## 1 Introduction
Numerous studies have documented how the overuse of Chaco forests leads to biodiversity loss [25; 26; 27; 28; 29; 30], changes in surface and groundwater hydrological cycles [31; 32; 33; 34; 35; 36], and soil loss, which contributes to the advancement of land desertification [33; 35]. Furthermore, forest degradation leads to increased carbon dioxide emissions into the atmosphere [36; 37; 38; 39]. Previous studies in the Chaco region have examined the effects of anthropogenic and natural disturbances on plant community phenology using RS data [11; 40; 41]. Most of these studies have focused on the Normalized Difference Vegetation Index (NDVI),widely recognized for its ability to assess the fraction of photosynthetically active radiation absorbed by vegetation [42; 43; 44]. Recently, however, advancements in satellite data and processing power have enabled the exploration of additional spectral indices with enhanced spatial and temporal resolution and faster processing speeds [18; 45]. These new tools provide opportunities for detailed phenological analyses and the development of novel indicators for sustainable management specifically tailored to the Chaco region. This study aims to explore the potential of spectral indices derived from Sentinel-2 remote sensing data, combined with field data, as indicators of forest structural integrity and degradation resulting from sustainable or unsustainable management practices in the Arid Chaco forests. We hypothesize that the phenological patterns of RS ecological indices vary depending on forest characteristics and the degree of alteration. To test this hypothesis, we calculated a series of spectral indices useful for RS-based ecosystem descriptions, including those related to photosynthetic activity and biomass, canopy water content and moisture, water stress, soil characteristics, chlorophyll and carotenoid content, and fire-induced stress. We then performed a Variance Inflation Factor (VIF) analysis to identify and retain only the variables with low multicollinearity, ensuring a focus on minimally correlated variables. Next, we assessed the relationship between this set of low-correlated spectral indices and field-collected data on forest structures across ecosystems with varying levels of structural alteration. Specifically, based on a set of minimally correlated monthly RS spectral variables and field data, we addressed the following questions:  
1. Do forests with different management regimes, levels of degradation (structural alteration), and dominant species exhibit different spectral phenology (i.e., the study of seasonal changes in vegetation as observed through spectral indices)? 2. Which indices effectively differentiate forests with different levels of structural degradation? Validating our hypothesis--that in highly seasonal subtropical forests, the phenological patterns of remote sensing (RS) ecological indices vary with ecosystem characteristics and the degree of alteration--could provide further evidence of RS as a powerful proxy for modeling forest attributes. The integration of a statistically adequate number of field samples with monthly Sentinel RS ecological indices would strengthen our ability to conduct accurate, large-scale assessments at a significantly lower cost than relying solely on traditional field inventories.