## 4 Discussion
### The Need for Integration of New and High-Resolution RS Datasets and High Quality Reference Data
(2015) [64] found that overlapping crop calendars can result in similarities in the spectral profiles of different crops. This can be attributed to similarities in their growth stages and cropping schedules. Moreover, high spectral variability and within-field heterogeneity, which may be influenced by factors such as soil fertility, soil moisture conditions and pests or diseases, further complicate crop differentiation. Cropland systems across West Africa are highly diverse and often adapted to very specific environmental conditions, making it challenging to distinguish different land use types based on their spectral phenological signatures alone. To address these challenges, one potential solution is the use of higher radiometric resolution, as offered by hyperspectral missions like EnMAP [128; 200]. Integrating such data could, for instance, enhance the effectiveness of approaches aimed at distinguishing similar crops and land use types. Similarly, [PERSON] et al. [120] (2021) suggest using data from other modalities like thermal or LiDAR to improve the models in the future. Hyperspectral, thermal, and LiDAR missions offer transformative potential for improving the monitoring and management of small-scale agriculture in complex and diverse regions such as West Africa. Hyperspectral missions excel in capturing fine spectral details across a broad range of wavelengths, allowing for precise differentiation of crop health, soil properties, and environmental variables. This capability supports more accurate yield forecasts, soil assessments, and early detection of crop stress and disease, allowing for timely interventions and optimized resource use However, their application is hindered by the computational intensity of data processing, limited spatial resolution for small-scale contexts, and relatively high cost and restricted availability of hyperspectral sensors compared to multispectral systems [200]. Thermal RS complements hyperspectral data by providing critical insights into crop and soil thermal properties, directly linked to water stress, irrigation needs, and soil moisture content. In West Africa, where agriculture is highly dependent on seasonal rainfall and vulnerable to drought, thermal data becomes invaluable for identifying water-deficient zones and improving irrigation practices. When integrated with hyperspectral and multispectral data, thermal sensing enhances the depth and reliability of agricultural assessments. Nevertheless, its lower spatial resolution and susceptibility to atmospheric conditions, such as cloud cover, can limit its precision and effectiveness [201]. LiDAR further enriches the potential of remote sensing by offering three-dimensional structural information about crop canopies and terrain. These data can improve biomass estimation and land use mapping, critical for understanding the dynamics of small-scale agricultural systems. As these technologies evolve, integrating hyperspectral, thermal, and LiDAR data, alongside advancements in computing capacity, holds promise for overcoming current limitations and unlocking new applications tailored to the challenges of small-scale farming in West Africa [46; 202]. * As indicated in Table 2, several studies criticize the lack and quality of reference data for small-holder farms in West Africa. High quality and abundant unbiased reference data are crucial to improve and validate the accuracy of RS methodologies, particularly in heterogenous landscapes [117; 119; 203]. Such data, used for training and validation, are critical for enhancing results. Inadequate or insufficient data can lead to overfitting and pose challenges in maintaining the performance of deep learning methods [50]. Several articles noted the scarcity of validation and trainings data [29; 33; 36; 50; 144; 160], underscoring the need for reliable reference datasets. [PERSON] et al. 2018 [36] employed a phenology-based classification method as it has shown advantages when reference data, here field survey data, is too scarce.