Pith. sign in

REVIEW 2 major objections 2 minor 115 references

From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping

T0 review · 2 major / 2 minor · reviewed 2026-05-19 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Fine-scale time-series preprocessing with Transformer models delivers top performance for large-scale crop mapping in both supervised and transfer settings.

desk verdict This paper runs useful new experiments comparing preprocessing and models for satellite crop mapping and finds fine-scale intervals with Transformers on top, but the claims need checks for CDL label noise and site limits. read the letter →

arxiv 2507.12590 v2 submitted 2025-07-16 cs.CV cs.LG

Judy Long , Tao Liu , Sean Alexander Woznicki , Miljana Marković , Oskar Marko , Molly Sears This is my paper · ORCID
classification cs.CVcs.LG
keywords cropmappingremotesensingpixel-wiseclassificationtransferlearningtimeseriespreprocessingtransformermodelssuperviseddomainadaptation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper reviews and experimentally compares methods for preparing satellite time series and selecting models to map crop types at the pixel level over large areas. It tests six preprocessing approaches and eleven classification models on data from five agricultural sites using Landsat imagery and trusted labels. The central finding is that preprocessing with fine time intervals combined with Transformer models achieves the best results whether training directly on the target area or adapting from other regions. The work also shows that the best strategy shifts with the amount of labeled data available, favoring supervised methods when labels are plentiful and transfer learning when they are scarce.

What carries the argument

Comparative evaluation of time-series generation methods and pixel-wise classification models for crop type identification from multi-temporal remote sensing data.

What would settle it

Applying the identified optimal preprocessing and models to an additional agricultural area with substantially different crop mixes, climate, or imaging conditions and finding markedly reduced accuracy would challenge the claim of consistent optimality.

Watch

Extended reading notes

Core claim

Through systematic experiments, the study demonstrates that fine-scale interval preprocessing paired with Transformer models consistently delivered optimal performance for both supervised and transferable workflows across the tested sites. Random forest models offer rapid training with competitive accuracy in conventional supervised learning and direct transfer to similar domains. Transfer learning techniques, particularly unsupervised domain adaptation for homogeneous classes and fine-tuning for diverse scenarios, enhance adaptability, with the choice depending on the availability of labeled samples.

Load-bearing premise

The five agricultural sites along with CDL trusted pixels and field surveys supply representative and accurate ground-truth labels that hold for conditions outside the tested regions and data sources.

Editorial extensions

If this is right

  • Supervised training with sufficient samples yields more accurate and generalizable crop maps than transfer approaches.
  • Matching the transfer learning technique to the degree of domain shift improves results when labeled data is limited.
  • Random forest remains a practical choice for quick implementation in similar domain transfers.
  • Workflow performance varies with sample size, variable combinations, and crop class homogeneity.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The results imply that temporal resolution in preprocessing may be more critical than model complexity in many remote sensing classification tasks.
  • Extending the approach to other land cover types or different satellite sensors could test the robustness of these optimal configurations.
  • Practitioners might prioritize acquiring high-quality labels from a few representative sites over broad but sparse coverage.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Request a human review

A listed scientist reviews the paper for a fee and the review publishes here regardless of verdict. See the reviewers or get listed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript presents the first comprehensive review and experimental comparison of pixel-wise crop mapping workflows using Landsat 8 time-series data. It evaluates six satellite image preprocessing methods for time-series generation and eleven supervised classification models across five agricultural sites, with ground truth from CDL trusted pixels and field surveys. The study further examines the impact of training sample sizes, variable combinations, and transfer learning techniques (including UDA and fine-tuning) for varying domain shifts. The central claim is that fine-scale interval preprocessing combined with Transformer models consistently yields optimal performance for both supervised and transferable workflows, while RF is competitive for rapid training in similar domains; workflow choice depends on labeled sample availability, with supervised training preferred above a threshold and matched transfer learning below it. All code is released publicly.

Significance. If the empirical rankings hold under more rigorous validation, the work provides actionable guidance for large-scale crop mapping practitioners by identifying effective preprocessing-model combinations and transfer strategies. The public code release is a clear strength that supports reproducibility in remote sensing and agricultural CV applications.

major comments (2)
  1. [Abstract and Results] Abstract and Results sections: The headline claim that fine-scale interval preprocessing paired with Transformer models 'consistently delivered optimal performance' for supervised and transferable workflows is not accompanied by statistical significance tests (e.g., McNemar or paired t-tests on performance deltas), error bars from multiple random seeds, or hyperparameter fairness controls. This undermines confidence in the method rankings across the six preprocessing pipelines and eleven models.
  2. [Methods] Methods (Ground Truth and Experimental Setup): The evaluation depends on CDL trusted pixels plus field surveys without any reported sensitivity analysis or uncertainty propagation for known CDL label noise (typically 5-15% commission/omission errors). If these errors are non-uniform across classes or sites, the relative optimality of preprocessing and models could shift, making this a load-bearing concern for the cross-site claims.
minor comments (2)
  1. [Abstract] Abstract: Explicitly enumerate the six preprocessing methods and eleven models to improve immediate readability of the experimental scope.
  2. [Throughout] Notation and figures: Ensure consistent terminology for transfer learning components (e.g., UDA vs. fine-tuning) and improve clarity of any performance tables or plots showing cross-site results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below and outline specific revisions to strengthen the empirical claims and robustness of the analysis.

read point-by-point responses

  1. Referee: [Abstract and Results] Abstract and Results sections: The headline claim that fine-scale interval preprocessing paired with Transformer models 'consistently delivered optimal performance' for supervised and transferable workflows is not accompanied by statistical significance tests (e.g., McNemar or paired t-tests on performance deltas), error bars from multiple random seeds, or hyperparameter fairness controls. This undermines confidence in the method rankings across the six preprocessing pipelines and eleven models.

    Authors: We agree that formal statistical testing and variability reporting would increase confidence in the reported rankings. In the revised version we will (i) run each model with three independent random seeds and report mean performance with standard deviation error bars, (ii) apply paired t-tests (or McNemar tests for classification accuracy) to the key performance deltas between the top-ranked and runner-up pipelines, and (iii) document that hyper-parameter search budgets were equalized across models using the same Optuna configuration and early-stopping protocol. These additions will be placed in a new subsection of Results and referenced in the Abstract. revision: yes

  2. Referee: [Methods] Methods (Ground Truth and Experimental Setup): The evaluation depends on CDL trusted pixels plus field surveys without any reported sensitivity analysis or uncertainty propagation for known CDL label noise (typically 5-15% commission/omission errors). If these errors are non-uniform across classes or sites, the relative optimality of preprocessing and models could shift, making this a load-bearing concern for the cross-site claims.

