but article and book processing charges also exclude talented authors and editors who can’t afford to pay. The goal of our Women in Science program is to charge zero APCs, so none of our authors or editors have to pay for publication.
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