## 3 Results
### Focus of the Studies
(2015) [115] built an accurate classifier through extracted multidimensional sequential patterns with MODIS time series combined with field data. [PERSON] et al. [118] employed MODIS data paired with 600 ground survey points to create a landcover map of Mali. They claim the ability to detect small-scale, but important, wetland features such as rice cultivation areas for regional-scale studies. [PERSON] et al. (2020) [47] used more than 400,000 land-cover training data points for the year 2013, paired with Landsat images to train locally optimized Random Forest models predicting the presence and absence of rainfed and irrigated agricultural fields across the non-desert area of West Africa. Irrigated areas, especially those dedicated to rice cultivation, are the subject of several publications on crop type mapping. [PERSON] et al. [111] mapped irrigated areas in Ghana using Landsat and MODIS, [PERSON] et al. [109] identified suitable areas for rice cultivation in Nigeria, and [PERSON] et al. [114] mapped the distribution of croplands under active irrigation between 1987 and 2020 along the Senegal River based on Landsat imagery, whereas [PERSON] et al. [116] focused on the Kou river in Burkina Faso between 1988 and 2009. In a second study, [PERSON] et al. [113] identified critical spatial data layers for assessing land suitability that pinpoints optimal rice cultivation areas in inland valley wetlands. In 2020, [PERSON] et al. [108] investigated rice cultivation by combining Sentinel 1 and 2 datasets to leverage the individual advantages of each data source. [PERSON] et al. [63,64] aimed to enhance the classification accuracy of multi-sensor crop mapping in West Africa by similarly combining two data sources. However, [PERSON] employed optical and SAR data in a sequential masking classification to address the region's spatial heterogeneity. In a third study from 2017, [PERSON] et al. [34] estimated fractional cropland cover using a sub-pixel approach based on MODIS and Landsat to improve the accuracy. [PERSON] et al. [61] proposed a framework for integrating Sentinel-1 and 2 imagery to map maize crops, focusing on the ideal combination of Sentinel-1's dual-polarization to complement Sentinel-2 for effective agricultural applications. In a second study from 2023, [PERSON] et al. [62] conducted pioneering research in Nigeria by examining the potential of the Google Earth Engine platform and machine learning techniques for mapping maize croplands in Nigeria from 2016 to 2019. For a comprehensive crop distribution map in southern Mali, in 2018, [PERSON] et al. [11] evaluated a cloud-based multi-temporal ensemble classifier, integrating WorldView images and various machine learning approaches. [PERSON] et al. [117] used deep learning techniques that have recently shown promising results in crop mapping. However, the lack of labeled samples limits the classification performance. Consequently,there is a need for methods that are capable of exploiting label-rich environments to classify crops in label-scarce regions. While few-shot-learning methods have successfully adapted this issue in a computer vision for natural images, their application in crop mapping using time-series data remains largely unexplored. Field characteristics encompass a broad range of factors, but the reviewed studies here specifically addressed parameters such as field boundaries, textural features, soil properties or the share of trees and tree species on a field. Accurate delineation of smallholder farm boundaries is challenging due to the field's small size, irregular shapes, and mixed cropping systems. [PERSON] et al. [4] use very high-resolution WorldView-2/3 data and a fully convolutional neural network to achieve promising results in Nigeria and Mali for detecting field boundaries. [PERSON] et al. [29] emphasize the importance of satellite-derived textural features as a valuable complement to spectral and polarimetric features, finding that textural features in this study comprise half of the total significant feature analyzed.