In this study a temporal analysis (2008-2017) was conducted to examine the occurrence of droughts and the spatial distribution of livestock herds in the Brazilian semi-arid region. Maps of vegetation cover, classes of normalized difference vegetation index (NDVI) and normalized difference drought index (NDDI) were used. Image processing took place on the Google Earth Engine platform along with QGIS Software (2.18). Two historical series were created to compare the variation between two herds and the dry monitoring, one called the 1st series (2008-2012) and 2nd series (2013-2017) which were compared using a paired t-test. The comparative analysis showed that the classes of extreme drought and exceptional drought covered the greatest land areas in the 2^nd series, while abnormal drought covered the greatest land area in the 1^st series. The analysis of herds, a reduction in the cattle herd and an increase in goat and sheep herds were noted. This study considers the efficacy of NDDI and NDVI indices to monitor the distribution of droughts in the Brazilian semi-arid region. The geoprocess of Sidra data enabled an analysis of the spatial distribution and temporal variation of the herds in the semi-arid region and how drought may have impacted livestock dynamics.

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers.

To effectively integrate sediment reuse in agricultural management, comprehensive knowledge of the physicochemical properties of soil and sediment on large spatial scale is essential. An alternative approach to direct cost-intensive laboratory analysis of soil and sediment is the use of VNIR-SWIR spectroscopy. This study aims to explore the potential of mapping soil and sediment properties using spectral indices and PLSR based on laboratory (ASD FieldSpec Pro spectroradiometer) and satellite image (Sentinel-2, PRISMA, and EnMAP) spectra. The study was carried out using bare soil reflectance image pixels and associated topsoil samples collected at agricultural areas and reservoir bottoms in semiarid Northeast Brazil. Pre-processing techniques were employed, including first-derivative spectra smoothened using the Savitzky–Golay (FD-SG) technique and continuum-removal. The approaches were also tested for different dataset combinations (only soil, only sediment, soil-sediment database). The combination of reflectance spectroscopy with multivariate statistical techniques resulted in an effective approach for predicting electrical conductivity, organic carbon and clay content of soils and reservoir sediments. Better accuracy can be achieved if models are built for each environmental component individually. The prediction of salinity in both soil and sediment samples was most accurate using the ratio index (RI) model combined with the FD-SG pre-processing. PLSR models were found to be more suitable for predicting soil organic carbon and clay content, especially when no spectral preprocessing was used. The study provides recommendations for soil and sediment mapping for sediment reuse in similarly challenging environments.