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