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moquedace/README.md

Cássio Moquedace

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About

I am a Postdoctoral Researcher at ESALQ/USP (Luiz de Queiroz College of Agriculture, University of São Paulo), with a Ph.D. in Soil Science.

My research applies machine learning, deep learning, and artificial intelligence to soil science — developing reproducible workflows for spatial prediction, digital mapping, and understanding soil carbon dynamics across diverse environments and scales.

Research Focus

  • Digital soil mapping and pedometrics
  • Soil organic carbon stock mapping and dynamics
  • Machine learning and deep learning for soil property prediction
  • Remote sensing and spatial covariate engineering
  • Reproducible research pipelines in R

Selected Projects

Project Focus
ro_soil_carbon_stock High-resolution soil carbon stock mapping in the western Amazon
global_soil_respiration Soil texture and global soil respiration modeling
soc_stock_antarctica Soil organic carbon stock mapping in Antarctica
r-cnn-soil-mapping CNN/deep learning approach for digital soil mapping
funcs R functions for spatial modeling, raster workflows and prediction pipelines

Tools & Stack

machine learning · deep learning · pedometrics · digital soil mapping · remote sensing · spatial modeling · soil carbon

Soil data · Carbon dynamics · Maps · Models · Reproducible science

Pinned Loading

  1. ro_soil_carbon_stock ro_soil_carbon_stock Public

    R workflows for high-resolution soil carbon stock mapping in the western Amazon.

    R 2

  2. global_soil_respiration global_soil_respiration Public

    R code for modeling soil texture effects on future global soil respiration.

    1

  3. r-cnn-soil-mapping r-cnn-soil-mapping Public

    Convolutional neural network approach for digital soil mapping and spatial prediction.

    R 2

  4. soc_stock_antarctica soc_stock_antarctica Public

    R workflows for soil organic carbon stock mapping in Antarctic ice-free areas using digital soil mapping and machine learning.

    1

  5. funcs funcs Public

    R functions for digital soil mapping, raster prediction and spatial modeling workflows.

    R