Remote sensing imagery and products used in water harvesting studies: a review

Remote sensing plays a pivotal role in water harvesting studies by enabling the collection and interpretation of spatial data across extensive regions. This paper examines 290 peer-reviewed articles to assess the adoption and utilisation of remote sensing in water harvesting research. Findings reveal that remote sensing is widely used, with around 92 % of studies published in 2023 incorporating such data. The most frequently used include digital elevation models (DEMs) such as SRTM (91 studies) and ASTER GDEM (60 studies), multi-spectral datasets like Landsat (117 studies), and climatic products such as TRMM (20 studies). DEMs are predominantly used for hydrological modelling, while multi-spectral imagery sources facilitate land use and land cover (LULC) mapping, often through bespoke classification rather than the use of pre-existing global datasets. Despite the critical role of rainfall in water harvesting, the adoption of satellite-derived climatic data remains limited, with researchers often relying on in situ measurements. This review highlights the advantages of extracting multiple thematic layers from a single remote sensing source to ensure consistency in resolution and coverage. Additionally, data fusion techniques are increasingly important for integrating disparate datasets, though challenges remain in reconciling differing spatial and temporal resolutions. This review demonstrates the increasing reliance on remote sensing in water harvesting research while identifying gaps, such as the underutilization of high-resolution climatic imagery sources and products. Evidence-based recommendations are provided to guide future research, including the selection of appropriate DEMs, the adoption of satellite-derived rainfall data, and the optimisation of multi-source data fusion. The findings highlight the need for researchers to adopt a more systematic approach in documenting and detailing the remote sensing sources and products used, to enhance their utility in water harvesting applications. • A gap exists between the extensive use of DEMs/multi-spectral data and the limited use of high-resolution climatic products . • Researchers heavily favour creating bespoke land cover maps from imagery rather than using available global datasets. • Data fusion of disparate RS sources is a growing necessity but remains challenging due to differing resolutions. • This review provides a clear framework for DEM selection and advocates for the greater adoption of satellite rainfall data. • We recommend extracting multiple thematic layers from a single RS source to ensure consistency and efficiency.