Crop type classification in Southern Brazil: Integrating remote sensing, crop modeling and machine learning
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Crop type classification in Southern Brazil: Integrating remote sensing, crop modeling and machine learning
Authors
Keywords
-
Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 201, Issue -, Pages 107320
Publisher
Elsevier BV
Online
2022-08-22
DOI
10.1016/j.compag.2022.107320
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Early- and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach
- (2022) Chenxi Lin et al. REMOTE SENSING OF ENVIRONMENT
- Dynamic World, Near real-time global 10 m land use land cover mapping
- (2022) Christopher F. Brown et al. Scientific Data
- Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil
- (2021) Luan Pierre Pott et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions
- (2021) Dan M. Kluger et al. REMOTE SENSING OF ENVIRONMENT
- Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
- (2020) Carlos M. Souza et al. Remote Sensing
- High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data
- (2020) Walter T. Dado et al. Remote Sensing
- Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive
- (2020) Sherrie Wang et al. Scientific Data
- Exploring Google Street View with deep learning for crop type mapping
- (2020) Yulin Yan et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt
- (2020) Jillian M. Deines et al. REMOTE SENSING OF ENVIRONMENT
- Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques
- (2019) Sherrie Wang et al. REMOTE SENSING OF ENVIRONMENT
- Smallholder maize area and yield mapping at national scales with Google Earth Engine
- (2019) Zhenong Jin et al. REMOTE SENSING OF ENVIRONMENT
- Intercomparison and Performance of Maize Crop Models and Their Ensemble for Yield Simulations in Brazil
- (2019) Yury C. N. Duarte et al. International Journal of Plant Production
- APSIM Next Generation: Overcoming challenges in modernising a farming systems model
- (2018) Dean Holzworth et al. ENVIRONMENTAL MODELLING & SOFTWARE
- A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
- (2018) Yaping Cai et al. REMOTE SENSING OF ENVIRONMENT
- Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects
- (2018) David Frantz et al. REMOTE SENSING OF ENVIRONMENT
- Big earth observation time series analysis for monitoring Brazilian agriculture
- (2018) Michelle Cristina Araujo Picoli et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil
- (2017) Rafael Battisti et al. FIELD CROPS RESEARCH
- A new method for crop classification combining time series of radar images and crop phenology information
- (2017) Damian Bargiel REMOTE SENSING OF ENVIRONMENT
- SoilGrids250m: Global gridded soil information based on machine learning
- (2017) Tomislav Hengl et al. PLoS One
- Conversion of Soil pH 1:2.5 KCl and 1:2.5 H2O to 1:5 H2O: Conclusions for Soil Management, Environmental Monitoring, and International Soil Databases
- (2016) Cezary Kabała et al. POLISH JOURNAL OF ENVIRONMENTAL STUDIES
- A scalable satellite-based crop yield mapper
- (2015) David B. Lobell et al. REMOTE SENSING OF ENVIRONMENT
- A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean
- (2014) Sotirios V. Archontoulis et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Köppen's climate classification map for Brazil
- (2014) Clayton Alcarde Alvares et al. METEOROLOGISCHE ZEITSCHRIFT
- Green Leaf Area Index Estimation in Maize and Soybean: Combining Vegetation Indices to Achieve Maximal Sensitivity
- (2012) Anthony Nguy-Robertson et al. AGRONOMY JOURNAL
- Estimation of water retention and availability in soils of Rio Grande do Sul
- (2010) José Miguel Reichert et al. REVISTA BRASILEIRA DE CIENCIA DO SOLO
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now