## 6 Conclusions  
In summary, this review revealed the following trends and research opportunities. This review simplified the remote sensing of managed global grasslands into four broad areas: management indicators, pasture production, species/botanical classification, and the degradation of pastures from anthropogenic and environmental variables. In this review, less attention is received in Southeast Asia, the northern part of Latin America (except Mexico), the Middle East (except Iran and Syria), and Africa (except South Africa and Ethiopia). Low-resolution multispectral sensors (e.g., MODIS and Landsat) are the most used due to availability and low cost. The higher resolution multispectral satellites are not free but are also constrained to a few bands (mainly visible and NIR), except Worldview and Venus, which have 29 and 12 bands. SAR imagery, especially Sentinel 1 (publicly available), tended to be under-utilised. In particular, SAR data were not applied for mapping management traits and pasture degradation. The utility and accuracy of SAR modelling depend on the knowledge domain used to suit the biophysical variables and target environment. The hyperspectral sensors used in this review were mainly applied for pasture composition due to the level of detail required. Integrating multiple remote sensing tends to fix specific errors or limitations associated with sensors and the target environment. However, only some studies have combined sensors (SAR, multi and hyper-spectral images). Less than 20 studies considered study areas that were less than 50 ha. The number of studies that used daily (i.e., 8) and weekly (i.e., 5) time-series remote sensing products is few, thus, making operational and automation a drawback. Many studies that used machine learning approaches parameterized the empirical methods by selecting bands, thereby constraining this process to specific sites and parameters. Only a few studies used the characterization of vegetation properties based on reflectance values using spectral features to understand the relationship between pixels. The size of field data used in most of the studies for validations is relatively small (test data reveal \(\leq\) 120), thereby constraining remote sensing products regarding the robustness to capture image patterns and improve model calibration (for machine learning applications). A few studies (18 studies) considered the adaptive management of pastures, which involved integrating remote sensing products with management and environmental data. It is recommended that future research efforts consider the integration of management and environmental data with remote sensing products for validation purposes and to make pasture management more sustainable. This review identified that social awareness, knowledge, skill, and well-defined research objectives are essential milestones to bring end users into the workflow. We provided a list of agencies providing remote sensing services that can make the future of global monitoring of pastures more sustainable. The remote sensing of pasture monitoring with satellites and UAS to derive biomass, LAI, fPAR, fCOVER, ANPP, and quantify physical quantity like pasture heights, discriminate vegetation canopy, manage pasture quality indicators (i.e., soil nitrogen, irrigation, soil water content, fertilizer application, moving, etc.) and maintain pasture ecosystem from degradation has evolved. In this review, we provided a synthesis of how remote sensing can combine with modelling tools to facilitate the goal of digital agricultural sustainability. C.M. ; review and editing, I.A. ; review and editing, A.M.F. and M.T.H. All authors have read and agreed to the published version of the manuscript. This work was funded through the University of Tasmania, Tasmanian Institute of Agriculture. Data Availability Statement: Data used for analysis were extracted from list of referenced. Meta data is available upon request from the corresponding author. M.G.O.