The Bhutanese government is increasing its utilization of technological
approaches such as including Remote Sensing-based knowledge in their
decision-making process. This study focuses on crop type and crop extent in
Paro, one of the top rice-yielding districts in Bhutan, and employs publicly
available NICFI high-resolution satellite imagery from Planet. Two Deep
Learning (DL) approaches, point-based (DNN) and patch-based (U-Net), models
were used in conjunction with cloud-computing platforms. Three different models
per DL approaches (DNN and U-Net) were trained: 1) RGBN channels from Planet;
2) RGBN and elevation data (RGBNE); 3) RGBN and Sentinel-1 (S1) data (RGBNS),
and RGBN with E and S1 data (RGBNES). From this comprehensive analysis, the
U-Net displayed higher performance metrics across both model training and model
validation efforts. Among the U-Net model sets, the RGBN, RGBNE, RGBNS, and
RGBNES models had an F1-score of 0.8546, 0.8563, 0.8467, and 0.8500
respectively. An independent model evaluation was performed and found a high
level of performance variation across all the metrics. For this independent
model evaluation, the U-Net RGBN, RGBNE, RGBNES, and RGBN models displayed the
F1-scores of 0.5935, 0.6154, 0.5882, and 0.6582, suggesting U-Net RGBNES as the
best model. The study shows that the DL approaches can predict rice. Also, DL
methods can be used with the survey-based approaches currently utilized by the
Bhutan Department of Agriculture. Further, this study demonstrated the usage of
regional land cover products such as SERVIR's RLCMS as a weak label approach to
capture different strata addressing the class imbalance problem and improving
the sampling design for DL application. Finally, through preliminary model
testing and comparisons outlined it was shown that using additional features
such as NDVI, EVI, and NDWI did not drastically improve model performance.

该研究使用高分辨率卫星图像，结合深度学习方法（DNN 和 U-Net），主要关注不丹帕罗地区的作物类型和范围，证明了深度学习方法可以预测水稻，并可以与不丹农业部目前使用的基于调查的方法相结合。同时，利用区域土地覆盖产品作为弱标签方法来解决类别不平衡问题，并改进深度学习应用的抽样设计。此外，通过初步的模型测试和比较表明，使用额外特征如 NDVI、EVI 和 NDWI 没有明显改善模型性能。