Abstract
The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.
Highlights
Accurate and detailed knowledge of land cover/land use (LC/LU) is a crucial issue for research work and many operational applications in agriculture such as a crop water requirement, crop yield prediction, etc. e availability of remote sensing imagery, that offered the access to a set of large regions, is a major asset to elaborate LC/LU maps
In order to achieve these improvements and better identify land cover types, combining datasets acquired from remote sensors that rely on different physical fundamentals, and providing synergistic information on surface properties, leads to a promising approach [11,12,13], with the recent free of charge image datasets [14], which provide the possibility of data fusion of higher spectral resolution, compensating the limits of the use of unique data
For example, the start of the season begins when the rate of increase in NDVI values is greater than the previous successive observations during the period of vegetation growth. e end of the season is defined as the time during the maturation period when there is a significant decrease in NDVI
Summary
Accurate and detailed knowledge of land cover/land use (LC/LU) is a crucial issue for research work and many operational applications in agriculture such as a crop water requirement, crop yield prediction, etc. e availability of remote sensing imagery, that offered the access to a set of large regions, is a major asset to elaborate LC/LU maps. In order to achieve these improvements and better identify land cover types, combining datasets acquired from remote sensors that rely on different physical fundamentals, and providing synergistic information on surface properties, leads to a promising approach [11,12,13], with the recent free of charge image datasets (optical and radar images from the Sentinel satellite sensors) [14], which provide the possibility of data fusion of higher spectral resolution, compensating the limits of the use of unique data. On the basis of this hypothesis, the objective of this study was to evaluate the usefulness of using SAR data on the one hand and of the combination of both types of remote sensing data, on the other hand, to map and identify crop types
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