Development of a Remote Sensing-Based “Boro” Rice Mapping System
Bibliographic record
Abstract
Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping “boro” rice (i.e., cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets (i.e., land use map, crop calendar, and ground-based rice production information) during the period 2007–2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007–2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007–2009; and (iii) model calibration using data from the period 2007–2009 and its validation using data from the period 2010–2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (i.e., MODIS-based) and ground-based estimates of boro rice area at both country (i.e., percentage error in the range −0.83–1.42%) and district levels (i.e., r2 in the range 0.69–0.89) during the period 2010–2012. Our proposed method demonstrated its effectiveness in mapping rice system at the regional/country scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".