    Authors: We acknowledge that CDL label noise constitutes a potential confounding factor. While the 'trusted pixel' filtering already removes low-confidence CDL labels, we did not quantify its residual impact. In revision we will add a sensitivity experiment that injects controlled label noise (5 %, 10 %, 15 %) at both class-balanced and class-imbalanced rates, re-train the top three pipelines on each perturbed label set, and report how the relative ordering of preprocessing–model combinations changes across the five sites. Results will appear in a new subsection of Methods and a supplementary figure. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely empirical comparison with held-out evaluations

full rationale

The paper reports results from systematic experiments comparing six preprocessing pipelines and eleven models across five agricultural sites using Landsat 8 imagery and CDL/field-survey labels. No mathematical derivations, first-principles predictions, or equations are present that could reduce to fitted parameters or self-citations by construction. All optimality claims rest on direct performance measurements on held-out sites rather than any definitional or fitted-input loop, satisfying the self-contained benchmark criterion.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is an empirical benchmarking study rather than a theoretical derivation. No mathematical free parameters or invented physical entities are introduced. Model and preprocessing selections are drawn from existing literature and tested experimentally.

how reviews work

0 comments
Cite this review

Pith. "Pith review of From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping." pith.science (2026). https://pith.science/paper/2507.12590

@misc{pith2026250712590,
  author       = {Pith},
  title        = {Pith review of: From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2507.12590}},
  note         = {Machine review of arXiv:2507.12590}
}
read the original abstract

Crop mapping involves identifying and classifying crop types using spatial data, primarily derived from remote sensing imagery. This study presents the first comprehensive review of large-scale, pixel-wise crop mapping workflows, encompassing both conventional supervised methods and emerging transfer learning approaches. To identify the optimal time-series generation approaches and supervised crop mapping models, we conducted systematic experiments, comparing six widely adopted satellite image-based preprocessing methods, alongside eleven supervised pixel-wise classification models. Additionally, we assessed the synergistic impact of varied training sample sizes and variable combinations. Moreover, we identified optimal transfer learning techniques for different magnitudes of domain shift. The evaluation of optimal methods was conducted across five diverse agricultural sites. Landsat 8 served as the primary satellite data source. Labels come from CDL trusted pixels and field surveys. Our findings reveal three key insights. First, fine-scale interval preprocessing paired with Transformer models consistently delivered optimal performance for both supervised and transferable workflows. RF offered rapid training and competitive performance in conventional supervised learning and direct transfer to similar domains. Second, transfer learning techniques enhanced workflow adaptability, with UDA being effective for homogeneous crop classes while fine-tuning remains robust across diverse scenarios. Finally, workflow choice depends heavily on the availability of labeled samples. With a sufficient sample size, supervised training typically delivers more accurate and generalizable results. Below a certain threshold, transfer learning that matches the level of domain shift is a viable alternative to achieve crop mapping. All code is publicly available to encourage reproducibility practice.

Figures

Figures reproduced from arXiv: 2507.12590 by the authors.

Figure 1
Figure 1. From 2010 to 2024, the number of studies integrating remote sensing with crop mapping increased by approximately 20% annually. From 2016 onward, the use of transfer learning in this field grew at a geometric mean annual rate of 81%. Data were retrieved from Google Scholar using two sets of search terms: (a) "Remote Sensing" AND "Crop Mapping" and (b) "Remote Sensing" AND "Crop Mapping" AND "Transfer Learning." Abbre… view at source ↗
Figure 2
Figure 2. Geographic distribution of five study sites. Sites A, B, and C are located within the U.S. Corn and Soybean Belt and serve as both source and target sites in temporal and sensor transfer scenarios, denoted by blue borders. In the spatial transfer scenario, Site A is designated as the source site, while Sites B, D, and E are the target sites, denoted by orange borders. Site D lies in a transitional zone between the c… view at source ↗
Figure 3
Figure 3. Geographic identifiers, valid pixel distribution, time series length, and crop-specific sample composition for each study site from April 1 to October 1, 2023. The footprint location column lists the geographic identifiers for each site, including Landsat path/row for U.S. regions and the province name for the Serbian site. Valid pixel count refers to the number of pixels with reliable surface reflectance in each ob… view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Experimental design overview for evaluating pixel-wise crop mapping workflows. Crop maps were produced either via fully supervised learning or transfer learning. The supervised workflow comprises sequential modules: (1) time series reconstruction, (2) sample generation…
Figure 5
Figure 5. Figure 5: Six image-wide preprocessing methods are exemplified using NIR reflectance from a trusted sample at Site A. The grey background represents the target crop growing season defined in 2023. Observation (dots) selection depends on whether reconstruction relies on native pa…
Figure 6
Figure 6. Figure 6: Architecture illustration of pixel-based models. (a) Random Forest. (b) Transformer. (c) Single-layer RNN architecture (RNN, LSTM, and GRU). (d) Bidirectional RNN architecture (Bi-RNN, Bi-LSTM, and Bi-GRU). (e) Bidirectional RNN with an attention layer architecture (At…
Figure 7
Figure 7. Figure 7: Overview of the DANN framework used for unsupervised domain adaptation. The model processes time series from both source and target domains using a shared deep feature extractor. The resulting feature representations are passed to two branches: a label predictor traine…
Figure 8
Figure 8. Figure 8: Processed time series of six Landsat 8 optical bands and two Sentinel-1 polarization bands for Site A in 2023. The x-axis shows the time in the format of either DOY or time steps, and the y-axis represents the reflectance value. (a) Raw reflectance values from the grow…
Figure 9
Figure 9. Figure 9: Classification accuracy and computational cost of six preprocessing methods and eleven pixel-wise classification models at three Corn-Soybean Belt sites. (a) Overall accuracy of six preprocessing methods combined with five representative models, sorted by ascending ave…
Figure 10
Figure 10. Figure 10: Six sample sizes and four variable combinations were evaluated on optimal RF and Transformer workflows. (a) Boxplot of 10-fold cross-validation overall accuracies. Accuracy improves and becomes more stable as the training sample size increases, though gains diminish a…
Figure 11
Figure 11. Figure 11: Comparison of model transferability across temporal, sensor, and spatial domains. Each grouped bar plot summarizes the overall accuracy of eleven pixel-wise classification models combined with three optimal preprocessing methods. Colored bars represent the OA of trans…
Figure 12
Figure 12. Figure 12: Confusion matrices for target-site test sets under (a) temporal transfer at Site C and (b) spatial transfer to Site E, comparing single-source, multi-source, and fully supervised models. Fully supervised Transformer models were trained on 9,000 labeled samples from ea…
Figure 13
Figure 13. Figure 13: Comparison of per-class producer’s accuracy for direct transfer, DANN, and complete fine-tuning workflows across spatial transfer scenarios. Rows represent spatial transfer pairs with varying levels of domain shifts (A to B: low, A to D: moderate, A to E: challenging.…
Figure 14
Figure 14. Figure 14: Difference maps and prediction maps at Site B, generated using direct transfer, DANN-based transfer learning, and fully supervised learning workflows (zoomed area covers ~225 km2 ). Under low domain shift, both direct transfer and DANN-based models can achieve high-ac…
Figure 15
Figure 15. Figure 15: Training loss and validation accuracy curves for four fine-tuning strategies, including basic complete fine-tuning (R1), class-weighted sampling and loss (R2), balanced subset undersampling (R3), and two-stage fine-tuning (R4). The pre-trained models used training sam…
Figure 16
Figure 16. Figure 16: Per-class producer’s accuracy from single-source and multi-source domain fine-tuning checkpoints. Multi-source domain fine-tuning did not yield clear advantages over single-source approaches under large domain shifts, consistent with the findings in direct transfer re…
Figure 17
Figure 17. Figure 17: Difference maps and prediction maps at Site E, generated using direct transfer, fine-tuning R3-based transfer learning, and fully supervised learning workflows (zoomed area covers ~225 km2 ). The reference map was obtained from the BioSense Institute with a 10-m spati…
Figure 18
Figure 18. Figure 18: Workflow choice depends primarily on labeled sample availability, while transfer learning strategy selection is further influenced by the magnitude of domain shift. The sample size reflects the availability of labeled data at the target site, where the threshold betwe…
Figure 19
Figure 19. Figure 19: Overall accuracy from 10-fold cross-validation for RF and Transformer models across three preprocessing methods and two sample types at Sites A, B, and C. Each violin plot summarizes 30 accuracy values per model, comprising results from three preprocessing strategies …
Figure 20
Figure 20. Figure 20: Comparison of model predictions using original CDL versus trusted samples for a 500-by-500-pixel subset (~225 km2 ) at Site A. RF trained on original CDL and Transformer trained on trusted samples are shown alongside the 2023 CDL reference map. Transformer predictions…
Figure 21
Figure 21. Figure 21: NDVI and MSI time series curves for corn and soybean across spatial, temporal, and sensor transfer scenarios, indicating seasonal changes in vegetation greenness and water stress. Shaded areas represent one standard deviation for all scenarios except Site E, where the…
Figure 22
Figure 22. Figure 22: Comparison of transfer learning approaches across spatial domains in 2023. The grouped bar plot summarizes the overall accuracy of eleven pixel-wise classification models combined with three preprocessing methods. Grey bars indicate the test accuracy of the upper-boun…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

