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Sousa Silva, M

Silva, J.I

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Authors

Vergaray Ormeño, C.E

ten Caten, A

Alves Henriques, J.P

Maciel Reva, M.A

Sousa Silva, M

Vergaray Ormeño, C.E

ten Caten, A

Alves Henriques, J.P

Maciel Reva, M.A

Sousa Silva, M

Fonseca, A

da Silva, E.F

Bezerra, A.C

Moura, G.B

dos Anjos, J.F

Figueirôa , E.D

Coelho, G.P

de Andrade, J.P

Lopes, E

Silva, J.I

Topics

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Oral

Year

2026

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Filter results3 paper(s) found.
1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva
2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva
3. Nonlinear Modeling of Vegetation Response to Rainfall Variability in the Brazilian Semi-Arid Region Using Sentinel-2 and CHIRPS Data High climate variability in the Brazilian semi-arid region poses significant challenges to agriculture and the sustainable management of Caatinga ecosystems, requiring monitoring tools capable of anticipating vegetation responses to rainfall fluctuations. However, the spectral response of vegetation to precipitation does not always follow linear patterns and may reflect ecohydrological thresholds and water saturation effects. Sentinel-2 data were used to derive the Soil Adjusted Vegetation Index... A. Fonseca, E.F. Da Silva, A.C. Bezerra, G.B. Moura, J.F. Dos Anjos, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, J.I. Silva Download paper

Filter results3 paper(s) found.

1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models

Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions

Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

3. Nonlinear Modeling of Vegetation Response to Rainfall Variability in the Brazilian Semi-Arid Region Using Sentinel-2 and CHIRPS Data

High climate variability in the Brazilian semi-arid region poses significant challenges to agriculture and the sustainable management of Caatinga ecosystems, requiring monitoring tools capable of anticipating vegetation responses to rainfall fluctuations. However, the spectral response of vegetation to precipitation does not always follow linear patterns and may reflect ecohydrological thresholds and water saturation effects. Sentinel-2 data were used to derive the Soil Adjusted Vegetation Index... A. Fonseca, E.F. Da Silva, A.C. Bezerra, G.B. Moura, J.F. Dos Anjos, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, J.I. Silva

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