Klasifikasi Tutupan Awan Citra MODIS Menggunakan MobileNetV2 sebagai Tahap Pra-Pemrosesan Pemantauan Produktivitas Padi
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1218Keywords:
CNN; Cloud Cover Classification; Remote Sensing; MobileNetV2; MODIS MOD09GAAbstract
Cloud cover is a major constraint on the use of optical satellite imagery for rice productivity monitoring in tropical regions, because it degrades surface reflectance and introduces errors into vegetation index calculations. Most cloud detection methods still rely on spectral thresholds, which struggle to separate clouds from surface objects with similar reflectance. This study develops a cloud cover classification model based on a Convolutional Neural Network (CNN) with the MobileNetV2 architecture, applied to MODIS MOD09GA imagery as a preprocessing stage before rice productivity analysis. The dataset consists of 365 daily image patches from 2024 covering Pandeglang, Lebak, and Serang Regencies in Banten Province. Initial labels were produced by a rule-based spectral auto-labeling method using Band 1 and Band 4 reflectance. The data were split 70:15:15 and enhanced with mild augmentation. On the test set, the model achieved an accuracy of 71%, with precision of 72%, recall of 85%, and an F1-score of 78% for the cloudy class. The high recall for the cloudy class suits the role of the model as a filter for cloud-contaminated images ahead of time-series analysis, rather than as a pixel-level cloud segmentation tool. These findings indicate that the lightweight MobileNetV2 architecture has potential as an automated preprocessing layer in remote sensing-based rice productivity monitoring.
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S. Herliana, Q. Salimi, and F. Khoerani, “Analysis of Production Factors on National Rice Availability: Quantitative and Swot Approach,” The International Conference on Sustainable Economics Management and Accounting Proceeding, vol. 1, no. 01, pp. 3891–3903, 2025, doi: 10.32424/icsema.1.1.65.
C. Basset, “Soil security: The cornerstone of national security in an era of global disruptions,” Soil Security, vol. 16, p. 100154, 2024, doi: 10.1016/j.soisec.2024.100154.
X. Uwiringiyimana and E. Antriyandarti, “Declining Rice Production in Indonesia: A Case Study of Central Java, Challenges and Food Security Strategies,” Journal of Scientific Reports, vol. 9, no. 1, pp. 177–192, 2025, doi: 10.58970/jsr.1103.
F. Rozi et al., “Indonesian market demand patterns for food commodity sources of carbohydrates in facing the global food crisis,” Heliyon, vol. 9, no. 6, p. e16809, 2023, doi: 10.1016/j.heliyon.2023.e16809.
Erlin, A. Yunianta, L. A. Wulandhari, Y. Desnelita, N. Nasution, and Junadhi, “Enhancing Rice Production Prediction in Indonesia Using Advanced Machine Learning Models,” IEEE Access, vol. 12, no. September, pp. 151161–151177, 2024, doi: 10.1109/ACCESS.2024.3478738.
Kardiyono, T. Mulyaqin, P. N. Susilawati, D. Haryani, I. Setyowati, and H. Ismatul, “A Model for Accelerating Rice Planting in Paddy Fields to Provide Food in Banten Province during the Covid-19 Pandemic,” IOP Conf. Ser. Earth Environ. Sci., vol. 715, no. 1, 2021, doi: 10.1088/1755-1315/715/1/012028.
B. P. Statistik, “Luas panen, produksi, dan produktivitas padi menurut provinsi.”
A. C. Tristan, A. P. Mandiri, H. Verdiant, Y. Yoseva, and R. F. Putri, “Analysis of Carrying Capacity of Crop Agricultural Land in Banten Province,” E3S Web of Conferences, vol. 468, pp. 1–4, 2023, doi: 10.1051/e3sconf/202346806002.
R. Zhao, Y. Li, and M. Ma, “Mapping paddy rice with satellite remote sensing: A review,” Sustainability (Switzerland), vol. 13, no. 2, pp. 1–20, 2021, doi: 10.3390/su13020503.
S. H. Chiang and M. B. Ton, “Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam,” Remote Sens. (Basel)., vol. 17, no. 9, 2025, doi: 10.3390/rs17091583.
R. Jiang et al., “Cloud cover throughout all the paddy rice fields in guangdong, china: Impacts on sentinel 2 msi and landsat 8 oli optical observations,” Remote Sens. (Basel)., vol. 13, no. 15, pp. 1–17, 2021, doi: 10.3390/rs13152961.
N. Shin et al., “Perspective: Improving the accuracy of plant phenology observations and land-cover and land-use detection by optical satellite remote-sensing in the Asian tropics,” Frontiers in Forests and Global Change, vol. 6, no. February, pp. 1–16, 2023, doi: 10.3389/ffgc.2023.1106723.
Y. Yun, J. Jung, and Y. Han, “Cloud restoration of optical satellite imagery using time-series spectral similarity group,” GIsci. Remote Sens., vol. 61, no. 1, 2024, doi: 10.1080/15481603.2024.2324553.
