Land degradation is a cause of many social, economic, and environmentalproblems. Therefore identification and monitoring of high-risk areas forland degradation are necessary. Despite the importance of land degradationdue to wind and water erosion in some areas of the world, the combined studyof both types of erosion in the same area receives relatively littleattention. The present study aims to create a land degradation map in termsof soil erosion caused by wind and water erosion of semi-dry land. We focuson the Lut watershed in Iran, encompassing the Lut Desert that is influencedby both monsoon rainfalls and dust storms. Dust sources are identified usingMODIS satellite images with the help of four different indices to quantifyuncertainty. The dust source maps are assessed with three machine learningalgorithms encompassing the artificial neural network (ANN), random forest (RF),and flexible discriminant analysis (FDA) to map dust sources paired withsoil erosion susceptibility due to water. We assess the accuracy of the mapsfrom the machine learning results with the area under the curve (AUC)of the receiver operating characteristic (ROC) metric. The water and aeolian soilerosion maps are used to identify different classes of land degradationrisks. The results show that 43 % of the watershed is prone to landdegradation in terms of both aeolian and water erosion. Most regions(45 %) have a risk of water erosion and some regions (7 %) a risk ofaeolian erosion. Only a small fraction (4 %) of the total area of theregion had a low to very low susceptibility for land degradation. Theresults of this study underline the risk of land degradation for in aninhabited region in Iran. Future work should focus on land degradationassociated with soil erosion from water and storms in larger regions toevaluate the risks also elsewhere.