Using PRISMA Hyperspectral Satellite Imagery and GIS Approaches for Soil Fertility Mapping (FertiMap) in Northern Morocco

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-08, Vol.14 (16), p.4080
• Main Authors: Gasmi, Anis, Gomez, Cécile, Chehbouni, Abdelghani, Dhiba, Driss, El Gharous, Mohamed
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural development; Agricultural production; Agricultural sciences; Algorithms; Feature selection; Fertilization; Fertilizers; Food security; GIS approaches; hyperspectral image; Laboratories; Life Sciences; Mapping; Nutrients; Organic matter; Organic phosphorus; Organic soils; Phosphorus; Phosphorus pentoxide; Potassium; Potassium oxides; PRISMA; random forest; Remote sensing; Satellite imagery; Satellites; Sensors; Soil fertility; soil fertility mapping; Soil mapping; Soil nutrients; Soil organic matter; Soil study; Soils; Sustainable agriculture; Sustainable development; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14164080

Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P2O5), and potassium (K2O) content over a cultivated area in Khouribga, northern Morocco. These soil nutrients were estimated using (i) the random forest (RF) algorithm based on feature selection methods, including feature subset evaluation and feature ranking methods belonging to three categories (i.e., filter, wrapper, and embedded techniques), and (ii) 107 soil samples taken from the study area. The results show that the RF-embedded method produced better predictive accuracy compared with the filter and wrapper methods. The model for SOM showed moderate accuracy (Rval2 = 0.5, RMSEP = 0.43%, and RPIQ = 2.02), whereas that for soil P2O5 and K2O exhibited low efficiency (Rval2 = 0.26 and 0.36, RMSEP = 51.07 and 182.31 ppm, RPIQ = 0.65 and 1.16, respectively). The interpolation of RF-residuals by ordinary kriging (OK) methods reached the highest predictive results for SOM (Rval2 = 0.69, RMSEP = 0.34%, and RPIQ = 2.56), soil P2O5 (Rval2 = 0.44, RMSEP = 44.10 ppm, and RPIQ = 0.75), and soil K2O (Rval2 = 0.51, RMSEP = 159.29 ppm, and RPIQ = 1.34), representing the best fitting ability between the hyperspectral data and soil nutrients. The result maps provide a spatially continuous surface mapping of the soil landscape, conforming to the pedological substratum. Finally, the hyperspectral remote sensing imagery can provide a new way for modeling and mapping soil fertility, as well as the ability to diagnose nutrient deficiencies.