115 extracted references · 115 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

    work page

  2. [2]

    , author Kumar, A

    author Aleissaee, A.A. , author Kumar, A. , author Anwer, R.M. , author Khan, S. , author Cholakkal, H. , author Xia, G.S. , author Khan, F.S. , year 2023 . title Transformers in remote sensing: A survey . journal Remote Sensing volume 15 , pages 1860

    work page 2023

  3. [3]

    Neural Machine Translation by Jointly Learning to Align and Translate

    author Bahdanau, D. , year 2014 . title Neural machine translation by jointly learning to align and translate . journal arXiv preprint arXiv:1409.0473

    work page Pith review arXiv 2014

  4. [4]

    , author Yang, Z

    author Boryan, C. , author Yang, Z. , author Mueller, R. , author Craig, M. , year 2011 . title Monitoring us agriculture: the us department of agriculture, national agricultural statistics service, cropland data layer program . journal Geocarto International volume 26 , pages 341--358

    work page 2011

  5. [5]

    , author Mueller, R

    author Boudiaf, M. , author Mueller, R. , author Ben Ayed, I. , author Bertinetto, L. , year 2022 . title Parameter-free online test-time adaptation , in: booktitle Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. pages 8344--8353

    work page 2022

  6. [6]

    Random Forests

    author Breiman, L. , year 2001 . title Random forests . journal Mach. Learn. volume 45 , pages 5–32 . https://doi.org/10.1023/A:1010933404324, :10.1023/a:1010933404324

    work page doi:10.1023/a:1010933404324 2001

  7. [7]

    Remote Sensing of Environment 210, 35–47 (2018) https://doi.org/10.1016/j.rse.2018.02.045

    author Cai, Y. , author Guan, K. , author Peng, J. , author Wang, S. , author Seifert, C. , author Wardlow, B. , author Li, Z. , year 2018 . title A high-performance and in-season classification system of field-level crop types using time-series landsat data and a machine learning approach . journal Remote Sensing of Environment volume 210 , pages 35--47 ...

    work page doi:10.1016/j.rse.2018.02.045 2018

  8. [8]

    , year 1997

    author Caruana, R. , year 1997 . title Multitask learning . journal Machine learning volume 28 , pages 41--75

    work page 1997

Show all 115 references
  1. [9]

    , author Chen, S

    author Chen, J. , author Chen, S. , author Fu, R. , author Li, D. , author Jiang, H. , author Wang, C. , author Peng, Y. , author Jia, K. , author Hicks, B.J. , year 2022 . title Remote sensing big data for water environment monitoring: current status, challenges, and future p...

    2022

  2. [10]

    , author Cao, R

    author Chen, Y. , author Cao, R. , author Chen, J. , author Liu, L. , author Matsushita, B. , year 2021 . title A practical approach to reconstruct high-quality landsat ndvi time-series data by gap filling and the savitzky–golay filter . journal ISPRS Journal of Photogrammetry...

