Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine

Abstract

Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

“…They are home to specific species of vegetation and fish that can survive in salinated environments, but their drainage networks are often underground, making them hard to identify in RS imagery. An RF model was used in [166] to identify water cavities where sebkhas form in Morocco. Wetland inventory maps are increasingly being used to inform carbon pricing, ecosystem service values, and conservation/restoration decisions.…”

“…(2) SAR + optical RS images for better model performance: In addition, many studies reported [17,57,68,72,74,165,166,182,212,214] or suggested in future work [46,56,107,215] that SAR combined with optical RS images would improve model performance. Three classification methods (SVM, RF, and decision fusion) were used in [52] for the pixel-wise classification for crop mapping.…”

“…A RF model was used on GEE in [166] to identify water cavities where sebkhas form in Morocco. The authors used digital elevation data, SAR, and optical imagery, as well as digital photos on GEE to identify saltwater cavities and their aquifers with high accuracy.…”

“…There is a lack of domain-specific models and methods (GEE algorithms are general) because GEE is more developed in some areas (LULC, forest, vegetation, crop) than others [17,46,107,152,158,225,239]; No neural networks (NNs) are currently supported on GEE directly, but many authors use DL models for their research [46,55,71,107,136,161,166], and they either have to train their DL models offline or on Google Cloud AI, which is not free of charge. Authors can also use TensorFlow on Google Colab and Google Cloud AI but not directly on GEE.…”

Google Earth Engine e sua aplicabilidade na gestão de recursos hídricos

“…Ghorbani 2019] e de [Hakdaoui et al 2020] investigaram e avaliaram a complementaridade dos dados do Landsat, Sentinel-2 e do Sentinel-1 em um ambiente no deserto de Imlili Sebkha (Marrocos). Os autores destacam a potencial aplicação do GEE no processamento de grandes quantidades de dados de satélite para observação de cavidades de água salgada permanentes, úmidas/secas, espaciais-temporais, livres e de longo prazo, além do monitoramento de umidade de Imlili Sebkha.…”