Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing

Usta A.

Environmental Monitoring and Assessment, cilt.194, sa.10, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 194 Sayı: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s10661-022-10465-2
  • Dergi Adı: Environmental Monitoring and Assessment
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, MEDLINE, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Soil characteristics, Digital elevation model, Landsat image, Feature importance, Semi-arid land, Artificial neural networks, PERMANENT WILTING POINT, LAND-USE TYPE, AGGREGATE STABILITY, ORGANIC-MATTER, FIELD-CAPACITY, PEDOTRANSFER FUNCTIONS, MOISTURE PATTERNS, TEXTURE, CLAY, MANAGEMENT
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

© 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kızlarkalesi, and the Telme ponds built for agricultural irrigation in Gümüşhane-Şiran. The soil samples were obtained from two depths (0–10 cm and 10–20 cm) from 59 soil profiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R2, 0.046–4.115 for root mean square error (RMSE), 4.46–6.54 for normalized root mean square error (NRMSE), and 0.042–0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models.