    2021 doi

  3. [11]

    , author Song, X

    author Chen, Y. , author Song, X. , author Wang, S. , author Huang, J. , author Mansaray, L.R. , year 2016 . title Impacts of spatial heterogeneity on crop area mapping in canada using modis data . journal ISPRS Journal of Photogrammetry and Remote Sensing volume 119 , pages 451--461

    2016

  4. [12]

    , year 2014

    author Cho, K. , year 2014 . title On the properties of neural machine translation: Encoder-decoder approaches . journal arXiv preprint arXiv:1409.1259

    2014 arXiv

  5. [13]

    , author Gulcehre, C

    author Chung, J. , author Gulcehre, C. , author Cho, K. , author Bengio, Y. , year 2014 . title Empirical evaluation of gated recurrent neural networks on sequence modeling . journal arXiv [cs.NE] http://arxiv.org/abs/1412.3555

    2014 arXiv

  6. [14]

    , author Belgiu, M

    author Csillik, O. , author Belgiu, M. , author Asner, G.P. , author Kelly, M. , year 2019 . title Object-based time-constrained dynamic time warping classification of crops using sentinel-2 . journal Remote sensing volume 11 , pages 1257

    2019

  7. [15]

    , author Lavhale, P

    author Diwate, P. , author Lavhale, P. , author Pande, C.B. , author Sammen, S.S. , author Refadah, S.S. , author Khan, M.Y.A. , author Elkhrachy, I. , author Salem, A. , year 2025 . title Evaluating flood dynamics and effects in nagpur city using remote sensing and shannon’s ...

    2025

  8. [16]

    , author Pesendorfer, V

    author Eilers, P.H. , author Pesendorfer, V. , author Bonifacio, R. , year 2017 . title Automatic smoothing of remote sensing data , in: booktitle 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) , pp. pages 1--3 . :10.1109/Mul...

    2017 doi

  9. [17]

    , year 1990

    author Elman, J.L. , year 1990 . title Finding structure in time . journal Cognitive science volume 14 , pages 179--211

    1990

  10. [18]

    title Sentinel-1 sar grd product

    author ESA , year 2025 . title Sentinel-1 sar grd product . howpublished https://browser.dataspace.copernicus.eu/ . note Accessed: 2025-02-05

    2025

  11. [19]

    , author Xia, L

    author Fan, L. , author Xia, L. , author Yang, J. , author Sun, X. , author Wu, S. , author Qiu, B. , author Chen, J. , author Wu, W. , author Yang, P. , year 2024 . title A temporal-spatial deep learning network for winter wheat mapping using time-series sentinel-2 imagery . ...

    2024

  12. [20]

    , author Gao, M

    author Feng, F. , author Gao, M. , author Liu, R. , author Yao, S. , author Yang, G. , year 2023 . title A deep learning framework for crop mapping with reconstructed sentinel-2 time series images . journal Computers and Electronics in Agriculture volume 213 , pages 108227 . h...

    2023 doi

  13. [21]

    , author Rollin, P

    author Fisette, T. , author Rollin, P. , author Aly, Z. , author Campbell, L. , author Daneshfar, B. , author Filyer, P. , author Smith, A. , author Davidson, A. , author Shang, J. , author Jarvis, I. , year 2013 . title Aafc annual crop inventory , in: booktitle 2013 Second i...

    2013

  14. [22]

    , author Shen, R

    author Fu, Y. , author Shen, R. , author Song, C. , author Dong, J. , author Han, W. , author Ye, T. , author Yuan, W. , year 2023 . title Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm . journal Science of Remote Sens...

    2023

  15. [23]

    , author Vatsavai, R.R

    author Gadiraju, K.K. , author Vatsavai, R.R. , year 2020 . title Comparative analysis of deep transfer learning performance on crop classification , in: booktitle Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data , pp. pages 1--8

    2020

  16. [24]

    , author Lempitsky, V

    author Ganin, Y. , author Lempitsky, V. , year 2015 . title Unsupervised domain adaptation by backpropagation , in: booktitle International conference on machine learning , organization PMLR . pp. pages 1180--1189

    2015

  17. [25]

    , author Ustinova, E

    author Ganin, Y. , author Ustinova, E. , author Ajakan, H. , author Germain, P. , author Larochelle, H. , author Laviolette, F. , author Marchand, M. , author Lempitsky, V. , year 2016 . title Domain-adversarial training of neural networks . journal arXiv [stat.ML] http://arxi...

    2016 arXiv

  18. [26]

    , year 1996

    author Gao, B.C. , year 1996 . title Ndwi—a normalized difference water index for remote sensing of vegetation liquid water from space . journal Remote sensing of environment volume 58 , pages 257--266

    1996

  19. [27]

    , author Markovic, S

    author Gavrilov, M. , author Markovic, S. , author Jarad, A. , author Korac, V. , year 2015 . title The analysis temperature trends in vojvodina (serbia) from 1949 to 2006 . journal Thermal Science volume 19 , pages 339--350 . :10.2298/TSCI150207062G

    2015 doi

  20. [28]

    , author Gritz † , Y

    author Gitelson, A.A. , author Gritz † , Y. , author Merzlyak, M.N. , year 2003 . title Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves . journal Journal of Plant Physiolog...

    2003 doi

  21. [29]

    , author Hancher, M

    author Gorelick, N. , author Hancher, M. , author Dixon, M. , author Ilyushchenko, S. , author Thau, D. , author Moore, R. , year 2017 . title Google earth engine: Planetary-scale geospatial analysis for everyone . journal Remote sensing of Environment volume 202 , pages 18--27

    2017

  22. [31]

    , author Nendel, C

    author Griffiths, P. , author Nendel, C. , author Hostert, P. , year 2019 b. title Intra-annual reflectance composites from sentinel-2 and landsat for national-scale crop and land cover mapping . journal Remote Sensing of Environment volume 220 , pages 135--151 . https://www.s...

    2019 doi

  23. [32]

    , author Clark, M.L

    author Hakkenberg, C.R. , author Clark, M.L. , author Bailey, T. , author Burns, P. , author Goetz, S.J. , year 2024 . title Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions . journal Communications E...

    2024

  24. [33]

    , author Schmidhuber, J

    author Hochreiter, S. , author Schmidhuber, J. , year 1997 . title Long short-term memory . journal Neural Comput. volume 9 , pages 1735–1780 . https://doi.org/10.1162/neco.1997.9.8.1735, :10.1162/neco.1997.9.8.1735

    1997 doi

  25. [34]

    , author Liu, H

    author Huete, A. , author Liu, H. , author Batchily, K. , author Van Leeuwen, W. , year 1997 . title A comparison of vegetation indices over a global set of tm images for eos-modis . journal Remote sensing of environment volume 59 , pages 440--451

    1997

  26. [35]

    title Agricultural Water Management in Serbia

    author IWSU , year 2023 . title Agricultural Water Management in Serbia . type Technical Report . IWSU. https://www.iwsu.com.tr/assets/pdf/technicalreports/Tec_Rep_IV_Ag_Water_Mng_Serbia

    2023

  27. [36]

    , author Shu, Y

    author Jiang, J. , author Shu, Y. , author Wang, J. , author Long, M. , year 2022 . title Transferability in deep learning: A survey . http://arxiv.org/abs/2201.05867 arXiv:2201.05867

    2022

  28. [37]

    , author Pradhan, B

    author Joshi, A. , author Pradhan, B. , author Gite, S. , author Chakraborty, S. , year 2023 . title Remote-sensing data and deep-learning techniques in crop mapping and yield prediction: A systematic review . journal Remote Sensing volume 15 . https://www.mdpi.com/2072-4292/1...

