Detecting and mapping the occurrence, spatial distribution and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. This work, therefore, provides an overview of advancements in satellite remote sensing for mapping and monitoring of AIPs and associated challenges and opportunities. Satellite remote sensing techniques have been successful in detecting and mapping the spatial and temporal distribution of AIPs in rangeland ecosystems. Also, the launch of high spatial resolution and hyperspectral remote sensing sensors marked a major breakthrough to precise characterization of earth surface feature as well as optimal resource monitoring. Although essential, the improvements in spatial and spectral properties of remote sensing sensors presented a number of challenges including the excessive acquisition and limited temporal resolution. Therefore, the use of high spatial and hyperspectral datasets is not a plausible alternative to continued and operational scale earth observation, especially in financially constrained countries. On the other hand, literature shows that image classification algorithms have been instrumental in compensating the poor spatial and spectral resolution of remote sensing sensors. Furthermore, the emergence of robust and advanced nonparametric image classification algorithms have been a major development in image classification algorithms. Therefore, to address the inevitable challenges arising with satellite sensor development technology it is necessary to explore the use of robust and advanced non-parametric image classifiers with data provided by the new generation of multispectral sensors with improved spatial and spectral resolutions. This will promote longterm and large scale mapping of AIPs, especially in financially constrained countries.

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Grass senescence estimation in rangeland environments is particularly important for monitoring the conditions of forage quality and quantity. During senescence, grasses lose their nutrients from the leaves to the root systems and thereby affecting forage productivity. Numerous studies on the remote sensing of grasslands have been conducted during the senescent phenological stage. However, despite the efforts made in previous remote sensing studies on grass senescence, its role in estimating grass senescence is rudimentary. More so, the strengths and limitations presented by the newly developed remote sensing instruments in grass senescence estimation are not well documented. This work therefore provides a detailed overview on the progress of remote sensing applications in characterizing grass senescence. The review further highlights the challenges and possible opportunities presented by these techniques. Overall, the review indicates that the available studies on remotely sensed grass senescence applications are focused on understanding biophysical and biochemical properties and these studies identify the Leaf Area Index (LAI), biomass and chlorophyll content, among others, as the key indicators of grass senescence. Nonetheless, recent scientific research highlights a mismatch between studies on the grass senescence and the development in remote sensing technologies. The use of sophisticated and robust time-series analysis techniques like TIMESAT together with improved sensing characteristics from the new generation sensors seem to present new opportunities for the optimal quantification of grass senescence at resolutions complementary to the spatial extents of the rangelands. We therefore recommend further research in this field through the adoption of new satellite technologies and advanced spatial data analytics to enhance the monitoring of rangeland resources.

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 R2s of 0.69, 0.71, 0.56, and 0.71 for the NDRE, with RMSEs and R2s 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 (Tmin) and maximum (Tmax) 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 Tmax (p = 0.000 and 0.005 for the NDRE and the Chlred-edge, respectively), Tmin (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