Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r², square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm⁻³, and 1.36, respectively.

Imaging spectroscopy is a proven technology for identifying and mapping the constituents of soils based on their spectral signatures, the variation of their reflectance or emissivity across different wavelengths, in the visible and infrared bands. This work aims to correlate soil data obtained in the laboratory by two different hyperspectral sensors, AisaFENIX and spectroradiometer, in different bands of the electromagnetic spectrum. There is a lack of prior work examining the association between these two sensors, especially in controlled laboratory settings. Soils were sampled in Mandaguaçu, Paraná, an area of approximately 200 ha, totaling 207 georeferenced samples. The soil reflectance laboratory data were obtained using the Fieldspec 3 Jr spectroradiometer sensor, which collects a spectral range of 350-2,500 nm (Vis-NIR), and the reflectance values of the hyperspectral images were acquired with the AisaFENIX sensor, which collects a spectral range of 350-2,500 nm. The correlation between the sensors was satisfactory (R2 0.97) in the corresponding iron range (500-640 nm), and for the corresponding bands of kaolinite (2,200 nm), gibbsite (2,265 nm), water (1,400 nm) and water (1,900 nm), the R2 values found were 0.61, 0.65, 0.63 and 0.64 respectively. The lowest correlation coefficients found were for the bands of organic carbon (600-1100 nm), goethite (480 nm), hematite (530 nm) and specific goethite (450-480 nm), with R2 values less than 0.41. The t-test showed that all the variables had significant positive correlations (p <0.01), indicating that there is an excellent agreement between the two sensors. The SAM and ASDS indexes showed that the spectra obtained by the spectroradiometer and AisaFENIX sensors were similar. The methodology adopted in this work of studying the agreement between the data obtained by these sensors has significant potential and would be promising for future work to build upon.

The use of remote sensing is increasing in agriculture and this raises questions about its efficiency over other usual methods. Thus, the purposes of this study were to compare methodologies for soil mapping, using field samplings and spectral data (from laboratory and from a simulated Landsat-TM), and to estimate their correlation. The soil samples were collected in a wetland with a great variety of soil classes. The distribution of soil classes in the maps was based on independence analysis by the Chi-square. Ten soil classes were determined in the study area, 6 in the first category level. The map of laboratory spectral data showed low correlation with conventional data. The map of spectral data that simulated wavelenghts corresponding the spectral bands of Landsat-TM sensor showed the same behavior of the previous map, with lower correlation with the conventional data. Thus, we verified that the mapping of paddy soils with spectral data shows low correlation with conventional data, however, still rather positive.