    2023 doi

  29. [38]

    Chandrasekar, M

    author K. Chandrasekar, M. V. R. Sesha Sai, P.S.R. , author Dwevedi, R.S. , year 2010 . title Land surface water index (lswi) response to rainfall and ndvi using the modis vegetation index product . journal International Journal of Remote Sensing volume 31 , pages 3987--4005 ....

    2010 doi

  30. [39]

    , author Sahajpal, R

    author Kerner, H.R. , author Sahajpal, R. , author Pai, D.B. , author Skakun, S. , author Puricelli, E. , author Hosseini, M. , author Meyer, S. , author Becker-Reshef, I. , year 2022 . title Phenological normalization can improve in-season classification of maize and soybean:...

    2022

  31. [40]

    , author Pham, H

    author Khaki, S. , author Pham, H. , author Wang, L. , year 2021 . title Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning . journal Scientific Reports volume 11 , pages 11132

    2021

  32. [41]

    , author Matin, M.A

    author Khanal, N. , author Matin, M.A. , author Uddin, K. , author Poortinga, A. , author Chishtie, F. , author Tenneson, K. , author Saah, D. , year 2020 . title A comparison of three temporal smoothing algorithms to improve land cover classification: A case study from nepal ...

    2020

  33. [42]

    , author Shariff, A.R.M

    author Kok, Z.H. , author Shariff, A.R.M. , author Alfatni, M.S.M. , author Khairunniza-Bejo, S. , year 2021 . title Support vector machine in precision agriculture: a review . journal Computers and Electronics in Agriculture volume 191 , pages 106546

    2021

  34. [43]

    , author Kumar, J

    author Konduri, V.S. , author Kumar, J. , author Hargrove, W.W. , author Hoffman, F.M. , author Ganguly, A.R. , year 2020 . title Mapping crops within the growing season across the united states . journal Remote Sensing of Environment volume 251 , pages 112048

    2020

  35. [44]

    , author Grieser, J

    author Kottek, M. , author Grieser, J. , author Beck, C. , author Rudolf, B. , author Rubel, F. , year 2006 . title World map of the köppen-geiger climate classification updated . journal Meteorologische Zeitschrift volume 15 , pages 259--263 . :10.1127/0941-2948/2006/0130

    2006 doi

  36. [45]

    , author Skakun, S

    author Kussul, N. , author Skakun, S. , author Shelestov, A. , author Lavreniuk, M. , author Yailymov, B. , author Kussul, O. , year 2015 . title Regional scale crop mapping using multi-temporal satellite imagery . journal The International Archives of the Photogrammetry, Remo...

    2015

  37. [46]

    , author Tourre, Y

    author Lacaux, J. , author Tourre, Y. , author Vignolles, C. , author Ndione, J. , author Lafaye, M. , year 2007 . title Classification of ponds from high-spatial resolution remote sensing: Application to rift valley fever epidemics in senegal . journal Remote sensing of envir...

    2007

  38. [47]

    , author Wang, X

    author Lei, L. , author Wang, X. , author Zhang, L. , author Hu, X. , author Zhong, Y. , year 2024 . title Cropup: Historical products are all you need? an end-to-end cross-year crop map updating framework without the need for in situ samples . journal Remote Sensing of Enviro...

    2024

  39. [48]

    , author Abrams, J.F

    author Lenton, T.M. , author Abrams, J.F. , author Bartsch, A. , author Bathiany, S. , author Boulton, C.A. , author Buxton, J.E. , author Conversi, A. , author Cunliffe, A.M. , author Hebden, S. , author Lavergne, T. , et al., year 2024 . title Remotely sensing potential clim...

    2024

  40. [49]

    , author Di, L

    author Li, H. , author Di, L. , author Zhang, C. , author Lin, L. , author Guo, L. , author Yu, E.G. , author Yang, Z. , year 2024 a. title Automated in-season crop-type data layer mapping without ground truth for the conterminous united states based on multisource satellite i...

    2024 doi

  41. [50]

    , author Chen, B

    author Li, Z. , author Chen, B. , author Wu, S. , author Su, M. , author Chen, J.M. , author Xu, B. , year 2024 b. title Deep learning for urban land use category classification: A review and experimental assessment . journal Remote Sensing of Environment volume 311 , pages 114290

    2024

  42. [51]

    , author Kiefer, R.W

    author Lillesand, T. , author Kiefer, R.W. , author Chipman, J. , year 2015 . title Remote sensing and image interpretation . publisher John Wiley & Sons

    2015

  43. [52]

    , author Zhong, R

    author Lin, Z. , author Zhong, R. , author Xiong, X. , author Guo, C. , author Xu, J. , author Zhu, Y. , author Xu, J. , author Ying, Y. , author Ting, K. , author Huang, J. , et al., year 2022 . title Large-scale rice mapping using multi-task spatiotemporal deep learning and ...

    2022

  44. [53]

    , author Sun, Y

    author Liu, C. , author Sun, Y. , author Xu, Y. , author Sun, Z. , author Zhang, X. , author Lei, L. , author Kuang, G. , year 2024 . title A review of optical and sar image deep feature fusion in semantic segmentation . journal IEEE Journal of Selected Topics in Applied Earth...

    2024

  45. [54]

    , author Huete, A

    author Liu, H.Q. , author Huete, A. , year 1995 . title A feedback based modification of the ndvi to minimize canopy background and atmospheric noise . journal IEEE Transactions on Geoscience and Remote Sensing volume 33 , pages 457--465 . :10.1109/TGRS.1995.8746027

    1995 doi

  46. [55]

    , author Zhang, L

    author Liu, Z. , author Zhang, L. , author Yu, Y. , author Xi, X. , author Ren, T. , author Zhao, Y. , author Zhu, D. , author Zhu, A.x. , year 2022 . title Cross-year reuse of historical samples for crop mapping based on environmental similarity . journal Frontiers in Plant S...

