Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics
Remote sensing has revolutionized crop mapping and monitoring, providing valuable insights for sustainable agricultural practices. However, successfully implementing remote sensing-based crop type identification in tropical regions remains challenging. Unlike temperate regions, tropical areas benefi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002638 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849240761120325632 |
|---|---|
| author | Laura Elena Cué La Rosa Dario Augusto Borges Oliveira Raul Queiroz Feitosa |
| author_facet | Laura Elena Cué La Rosa Dario Augusto Borges Oliveira Raul Queiroz Feitosa |
| author_sort | Laura Elena Cué La Rosa |
| collection | DOAJ |
| description | Remote sensing has revolutionized crop mapping and monitoring, providing valuable insights for sustainable agricultural practices. However, successfully implementing remote sensing-based crop type identification in tropical regions remains challenging. Unlike temperate regions, tropical areas benefit from favorable weather conditions that support diverse land management practices, resulting in complex crop dynamics that are difficult to model. Additionally, frequent cloud cover in tropical regions limits the use of optical data during extended periods of the year, making SAR (Synthetic Aperture Radar) a more attractive alternative. Traditional models like Conditional Random Fields (CRFs) have been useful in classifying crop types using spatial and temporal contexts. However, these models often use fixed inputs, optimizing them towards a specific task, and fail to consider the CRFs’ feedback for end-to-end learning features and the spatiotemporal dependencies. This work addresses this gap by introducing an end-to-end hybrid model that combines deep learning with CRFs to incorporate prior knowledge-based modeling of crop dynamics. The proposed framework integrates a backbone encoder–decoder to capture spatial and temporal contexts, with a CRF block that models temporal dynamics by accounting for label dependencies between adjacent epochs. This block facilitates the integration of both data-driven and expert-domain temporal constraints, allowing the framework to adapt to local agricultural practices and enhance crop dynamics modeling. We evaluate the framework using multi-temporal SAR image sequences from two municipalities in Brazil, comparing the effectiveness of learned versus prior-knowledge temporal constraints. The results show improvements of up to 30% in per-class F1 score and 12% in average F1 score compared to a baseline model that excludes temporal dependencies. These findings highlight the value of incorporating prior knowledge-driven temporal constraints into crop mapping models. |
| format | Article |
| id | doaj-art-dc2b2cb074744021af0ae81c8711e3d2 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-dc2b2cb074744021af0ae81c8711e3d22025-08-20T04:00:27ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-08-0114210461610.1016/j.jag.2025.104616Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamicsLaura Elena Cué La Rosa0Dario Augusto Borges Oliveira1Raul Queiroz Feitosa2Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, 6708 PB Wageningen, The Netherlands; Corresponding author.School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro 22250, BrazilDepartment of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, 22451900, BrazilRemote sensing has revolutionized crop mapping and monitoring, providing valuable insights for sustainable agricultural practices. However, successfully implementing remote sensing-based crop type identification in tropical regions remains challenging. Unlike temperate regions, tropical areas benefit from favorable weather conditions that support diverse land management practices, resulting in complex crop dynamics that are difficult to model. Additionally, frequent cloud cover in tropical regions limits the use of optical data during extended periods of the year, making SAR (Synthetic Aperture Radar) a more attractive alternative. Traditional models like Conditional Random Fields (CRFs) have been useful in classifying crop types using spatial and temporal contexts. However, these models often use fixed inputs, optimizing them towards a specific task, and fail to consider the CRFs’ feedback for end-to-end learning features and the spatiotemporal dependencies. This work addresses this gap by introducing an end-to-end hybrid model that combines deep learning with CRFs to incorporate prior knowledge-based modeling of crop dynamics. The proposed framework integrates a backbone encoder–decoder to capture spatial and temporal contexts, with a CRF block that models temporal dynamics by accounting for label dependencies between adjacent epochs. This block facilitates the integration of both data-driven and expert-domain temporal constraints, allowing the framework to adapt to local agricultural practices and enhance crop dynamics modeling. We evaluate the framework using multi-temporal SAR image sequences from two municipalities in Brazil, comparing the effectiveness of learned versus prior-knowledge temporal constraints. The results show improvements of up to 30% in per-class F1 score and 12% in average F1 score compared to a baseline model that excludes temporal dependencies. These findings highlight the value of incorporating prior knowledge-driven temporal constraints into crop mapping models.http://www.sciencedirect.com/science/article/pii/S1569843225002638Crop mappingConditional random fieldsDeep learningHybrid modelMulti-temporal classificationSynthetic aperture radar |
| spellingShingle | Laura Elena Cué La Rosa Dario Augusto Borges Oliveira Raul Queiroz Feitosa Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics International Journal of Applied Earth Observations and Geoinformation Crop mapping Conditional random fields Deep learning Hybrid model Multi-temporal classification Synthetic aperture radar |
| title | Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics |
| title_full | Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics |
| title_fullStr | Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics |
| title_full_unstemmed | Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics |
| title_short | Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics |
| title_sort | harnessing deep learning and crf for prior knowledge modeling of crop dynamics |
| topic | Crop mapping Conditional random fields Deep learning Hybrid model Multi-temporal classification Synthetic aperture radar |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002638 |
| work_keys_str_mv | AT lauraelenacuelarosa harnessingdeeplearningandcrfforpriorknowledgemodelingofcropdynamics AT darioaugustoborgesoliveira harnessingdeeplearningandcrfforpriorknowledgemodelingofcropdynamics AT raulqueirozfeitosa harnessingdeeplearningandcrfforpriorknowledgemodelingofcropdynamics |