Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area

Proximal and remote sensors are emerging as powerful sources of soil spectral information at an array of temporal and spatial resolutions. This study investigated clay content prediction at three spectral acquisition levels: laboratory, airborne, and spaceborne. Two approaches were tested, the use of prediction models developed with local and regional spectral libraries (52 samples for local scale and 950, 200 e 224 samples for regional scale), termed internal and external models respectively. Local soil samples (52), were collected in a smallholder area, 83 ha, located in southeastern Brazil. Spectral data in the visible (Vis), near-infrared (NIR), and shortwave infrared (SWIR) regions were acquired in the laboratory using FieldSpec 3 sensor, and the clay content was determined by sedimentation technique. Afterward, bare soil images from AISA-FENIX, Planetscope, Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) were obtained. The clay content determined in the laboratory was related to the soil spectra acquired by each of the sensors and was predicted using the Cubist regression tree algorithm. The results obtained from local spectral libraries showed good predictions using FieldSpec 3 and AISA-FENIX sensors. Landsat-8 OLI and Sentinel-2 MSI provided satisfactory results, while PlanetScope gave poor results. For the prediction using regional spectral libraries, only lab-based FieldSpec 3 sensor provided a fair prediction, while other sensors gave poor results. This study demonstrated that soil sensing is possible at any level taking into account its advantages and limitations. This approach paves the way for acquiring soil spectra for smallholder farms.

Automated summary

Proximal and remote sensors are important sources of soil spectral information, and this study explored clay content prediction at different spectral acquisition levels. Results showed that local spectral libraries performed well with FieldSpec 3 and AISA-FENIX sensors, while regional spectral libraries had poor prediction results.