B. C. Gao, R. R. Li, Y. Yang, and M. Anderson, “Correction of Thin Cirrus Absorption Effects in Landsat 8 Thermal Infrared Sensor Images Using the Operational Land Imager Cirrus Band on the Same Satellite Platform,” Sensors, vol. 24, no. 14, 2024, doi: 10.3390/s24144697.
S. Park and N. W. Park, “Combining Gaussian Process Regression with Poisson Blending for Seamless Cloud Removal from Optical Remote Sensing Imagery for Cropland Monitoring,” Agronomy, vol. 13, no. 11, 2023, doi: 10.3390/agronomy13112789.
M. S. Memon et al., “Estimating rice yield under different wheat residue coverage levels using multispectral Gaofen satellite data and remote sensing indices,” Journal of Agricultural Engineering, vol. 56, no. 3, 2025, doi: 10.4081/jae.2025.1698.
C. Zhang, P. Yi, Y. Liu, L. Ma, W. Hu, and J. Wu, “Thin cloud correction method for visible remote sensing images using a spectral transformation scheme,” GIsci. Remote Sens., vol. 60, no. 1, 2023, doi: 10.1080/15481603.2023.2196133.
R. Yao, Y. Zhang, L. Wang, J. Li, and Q. Yang, “Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method,” Int. J. Digit. Earth, vol. 16, no. 2, pp. 4749–4768, 2023, doi: 10.1080/17538947.2023.2283492.
X. Liu, L. Ji, C. Zhang, and Y. Liu, “A method for reconstructing NDVI time-series based on envelope detection and the Savitzky-Golay filter,” Int. J. Digit. Earth, vol. 15, no. 1, pp. 553–584, 2022, doi: 10.1080/17538947.2022.2044397.
A. Suprijanto, Y. Tan, S. M. Masum, and R. D. Moreno Santillan, “A new hybrid filter for NDVI time series reconstruction and data quality enhancement,” Geomatics, Natural Hazards and Risk, vol. 15, no. 1, p., 2024, doi: 10.1080/19475705.2024.2410359.
B. Wu et al., “Challenges and opportunities in remote sensing-based crop monitoring: a review,” no. December, 2022.
F. Mouret, M. Albughdadi, S. Duthoit, D. Kouamé, G. Rieu, and J. Y. Tourneret, “Reconstruction of Sentinel-2 derived time series using robust Gaussian mixture models — Application to the detection of anomalous crop development,” Comput. Electron. Agric., vol. 198, pp. 0–3, 2022, doi: 10.1016/j.compag.2022.106983.
J. Kong et al., “Uncovering the importance of spatiotemporal resolution in satellite-based rice yield estimation using a simple but effective proxy,” Agric. For. Meteorol., vol. 376, no. October 2025, p. 110892, 2026, doi: 10.1016/j.agrformet.2025.110892.
H. Zhang, Y. Zhang, T. Gao, S. Lan, F. Tong, and M. Li, “Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes,” Remote Sens. (Basel)., vol. 15, no. 11, 2023, doi: 10.3390/rs15112951.
M. Waleed et al., “Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation,” Sci. Rep., vol. 12, no. 1, pp. 1–15, 2022, doi: 10.1038/s41598-022-17454-y.
Fatchurrachman et al., “High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine,” Remote Sens. (Basel)., vol. 14, no. 8, pp. 1–22, 2022, doi: 10.3390/rs14081875.
B. Suseno, G. Brunel, H. Wijayanto, K. Sadik, F. M. Afendi, and B. Tisseyre, “Reconstructing satellite temporal series data under cloudy conditions: Application in predicting rice growth phases,” Smart Agricultural Technology, vol. 12, p. 101378, 2025, doi: 10.1016/j.atech.2025.101378.
P. Mu and F. Tian, “Spatiotemporal Fusion of Multi-Temporal MODIS and Landsat-8/9 Imagery for Enhanced Daily 30 m NDVI Reconstruction: A Case Study of the Shiyang River Basin Cropland (2022),” Remote Sens. (Basel)., vol. 17, no. 9, 2025, doi: 10.3390/rs17091510.
Y. Guo, L. Han, D. Zhang, G. Sun, J. Fan, and X. Ren, “The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change,” Sustainability (Switzerland), vol. 15, no. 14, 2023, doi: 10.3390/su151411350.
Y. Lin et al., “Influence of Vegetation Phenology on the Temporal Effect of Crop Fractional Vegetation Cover Derived from Moderate-Resolution Imaging Spectroradiometer Nadir Bidirectional Reflectance Distribution Function–Adjusted Reflectance,” Agriculture (Switzerland), vol. 14, no. 10, 2024, doi: 10.3390/agriculture14101759.
C. Liu et al., “Global Comparison of Leaf Area Index Products over Water-Vegetation Mixed Heterogeneous Surface Network (HESNet-WV),” Remote Sens. (Basel)., vol. 15, no. 5, 2023, doi: 10.3390/rs15051337.
V. Zekoll, M. Main-Knorn, J. Louis, D. Frantz, R. Richter, and B. Pflug, “Comparison of masking algorithms for sentinel-2 imagery,” Remote Sens. (Basel)., vol. 13, no. 1, pp. 1–21, 2021, doi: 10.3390/rs13010137.
