## 1 Introduction
In the hydrological cycle, ET also serves as a key process that links energy, carbon and water cycles [17]. Ground-based observation networks, mainly from eddy covariance flux towers, have been established to estimate various variables such as ET and gross primary productivity (GPP). Some typical networks include FLUXNET [18], AmeriFlux [19], ILTER [20] and NEON [21]. These sites are mostly situated in more humid and developed areas, while large drylands in Africa, South America, Middle East, and central Asia have limited representation. Remote sensing has been functional in demonstrating the role of GPP and ET, as well as ecosystem structure within the context of the broader Earth system [4; 5]. Favorable atmospheric conditions (lower cloud coverage) in drylands offer advantages for optical remotesensing and increase the chances of high-quality observations. In addition, pioneering technologies were developed in drylands, including the retrieval of surface reflectance [22; 23], as well as quantitative estimates of vegetation conditions and photosynthetic capacity in the rangelands of the Great Plains [24]. The later research afterward became the Normalized Difference Vegetation Index (NDVI) [25], calculated as NDVI = (NIR - R)/(NIR + R), which is generally based on the high reflectivity of plants in the near-infrared (NIR) wavelength range and the high absorption by plants in the red (R) wavelength range for photosynthesis. With spectral normalization, NDVI minimizes effects from topographic variation, sun-sensor geometry and shadows [26], thus becoming one of the most widely used vegetation indices [26; 27; 28]. The long-term and high-frequency NDVI time series retrievals, like the 23 year time series generated for long-term studies of the Sahel region [26], enhanced our understanding of the long-term trends and driving forces behind regional dryland dynamics. In Australia, an analysis to check whether vegetation cover increased was conducted using calibrated advanced very high-resolution radiometer data spanning 1981-2006 [27]. In the same region, an analysis of the decadal time-scale changes was conducted using AVHRR GIMMS NDVI dataset to check the relationship between a proxy for vegetation productivity and annual rainfall [28]. Subsequently, [PERSON] et al. [29] observed a greening of the globe over recent decades which they attributed to CO2 fertilization effect. A study by [PERSON] et al. [30] to analyze trends in vegetation greenness of semi-arid areas showed an increase in greenness found both in semi-arid areas where precipitation is the dominating limiting factor for plant production and where air temperature is the primary growth constraint. Similarly, [PERSON] et al. [31] in their research using NOAA AVHRR data in Mediterranean basin, the Sahel from the Atlantic to the Red Sea, major parts of the drylands of Southern Africa, China-Mongolia and the drylands of South America showed a greening-up. Other studies conducted by [PERSON] et al. [32], which showed an increase in net primary productivity (NPP), and by [PERSON] et al. [33], which discussed the greening of earth and its drivers, attributed the major cause to be CO\({}_{2}\) fertilization. These investigations recently elucidated that global drylands greening is largely due to CO\({}_{2}\) fertilization's effects in the context of global climate change [29; 30; 31; 32; 33]. Since the 2000s, there has been an exponential increase in the volume of remote sensing data, resulting from newly launched sensors and products, as well as higher resolution domains (spatial, temporal, and spectral). Such expansion of big data revolutionized our understanding of the Earth system, while posing a higher request for advances in data management and processing capability. Accordingly, increasing the availability of cloud-based computing and analytical platforms, such as Amazon Web Service (AWS) and Google Earth Engine [34], is being widely used for dryland research [35; 36; 37; 38; 39; 40].