# Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery
## 1 Introduction  
Understanding the global carbon cycle in the context of climate change is currently a key point for researchers and society, and a critical parameter related to the carbon cycle is the terrestrial gross primary productivity (GPP). The accumulated GPP is used as a variable to calculate, over a period of time, the total amount of carbon dioxide fixed through photosynthesis [1, 2]. The GPP is also used to assess the efficiency of terrestrial ecosystems on carbon transfer [3, 4]. Changes in the GPP (diurnal and seasonal) depend on nutrient availability and climatic conditions (precipitation, humidity, light, and temperature), and the latter also influences its spatial distribution [5]. Estimations of GPP are decisive in monitoring carbon exchange in forest lands and agricultural ecosystems for the following reasons: information about GPP is essential for measuring and checking the variations in productivity; estimations have a spatial and temporal distribution; and the products can be used in optimizing agricultural management practices, especially over a global climate change context [6, 7]. The GPP can be derived using the eddy covariance (EC) method as the difference between the net ecosystem exchange (NEE) and the ecosystem respiration (R\({}_{\text{reco}}\)) during daylight [8]. Additionally, GPP can be obtained by using flux chambers; however, they are rarely used due to their small coverage and inaccurate results, associated with the collar insertion methodology [9]. The high spatial variability of the GPP affects its measurement from the ground at regional scales, making it very challenging [10]. For instance, the spatial coverage of the GPP estimated using the EC system is delimited to the tower footprint area [2]. Nevertheless, remote sensing (RS) has been used to estimate GPP from satellite platforms at different resolutions, contributing information to decisionmakers for improved management [2, 11]. Spatially-continuous GPP estimates have been mainly obtained from three RS models [12]. First, there are the vegetation index statistical (VI) models (VI-GPP) that report a direct relationship between vegetation indexes (VIs) and GPP [13, 14, 15, 16, 17, 10, 18, 19], as well as VIs combined with atmospheric variables, such as the photosynthetically active radiation (PAR) [1, 18, 20], land surface temperature [21], the land surface water index (LSWI), and the enhanced vegetation index (EVI) [22], among other models [12]. Second, there are light use efficiency (LUE-GPP) models, where the GPP is computed as the production of absorbed photosynthetically active radiation (APAR) by the plant canopy and the efficiency of converting APAR into carbon fixed trough photosynthesis, such as the MODIS-GPP model [23], the vegetation photosynthesis model (VPM) [10, 16], the CI-LUE model [24], and several more LUE-based models [12]. Third, there are process-based models [5]. These models use complex model structures with climatic, biological, and soil variables to explain the main components of ecological processes related to photosynthesis [15, 25]. The VI-based models are typical in agriculture because they reflect the actual status and growth stage of crops [26]. Estimation of GPP from VI-based models has been achieved through statistical relationships, using simple algorithms and few input variables [12]. Several researchers [2, 5, 13, 14, 15, 16, 17, 18, 19, 20, 23] have shown that VI from RS technology could be used to predict the GPP. In this context, VIs are spectral imaging transformations (mathematical expressions) of two or more bands. These VIs are usually calculated as ratios, subtractions, or linear combinations of the light reflectance in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) spectral regions [27]. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI, EVI2), and near-infrared reflectance of vegetation (NIRv) are the most common indexes used to estimate GPP [28].