Ieda Del'Arco Sanches

Orcid: 0000-0003-1296-0933

According to our database1, Ieda Del'Arco Sanches authored at least 26 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
A Method for Estimating Soybean Sowing, Beginning Seed, and Harvesting Dates in Brazil Using NDVI-MODIS Data.
Remote. Sens., July, 2024

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review.
Remote. Sens., March, 2024

2023
Estimating Crop Sowing and Harvesting Dates Using Satellite Vegetation Index: A Comparative Analysis.
Remote. Sens., November, 2023

Mapping Agricultural Intensification in the Brazilian Savanna: A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2.
ISPRS Int. J. Geo Inf., July, 2023

Detecting Irrigated Croplands: A Comparative Study With Segment Anything Model And Region-Growing Algorithm.
Proceedings of the XXIV Brazilian Symposium on Geoinformatics, 2023

Assessing The Influence Of Borders And Roads On The Segmentation Of Rice Fields: A Case Study.
Proceedings of the XXIV Brazilian Symposium on Geoinformatics, 2023

2022

Hierarchical Classification of Soybean in the Brazilian Savanna Based on Harmonized Landsat Sentinel Data.
Remote. Sens., 2022

Leaf Spectra Changes of Plants Grown in Soils Pre- and Post-Contaminated with Petroleum Hydrocarbons.
Remote. Sens., 2022

2020
Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review.
Remote. Sens., 2020

SAR Data for Land Use Land Cover Classification in a Tropical Region with Frequent Cloud Cover.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020

Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020

2019
Combining Deep Learning and Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR Image Sequences.
Remote. Sens., 2019

Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series.
Int. J. Appl. Earth Obs. Geoinformation, 2019

Comparing Phenometrics Extracted From Dense Landsat-Like Image Time Series for Crop Classification.
Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

Potential of Using Sentinel-1 Data to Distinguish Targets in Remote Sensing Images.
Proceedings of the Computational Science and Its Applications - ICCSA 2019, 2019

2018
Campo Verde Database: Seeking to Improve Agricultural Remote Sensing of Tropical Areas.
IEEE Geosci. Remote. Sens. Lett., 2018

Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region.
Int. J. Appl. Earth Obs. Geoinformation, 2018

Mapping croplands, cropping patterns, and crop types using MODIS time-series data.
Int. J. Appl. Earth Obs. Geoinformation, 2018

2017
A Comparative Analysis of Deep Learning Techniques for Sub-Tropical Crop Types Recognition from Multitemporal Optical/SAR Image Sequences.
Proceedings of the 30th SIBGRAPI Conference on Graphics, Patterns and Images, 2017

Spatial-temporal conditional random field based model for crop recognition in tropical regions.
Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017

2016
Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas.
Remote. Sens., 2016

Assessment of a multi-sensor approach for noise removal on Landsat-8 OLI time series using CBERS-4 MUX data to improve crop classification based on phenological features.
Proceedings of the XVII Brazilian Symposium on Geoinformatics, 2016

2015
Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil.
Remote. Sens., 2015

2011
Hidden Markov Models for crop recognition in remote sensing image sequences.
Pattern Recognit. Lett., 2011

2008
Crop Type Recognition Based on Hidden Markov Models of Plant Phenology.
Proceedings of the SIBGRAPI 2008, 2008


  Loading...