# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 1 Introduction  
Levs are temporary forages (either harvested or grazed) which are part of a crop rotation. In Nordic countries, forages are the main feeding source for livestock in dairy and meat production systems. In Sweden, ley dominates the agricultural land use, and comprised 44% of the total arable lands in 2022 [1]. Leps are typically harvested 2-4 times per year in southern Sweden, and 2-3 times in northern Sweden. Determining the harvest window, especially for first harvest, is important for farmers, since it directly affects the forage yield and quality and, ultimately, the profitability [2]. Accurate estimations of forage biomass is one of the factors for determining the harvest time and is also important for fertilization strategies and herbicide spraying [3]. Traditional approaches involving field sampling and laboratory measuring are destructive and time- and resource-consuming. As an alternative, several studies explored the utilization of hand-held rising plate meters and field spectrometers (e.g., FieldSpec, Yara N-sensor) to make rapid and accurate biomass in situ estimations of forages [4, 5, 6, 7]. Rising plate meters are easy-to-use and inexpensive tools that measure the height and density of swards from which biomass can be derived based on species-dependent calibrationcurves. However, these tools have limited spatialization capabilities. Field spectrometers provide high-resolution spectral information, but their use for practical applications is limited by their costs and, more importantly, limited spatialization capabilities compared to imaging sensors. Alternatively, open-access satellites (e.g., Landsat, MODIS and Sentinel-2) supply multispectral images with a spatial resolution ranging between 10 and 250 m for large-scale vegetation monitoring [8; 9; 10]. Satellite imaging systems offer several advantages, such as technical maturity and stability, open access to data and a large field of view [11]. The Sentinel-2 satellites constellation provides open access images with a relatively high spatial resolution (10-60 m) and high frequency of revisit [12] of about 2 days in northern Sweden. These time series of satellite images can be useful indicators to monitor the biomass and growth of crops [13; 14; 15]. Traditional satellite-based biomass estimation models are usually developed by linking spectral-derived vegetation index (hereafter referred to as VI, such as the normalized difference vegetation index, _NDVI_) and field measurements using basic univariate regression (UR) models, such as linear, polynomial, exponential, power, etc. [10; 16]. However, with increasingly larger datasets, multivariate regressions (MR), e.g., partial least square regression (PLSR) and machine learning-based support vector machine (SVM) regression (SVR) and random forest (RF) regression (RFR), have become increasingly used for the estimation of crop traits, such as leaf area index (LAI [17; 18]), plant nitrogen nutrition [19; 20] and biomass [3; 5; 21; 22]. PLSR aims to extract latent factors that represent most of the variation between predictor and response variables to reduce overfit [23; 24]. Therefore, PLSR is able to encompass more explanatory variables (e.g., individual bands and VIs) to build regression models with greater robustness compared to traditional regression approaches. SVM is a nonparametric statistical technique without data distribution assumptions. It was originally proposed by [PERSON] [25] for classification purposes by setting labels for datasets and searching separation hyperplanes; it was later developed further for regressions [26]. Similar to SVM, RF requires no specific data distribution assumption, but the difference is that it uses an ensemble-learning method, which utilizes multiple algorithms (trees) over one model to make a more accurate prediction [27], and it was also developed for regressions. RFR can deal with small size datasets [28] as well as high dimensional and collinear data [29] at a high running speed.