Multi-Temporal Assessment of Remotely Sensed Autumn Grass Senescence across Climatic and Topographic Gradients

Climate and topography are influential variables in the autumn senescence of grassland ecosystems. For instance, extreme weather can lead to earlier or later than normal, while higher altitudes often favor early grass senescence. However, date, there is no comprehensive understanding key remote-sensing-derived environmental that influence occurrence senescence, particularly tropical subtropical regions. Meanwhile, knowledge relationship between required aid formulation optimal rangeland management practices. Therefore, this study aimed examine spatial autocorrelations remotely sensed vis-a-vis climatic topographic grasslands. Sentinel 2′s Normalized Difference NIR/Rededge Red-Edge (NDRE) Chlorophyll (Chlred-edge) indices were used as best proxies explain monthly (i.e., March June) estimates examined against their corresponding factors using Partial Least Square Regression (PLSR), Multiple Linear (MLR), Classification Trees (CART), Random Forest (RFR) models. The RFR model displayed a superior performance on both RMSEs 0.017, 0.012, 0.056, 0.013, well R2s 0.69, 0.71, 0.56, 0.71 for NDRE, with 0.023, 0.018, 0.014 0.59, 0.60, 0.72 Chlred-edge March, April, May, June, respectively). Next, mean values separately tested significance average minimum (Tmin) maximum (Tmax) air temperatures, rainfall, soil moisture, solar radiation) slope, aspect, elevation) define drivers Overall, results indicated Tmax (p = 0.000 0.005 NDRE Chlred-edge, respectively), Tmin 0.021 0.041 moisture 0.031 0.040 respectively) most drivers. these have shown role remote sensing techniques assessing along gradients determining area