A novel approach for monitoring in-field crop anomaliesA case study of asparagus crops in Peru using Sentinel-2 imagery

Gweld graff cysylltiadau
Fformatau enwi


Agricultural fields are naturally variable due to fluctuations in landscape, climate, soil properties, and management practices. Understanding the variability of biophysical features within agricultural fields helps to identify anomalies that occur at distinct points during the growing season. Early detection of these anomalies is fundamental to apply corrective measures before permanent plant damage occurs, preventing losses later in the season and therefore maximizing productivity and profitability. Multi-temporal remote sensing data has demonstrated great potential to describe the spatiotemporal variability of crop biophysical variables, and therefore may offer early detection of anomalies that occur within a field avoiding frequent ground inspection. Despite the high potential of satellite multi-temporal remote sensing data to describe the spatiotemporal variability of crop biophysical variables, it has not yet been fully exploited for application in anomaly crop detection. Due to the spatial resolution of many satellite EO products, most of crop monitoring approaches are more focused on LULC (Land Use Land Cover) systems rather than on agricultural systems. This has led to the majority of analyses being focused on the pixel as the spatial unit of analysis, despite crop management decisions are generally made at per-field basis. In addition, the selection of the best threshold value to discriminate anomalous from non-anomalous areas is not a trivial task. The recent availability of Sentinel-2 imagery and object-based analysis approaches provide a promising direction for monitoring crop changes over agricultural fields, that are more meaningful for crop managers. The image-object approach is especially important in crop monitoring, given the fact that agricultural management decisions are generally made at per-field basis. However, just a few authors have studied the use of Sentinel-2 imagery in agriculture from an object-based perspective. Although image-objects can be better characterized by integrating histogram analysis, the use of histograms to characterize image-objects is still limited. Histograms provide additional features to traditional statistic metrics that describe more effectively the heterogeneity of the pixels associated to a field plot and thus improve the study of changes in croplands. In addition, by transforming histograms into density curves the analyser can minimise the effects minor irregularities as well as outliers that can be caused by atmospheric or sensor disturbances. Traditionally, histograms have been used to establish thresholds that allow discriminate between changed and non-changed areas in Land Cover Change studies, but they have not been further explored to characterize within-field variability of croplands. The main aim of this work is to develop a method to characterize potential in-field anomalies of asparagus crop plots over space and time, using histogram analysis. To do that, this approach exploits the increased spatial and temporal resolution of Sentinel-2 imagery and uses histogram analysis to establish thresholds that differentiate the potentially anomalous pixels in each asparagus-field. The analysis was performed using Normalised Difference Vegetation Index (NDVI) products, derived from Sentinel-2 images, captured between April 2016 and September 2018 for the region of La Libertad-Peru. The spatial extents of the asparagus plots are known, and it is therefore proposed that the anomalous pixels can be differentiated from the non-anomalous ones using the distribution of the pixel NDVI values within the boundaries of each field. Since the same management activities are applied over the whole field, the distribution extracted from each plot is expected to be normal, and those values that form an elongated tail are more likely to be anomalous. To extract thematic information from plot data, a suitable threshold needs to be defined that optimally separates the anomalous from the non-anomalous pixels. An optimal threshold has to be different for each crop plot, as its distribution depends on biophysical variables that fluctuate over space and time, such as crop variety, landscape, climate, soil properties, and management practices. Thus, the thresholding method needs to be sensitive to the distribution of the NDVI pixel values contained within each crop field, and produce thresholds that are different at distinct points during the growing season. The histogram analysis proposed, allows identifying dynamic thresholds, sensitive to the statistics of NDVI pixel values distribution within each plot, to differentiate the potentially anomalous pixels. A fundamental assumption is that the NDVI pixel values extracted from each crop field are expected to be normally distributed. Thus, the difference between a distribution with anomalous values and one without anomalous values is the normality of each field distribution. The removal of the atypical values in increments from the tails towards the median until the distribution is normal, separates the anomalous from the non-anomalous NDVI pixels. The distribution normality is assessed based upon measures of skewness and kurtosis for each iteration. The combination of the lowest kurtosis and skewness is used to identify the bins at which the histogram should be sliced to produce the most normal distribution. Results show that the histogram analysis approach is able to extract thresholds sensitive to in-plot crop performance, based on multi-temporal NDVI values. The spatial distribution of anomalous pixels within each field showed to be different for each type of anomaly, and different crop stages. Most of the anomalous pixels situated under the lower thresholds were clustered near the plot edges due to the presence of paths but also potentially due to image mis-registration. It was found that when the field crops have the lowest values of NDVI (after the harvest), anomalous pixels, either above the upper threshold or below the lower threshold, formed small scattered clusters within the field crop, helping to inform in-plot management strategies.