    2022

  47. [56]

    , author Chen, S

    author Ma, Y. , author Chen, S. , author Ermon, S. , author Lobell, D.B. , year 2024 . title Transfer learning in environmental remote sensing . journal Remote Sensing of Environment volume 301 , pages 113924

    2024

  48. [57]

    , author Podra s c anin, Z

    author Mimi \'c , G. , author Podra s c anin, Z. , author Basarin, B. , year 2024 . title Change detection of the k \"o ppen climate zones in southeastern europe . journal Atmospheric Science Letters volume 25 , pages e1270

    2024

  49. [58]

    , author Belgiu, M

    author Mohammadi, S. , author Belgiu, M. , author Stein, A. , year 2024 a. title Few-shot learning for crop mapping from satellite image time series . journal Remote Sensing volume 16 , pages 1026

    2024

  50. [59]

    , author Belgiu, M

    author Mohammadi, S. , author Belgiu, M. , author Stein, A. , year 2024 b. title A source-free unsupervised domain adaptation method for cross-regional and cross-time crop mapping from satellite image time series . journal Remote Sensing of Environment volume 314 , pages 11438...

    2024 doi

  51. [60]

    title Monthly synoptic weather report - december 2022

    author National Centers for Environmental Information , year 2022 . title Monthly synoptic weather report - december 2022 . https://www.ncei.noaa.gov/access/monitoring/monthly-report/synoptic/202213. note accessed: 2025-04-29

    2022

  52. [61]

    , author Ho Tong Minh, D

    author Ndikumana, E. , author Ho Tong Minh, D. , author Baghdadi, N. , author Courault, D. , author Hossard, L. , year 2018 . title Deep recurrent neural network for agricultural classification using multitemporal sar sentinel-1 for camargue, france . journal Remote Sensing vo...

    2018

  53. [62]

    , author Mutavd z i \'c , B

    author Novkovi \'c , N. , author Mutavd z i \'c , B. , author Vukeli \'c , N. , year 2013 . title Vojvodina’s agriculture--analysis and possibilities

    2013

  54. [63]

    , author Mrziglod, J

    author Nowakowski, A. , author Mrziglod, J. , author Spiller, D. , author Bonifacio, R. , author Ferrari, I. , author Mathieu, P.P. , author Garcia-Herranz, M. , author Kim, D.H. , year 2021 . title Crop type mapping by using transfer learning . journal International Journal o...

    2021 doi

  55. [64]

    , author Pelletier, C

    author Nyborg, J. , author Pelletier, C. , author Lef \`e vre, S. , author Assent, I. , year 2022 . title Timematch: Unsupervised cross-region adaptation by temporal shift estimation . journal ISPRS Journal of Photogrammetry and Remote Sensing volume 188 , pages 301--313

    2022

  56. [65]

    , author Yang, Q

    author Pan, S.J. , author Yang, Q. , year 2010 . title A survey on transfer learning . journal IEEE Transactions on Knowledge and Data Engineering volume 22 , pages 1345--1359 . :10.1109/TKDE.2009.191

    2010 doi

  57. [66]

    , author Pavlović, D

    author Pandžić, M. , author Pavlović, D. , author Matavulj, P. , author Brdar, S. , author Marko, O. , author Crnojević, V. , author Kilibarda, M. , year 2024 . title Interseasonal transfer learning for crop mapping using sentinel-1 data . journal International Journal of Appl...

    2024 doi

  58. [67]

    , author Turkoglu, M.O

    author Perich, G. , author Turkoglu, M.O. , author Graf, L.V. , author Wegner, J.D. , author Aasen, H. , author Walter, A. , author Liebisch, F. , year 2023 . title Pixel-based yield mapping and prediction from sentinel-2 using spectral indices and neural networks . journal Fi...

    2023 doi

  59. [68]

    , author Inglada, J

    author Petitjean, F. , author Inglada, J. , author Gan c arski, P. , year 2012 . title Satellite image time series analysis under time warping . journal IEEE transactions on geoscience and remote sensing volume 50 , pages 3081--3095

    2012

  60. [69]

    , author Ngugi, M.K

    author Peña-Barragán, J.M. , author Ngugi, M.K. , author Plant, R.E. , author Six, J. , year 2011 . title Object-based crop identification using multiple vegetation indices, textural features and crop phenology . journal Remote Sensing of Environment volume 115 , pages 1301--1...

    2011 doi

  61. [70]

    , author Tetteh, G

    author Pham, V.D. , author Tetteh, G. , author Thiel, F. , author Erasmi, S. , author Schwieder, M. , author Frantz, D. , author van der Linden, S. , year 2024 . title Temporally transferable crop mapping with temporal encoding and deep learning augmentations . journal Interna...

    2024

  62. [71]

    , author Brdar, S

    author Radulovi \'c , M. , author Brdar, S. , author Pejak, B. , author Lugonja, P. , author Athanasiadis, I. , author Pajevi \'c , N. , author Pavi \'c , D. , author Crnojevi \'c , V. , year 2023 . title Machine learning-based detection of irrigation in vojvodina (serbia) usi...

    2023

  63. [72]

    , author Markovi \'c , M

    author Radulovi \'c , M. , author Markovi \'c , M. , author Brdar, S. , author Athanasiadis, I. , author Mimi \'c , G. , year 2025 a. title The first geospatial dataset of irrigated fields (2020--2024) in vojvodina (serbia) . journal Scientific Data volume 12 , pages 105

    2025

  64. [73]

    , author Markovi \'c , M

    author Radulovi \'c , M. , author Markovi \'c , M. , author Brdar, S. , author Athanasiadis, I. , author Mimi \'c , G. , year 2025 b. title The first geospatial dataset of irrigated fields (2020--2024) in vojvodina (serbia) . journal Scientific Data volume 12 , pages 105

    2025

  65. [74]

    , author Di, L

    author Rahman, M.S. , author Di, L. , author Yu, E. , author Zhang, C. , author Mohiuddin, H. , year 2019 . title In-season major crop-type identification for us cropland from landsat images using crop-rotation pattern and progressive data classification . journal Agriculture ...

    2019

  66. [75]

    , author Ghosh, R

    author Ravirathinam, P. , author Ghosh, R. , author Khandelwal, A. , author Jia, X. , author Mulla, D. , author Kumar, V. , year 2024 . title Combining satellite and weather data for crop type mapping: An inverse modelling approach . journal arXiv [cs.CV] http://arxiv.org/abs/...

