Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

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.

Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

“…Various studies have shown the potential to map crop types based on Sentinel-1 time series without additional optical data input in Europe (e.g., [30][31][32][33][34][35][36][37][38][39][40][41]) and in Germany (e.g., [42][43][44]). However, particularly promising are approaches combining optical and SAR data (e.g., for Asia [45], Africa [46,47], Europe [48][49][50][51][52][53][54][55], and Germany [56][57][58]). Such synergistic approaches consistently showed better overall accuracies than monosensor classifications (e.g., [48,51,56,57]), particularly in the case of challenging cloud situations [49].…”

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

“…Even in well-developed and well-organised countries, ground-based crop area estimates are often not available until a few months after harvest [39]. Obtaining a reliable figure before harvest is a major challenge and, at the same time, vital for the formulation of policymaking and decision-making [39,59]. Figure 2 shows the processing steps typically applied to remote sensing data to predict crop type.…”

“…Compared to a humid environment, ASA regions typically have specific climatic and regional characteristics which pose many challenges to accurately identifying and estimating crop area [39,59]. Due to considerable changes in the spatial and temporal distribution of rainfall, and the fact that the majority of smallholder farms in ASA regions rely on rainfall to start planting, identifying and estimating crop area can be difficult [39].…”

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

“…Apart from temporal data requirements, crop mapping and identification demands robust algorithms that are sensitive to the spectral and temporal information of the multispectral data [17][18][19][20]. Different computer vision techniques have been applied depending upon the application in the field of agriculture.…”