## 1 Introduction  
Benefiting from the large number of high-quality spectral-temporal images captured from Earth observation satellites, remote-sensing based on multitemporal and multispectral imagery has a great potential to provide timely and comprehensive agricultural and environmental information. With the availability of more and more remote sensing platforms, massive amounts of remotely sensed data are produced [1]; for example, every 5 days, the Sentinel-2 multispectral satellite constellation can provide a global coverage of Earth's land surface. According to [PERSON] et al. [2], new policies for the free distribution of satellitesdata, the distribution of archive data that makes large-scale retrospective analysis possible, and the increasing number of satellites with higher revisit frequency, are the main reasons for the growing importance of a need for methodological research on multitemporal data analysis. These large amounts of available remote sensing data have largely been applied to hazard assessment, coastal applications, agricultural applications, natural resource management, etc. [3]. In the field of agriculture, repetitive information on crop status throughout the season at different scales and for different actors, provides the capacity to assist the adaptive evolution of agricultural practices [4]. [PERSON] et al. [5] summarized six main applications of remote sensing in agriculture management and monitoring. For all of these applications, the information of interest consists of traits or features of the agricultural systems, and especially how these vary in space and time. Since plant growth and plant developmental processes are strongly influenced by fluctuations in environmental conditions, and especially ambient temperature [6], capturing the dynamics of crop growth over time, especially at critical growth stages is crucial. Physiological and physical properties of the production system, and consequently the reflectance spectrum, change according to growth conditions, and time of measurement [7]. Consequently, remotely sensed data, obtained by satellites over the time, can provide a set of detailed data on plant growth and development [8]. Regardless of the applications, different approaches to deal with the increasing volumes of data and variables of interest can be found in the remote sensing literature. As [PERSON] et al. [9] state in their review, regression methods constitute a widely used tool for explanatory and prediction purposes. When it comes to producing estimates, a popular method to implement this prediction, is the Partial Least Squares method (PLS) [9]. Numerous papers have previously discussed this method from a geometrical, mathematical and statistical point of view [10; 11]. PLS methods have been extensively used [12] as they are well-suited to deal with multi-variate data structures with high covariance and redundancy [13], such as are found in remote sensing datasets. However, challenges remain with respect to addressing these increased data volumes and increased data variability [14]. Exploiting multispectral image time-series is a promising but still relatively underexplored research direction due to the complexity of jointly analysing spatial, spectral and temporal information [15]. The increasing complexity of information leads to a natural generalization of the concept of the 'data set' from conventional tables to higher-dimensional arrays [16]. As [PERSON] [17] specified, when including time as an additional dimension, the dataset becomes three-way, which cannot be handled by commonly used conventional prediction models. Among the alternatives proposed in the literature for modelling multi-way data, deep learning has the potential to outperform traditional classification and regression techniques [18]. However, deep-learning approaches often requires a large dataset to perform the learning and do not provide immediate access to intermediate steps to analyse and understand the agronomic processes that impact production.