    2024

  67. [76]

    , author Kasanick \`y , T

    author Rus n \'a k, T. , author Kasanick \`y , T. , author Mal \' k, P. , author Moj z i s , J. , author Zelenka, J. , author Svi c ek, M. , author Abrah \'a m, D. , author Halabuk, A. , year 2023 . title Crop mapping without labels: Investigating temporal and spatial transfer...

    2023

  68. [77]

    , author Wang, S

    author Ru wurm, M. , author Wang, S. , author Korner, M. , author Lobell, D. , year 2020 . title Meta-learning for few-shot land cover classification , in: booktitle Proceedings of the ieee/cvf conference on computer vision and pattern recognition workshops , pp. pages 200--201

    2020

  69. [78]

    , author Körner, M

    author Rußwurm, M. , author Körner, M. , year 2020 . title Self-attention for raw optical satellite time series classification . journal ISPRS Journal of Photogrammetry and Remote Sensing volume 169 , pages 421--435 . https://www.sciencedirect.com/science/article/pii/S09242716...

    2020 doi

  70. [79]

    , author Ghazaryan, G

    author Safonova, A. , author Ghazaryan, G. , author Stiller, S. , author Main-Knorn, M. , author Nendel, C. , author Ryo, M. , year 2023 . title Ten deep learning techniques to address small data problems with remote sensing . journal International Journal of Applied Earth Obs...

    2023

  71. [80]

    , author Erasmi, S

    author Schlund, M. , author Erasmi, S. , year 2020 . title Sentinel-1 time series data for monitoring the phenology of winter wheat . journal Remote Sensing of Environment volume 246 , pages 111814

    2020

  72. [81]

    , author Paliwal, K.K

    author Schuster, M. , author Paliwal, K.K. , year 1997 . title Bidirectional recurrent neural networks . journal IEEE transactions on Signal Processing volume 45 , pages 2673--2681

    1997

  73. [82]

    , year 2023

    author Selea, T. , year 2023 . title Agrisen-cog, a multicountry, multitemporal large-scale sentinel-2 benchmark dataset for crop mapping using deep learning . journal Remote Sensing volume 15 , pages 2980 . https://www.mdpi.com/2072-4292/15/12/2980

    2023

  74. [83]

    title Moisture index

    author Sentinel Hub , year 2024 . title Moisture index . howpublished https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/msi/ . note Accessed: 2025-02-24

    2024

  75. [84]

    , author Chanussot, J

    author Tang, P. , author Chanussot, J. , author Guo, S. , author Zhang, W. , author Qie, L. , author Zhang, P. , author Fang, H. , author Du, P. , year 2024 . title Deep learning with multi-scale temporal hybrid structure for robust crop mapping . journal ISPRS Journal of Phot...

    2024

  76. [85]

    , author Morais, R

    author Teixeira, I. , author Morais, R. , author Sousa, J.J. , author Cunha, A. , year 2023 . title Deep learning models for the classification of crops in aerial imagery: A review . journal Agriculture volume 13 . https://www.mdpi.com/2077-0472/13/5/965, :10.3390/agriculture13050965

    2023 doi

  77. [86]

    , author Hoang Tu, L

    author Thi Huyen, N. , author Hoang Tu, L. , author Kim Loi, N. , year 2022 . title Applying landscape approach in assessing effectiveness of mangrove conservation in ca mau cape national park, vietnam . journal Journal of Forest Research volume 27 , pages 371--378

    2022

  78. [87]

    , author Hrnjak, I

    author Tošić, I. , author Hrnjak, I. , author Gavrilov, M. , author Unkašević, M. , author Markovic, S. , author Lukic, T. , year 2013 . title Annual and seasonal variability of precipitation in vojvodina, serbia . journal Theoretical and Applied Climatology volume 117 , pages...

    2013 doi

  79. [88]

    , year 1979

    author Tucker, C.J. , year 1979 . title Red and photographic infrared linear combinations for monitoring vegetation . journal Remote Sensing of Environment volume 8 , pages 127--150 . https://www.sciencedirect.com/science/article/pii/0034425779900130, :https://doi.org/10.1016/...

    1979 doi

  80. [89]

    Department of Agriculture, Farm Service Agency , year 2023

    author U.S. Department of Agriculture, Farm Service Agency , year 2023 . title National agriculture imagery program . howpublished https://datagateway.nrcs.usda.gov/ . note 1-meter resolution aerial imagery, accessed via USDA Geospatial Data Gateway

    2023

  81. [90]

    title Serbia Grain and Feed Annual Annual Report on Wheat Corn and Barley

    author USDA, Foreign Agricultural Service , year 2019 . title Serbia Grain and Feed Annual Annual Report on Wheat Corn and Barley . type Technical Report . U.S. Department of Agriculture. address Washington, DC . https://apps.fas.usda.gov/newgainapi/api/report/downloadreportby...

    2019

  82. [91]

    title Field crops: Usual planting and harvesting dates

    author USDA, National Agricultural Statistics Service , year 2010 . title Field crops: Usual planting and harvesting dates . journal USDA National Agricultural Statistics Service, Agriculural Handbook volume 628

    2010

  83. [92]

    title Cropland data layer faqs

    author USDA, National Agricultural Statistics Service , year 2025 a. title Cropland data layer faqs . howpublished https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php . note Accessed: 2025-05-10

    2025

  84. [93]

    title State agricultural statistics

    author USDA, National Agricultural Statistics Service , year 2025 b. title State agricultural statistics . howpublished https://www.nass.usda.gov/Statistics_by_State/ . note Accessed: 2025-04-24

    2025

  85. [94]

    title Landsat 7 (etm+) collection 2 level-2 science products

    author USGS , year 2024 a. title Landsat 7 (etm+) collection 2 level-2 science products . howpublished https://earthexplorer.usgs.gov/ . note Accessed: 2024-02-05

    2024

  86. [95]

    title Landsat 8 (oli/tirs) collection 2 level-2 science products

    author USGS , year 2024 b. title Landsat 8 (oli/tirs) collection 2 level-2 science products . howpublished https://earthexplorer.usgs.gov/ . note Accessed: 2024-02-05

    2024

  87. [96]

    , author Shazeer, N

    author Vaswani, A. , author Shazeer, N. , author Parmar, N. , author Uszkoreit, J. , author Jones, L. , author Gomez, A.N. , author Kaiser, L. , author Polosukhin, I. , year 2017 . title Attention is all you need . journal CoRR volume abs/1706.03762 . http://arxiv.org/abs/1706.03762

    2017 arXiv

  88. [97]

    , author Bellemans, N

    author Waldner, F. , author Bellemans, N. , author Hochman, Z. , author Newby, T. , author de Abelleyra, D. , author Ver \'o n, S.R. , author Bartalev, S. , author Lavreniuk, M. , author Kussul, N. , author Le Maire, G. , et al., year 2019 . title Roadside collection of traini...

