Regional scale crop mapping using multi-temporal satellite imagery

Abstract

ABSTRACT:One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

“…In addition to regional estimates, early crop type information at the parcel level is also an essential prerequisite for any crop monitoring activity that aims at early anomaly detection. Satellite remote sensing has proven to be an invaluable tool for accurate crop mapping in regions across the world [4][5][6][7].…”

“…Clouds and cloud shadows, therefore, lead to gaps in optical imagery and missing data in optical time series [19]. For classification and monitoring purposes, this drawback significantly affects performance [6,20].…”

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

“…However, such advanced operational approaches cannot be replicated in developing regions other than North America and Europe because of the lack of systematic collection of ground training samples. Alternate procedures have consisted of unsupervised approaches [13,[21][22][23] and supervised methods in small regional areas with different classifiers including decision trees [12], Support Vector Machine [24,25], Random Forest [12], neural networks [26][27][28], data mining [29], and hybrid methods [30]. In order to improve classification results, the following issues were investigated in literature which include the selection of the dates [31], temporal windows derivation [32], input features selection [33] and automated classification methods [34].…”

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

“…Overall, there is a good correspondence between satellite-derived and reference areas, with an average R 2 of 0.91 and a bias of +4.3 thousand ha. This bias might be attributed to the confusion between winter crops and early spring cereal crops [60].…”