    2019

  89. [98]

    , author Zhang, J

    author Wang, X. , author Zhang, J. , author Wang, X. , author Wu, Z. , author Prodhan, F.A. , year 2024 a. title Incorporating multi-temporal remote sensing and a pixel-based deep learning classification algorithm to map multiple-crop cultivated areas . journal Applied Science...

    2024

  90. [99]

    , author Feng, L

    author Wang, Y. , author Feng, L. , author Zhang, Z. , author Tian, F. , year 2023 . title An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using sentinel-2 imagery . journal ISPRS Journal of Photogrammetry and Remo...

    2023 doi

  91. [100]

    , author Huang, H

    author Wang, Y. , author Huang, H. , author State, R. , year 2024 b. title Cross domain early crop mapping using cropstgan . journal IEEE Access volume 12 , pages 130800–130815 . http://dx.doi.org/10.1109/ACCESS.2024.3436620, :10.1109/access.2024.3436620

    2024 doi

  92. [101]

    , year 1922

    author Whittaker, E.T. , year 1922 . title On a new method of graduation . journal Proceedings of the Edinburgh Mathematical Society volume 41 , pages 63--75

    1922

  93. [102]

    , author Dzene, I

    author Wijesingha, J. , author Dzene, I. , author Wachendorf, M. , year 2024 . title Evaluating the spatial--temporal transferability of models for agricultural land cover mapping using landsat archive . journal ISPRS Journal of Photogrammetry and Remote Sensing volume 213 , p...

    2024

  94. [103]

    , author Yang, J

    author Xu, J. , author Yang, J. , author Xiong, X. , author Li, H. , author Huang, J. , author Ting, K. , author Ying, Y. , author Lin, T. , year 2021 . title Towards interpreting multi-temporal deep learning models in crop mapping . journal Remote Sensing of Environment volum...

    2021 doi

  95. [104]

    , author Zhu, Y

    author Xu, J. , author Zhu, Y. , author Zhong, R. , author Lin, Z. , author Xu, J. , author Jiang, H. , author Huang, J. , author Li, H. , author Lin, T. , year 2020 . title Deepcropmapping: A multi-temporal deep learning approach with improved spatial generalizability for dyn...

    2020 doi

  96. [105]

    , author Ma, Y

    author Xu, Y. , author Ma, Y. , author Zhang, Z. , year 2024 . title Self-supervised pre-training for large-scale crop mapping using sentinel-2 time series . journal ISPRS Journal of Photogrammetry and Remote Sensing volume 207 , pages 312--325

    2024

  97. [106]

    , author Dong, Y

    author Xuan, F. , author Dong, Y. , author Li, J. , author Li, X. , author Su, W. , author Huang, X. , author Huang, J. , author Xie, Z. , author Li, Z. , author Liu, H. , author Tao, W. , author Wen, Y. , author Zhang, Y. , year 2023 . title Mapping crop type in northeast chi...

    2023 doi

  98. [107]

    , author Wang, D

    author Yang, H. , author Wang, D. , author Xin, J. , author Qian, H. , author Li, C. , author Wang, Y. , author Tan, Y. , author Dai, J. , author Zhao, H. , author Li, Z. , year 2024 . title Integrating multidimensional feature indices and phenological windows for mapping crop...

    2024 doi

  99. [108]

    , author Driscol, J

    author Yang, L. , author Driscol, J. , author Sarigai, S. , author Wu, Q. , author Chen, H. , author Lippitt, C.D. , year 2022 . title Google earth engine and artificial intelligence (ai): a comprehensive review . journal Remote Sensing volume 14 , pages 3253

    2022

  100. [109]

    , author Huang, R

    author Yang, L. , author Huang, R. , author Zhang, J. , author Huang, J. , author Wang, L. , author Dong, J. , author Shao, J. , year 2023 . title Inter-continental transfer of pre-trained deep learning rice mapping model and its generalization ability . journal Remote Sensing...

    2023

  101. [110]

    , author Luo, F

    author Zhan, W. , author Luo, F. , author Luo, H. , author Li, J. , author Wu, Y. , author Yin, Z. , author Wu, Y. , author Wu, P. , year 2024 . title Time-series-based spatiotemporal fusion network for improving crop type mapping . journal Remote Sensing volume 16 , pages 235...

    2024 doi

  102. [111]

    , author Yang, J

    author Zhang, S. , author Yang, J. , author Leng, P. , author Ma, Y. , author Wang, H. , author Song, Q. , year 2024 . title Crop type mapping with temporal sample migration . journal International Journal of Remote Sensing volume 45 , pages 7014--7032

    2024

  103. [112]

    , author Yang, Q

    author Zhang, Y. , author Yang, Q. , year 2021 . title A survey on multi-task learning . journal IEEE transactions on knowledge and data engineering volume 34 , pages 5586--5609

    2021

  104. [113]

    , author Dong, Z

    author Zhi, F. , author Dong, Z. , author Guga, S. , author Bao, Y. , author Han, A. , author Zhang, J. , author Bao, Y. , year 2022 . title Rapid and automated mapping of crop type in jilin province using historical crop labels and the google earth engine . journal Remote Sen...

    2022

  105. [114]

    , author Gong, P

    author Zhong, L. , author Gong, P. , author Biging, G.S. , year 2014 . title Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using landsat imagery . journal Remote Sensing of Environment volume 140 , pages 1--13 . https://www.sciencedire...

    2014 doi

  106. [115]

    , author Hu, L

    author Zhong, L. , author Hu, L. , author Zhou, H. , year 2019 . title Deep learning based multi-temporal crop classification . journal Remote Sensing of Environment volume 221 , pages 430--443 . https://www.sciencedirect.com/science/article/pii/S0034425718305418, :https://doi...

    2019 doi

  107. [116]

    , author Marković, M

    author Živaljević, B. , author Marković, M. , author Mimić, G. , author Marko, O. , author Woznicki, S. , year 2024 . title Multi-annual crop maps reveal cropping patterns in the vojvodina region (serbia) , in: booktitle 2024 12th International Conference on Agro-Geoinformatic...

    2024 doi

Pith tools

Reviewed May 19, 2026 · model on record in the stance chip above.