Y. Yang, K. Shang, C. Xiao, C. Wang, and H. Tang, “Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China,” ISPRS Int. J. Geoinf., vol. 11, no. 2, 2022, doi: 10.3390/ijgi11020111.
C. T. Nguyen, A. Chidthaisong, P. K. Diem, and L. Z. Huo, “A modified bare soil index to identify bare land features during agricultural fallow-period in southeast asia using landsat 8,” Land (Basel)., vol. 10, no. 3, pp. 1–18, 2021, doi: 10.3390/land10030231.
E. A. L. Salas and S. S. Kumaran, “Hyperspectral Bare Soil Index (HBSI): Mapping Soil Using an Ensemble of Spectral Indices in Machine Learning Environment,” Land (Basel)., vol. 12, no. 7, 2023, doi: 10.3390/land12071375.
B. Delaney, K. Tansey, and M. Whelan, “Satellite Remote Sensing Techniques and Limitations for Identifying Bare Soil,” Remote Sens. (Basel)., vol. 17, no. 4, 2025, doi: 10.3390/rs17040630.
S. Skakun et al., “Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2,” Remote Sens. Environ., vol. 274, no. March, 2022, doi: 10.1016/j.rse.2022.112990.
X. Fan, L. Chen, X. Xu, C. Yan, J. Fan, and X. Li, “Land Cover Classification of Remote Sensing Images Based on Hierarchical Convolutional Recurrent Neural Network,” Forests, vol. 14, no. 9, pp. 1–27, 2023, doi: 10.3390/f14091881.
A. Alem and S. Kumar, “End-to-End Convolutional Neural Network Feature Extraction for Remote Sensed Images Classification,” Applied Artificial Intelligence, vol. 36, no. 1, 2022, doi: 10.1080/08839514.2022.2137650.
Z. Zhang et al., “Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples,” Sensors, vol. 23, no. 18, 2023, doi: 10.3390/s23188010.
Y. Gulzar, “Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique,” Sustainability (Switzerland), vol. 15, no. 3, 2023, doi: 10.3390/su15031906.
M. Alkanan and Y. Gulzar, “Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning,” Front. Appl. Math. Stat., vol. 9, 2023, doi: 10.3389/fams.2023.1320177.
M. M. A. Zaid, A. A. Mohammed, and P. Sumari, “Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques,” Journal of Computer Science, vol. 21, no. 3, pp. 635–645, 2025, doi: 10.3844/jcssp.2025.635.645.
Wenfa Xu, “Adaptive Threshold Cloud Detection Method based on Gaofen-1 Remote Sensing image,” Computer Fraud and Security, pp. 276–284, 2025, doi: 10.52710/cfs.498.
X. Hao, L. Liu, R. Yang, L. Yin, L. Zhang, and X. Li, “Image Target Recognition,” Remote Sens. (Basel)., vol. 15, no. 3, p. 827, 2023.
R. Zhao, Y. Li, J. Chen, M. Ma, L. Fan, and W. Lu, “Mapping a paddy rice area in a cloudy and rainy region using spatiotemporal data fusion and a phenology-based algorithm,” Remote Sens. (Basel)., vol. 13, no. 21, pp. 1–20, 2021, doi: 10.3390/rs13214400.
R. Singh, M. Pal, and M. Biswas, “Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review,” Geomatics, vol. 5, no. 3, pp. 1–42, 2025, doi: 10.3390/geomatics5030027.
B. Pham-Duc, H. Nguyen, H. Phan, and Q. Tran-Anh, “Trends and applications of google earth engine in remote sensing and earth science research: a bibliometric analysis using scopus database,” Earth Sci. Inform., vol. 16, no. 3, pp. 2355–2371, 2023, doi: 10.1007/s12145-023-01035-2.
Q. Zhao, L. Yu, X. Li, D. Peng, Y. Zhang, and P. Gong, “Progress and trends in the application of google earth and google earth engine,” Remote Sens. (Basel)., vol. 13, no. 18, pp. 1–21, 2021, doi: 10.3390/rs13183778.
A. Velastegui-Montoya, N. Montalván-Burbano, P. Carrión-Mero, H. Rivera-Torres, L. Sadeck, and M. Adami, “Google Earth Engine: A Global Analysis and Future Trends,” Remote Sens. (Basel)., vol. 15, no. 14, 2023, doi: 10.3390/rs15143675.
S. Ojaghi, Y. Bouroubi, S. Foucher, A. Nteziyaremye, and A. Zannou, “Transformer-Based BRDF Modeling and Normalization of MODIS Surface Reflectance Using Google Earth Engine for Agricultural Monitoring,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 19, pp. 25919–25933, 2026, doi: 10.1109/JSTARS.2026.3716596.
A. I. Flores-Anderson, J. Cardille, K. Azad, E. Cherrington, Y. Zhang, and S. Wilson, “Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation,” Sci. Data, vol. 10, no. 1, pp. 1–8, 2023, doi: 10.1038/s41597-023-02439-x.
J. Zhou et al., “A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform,” Int. J. Digit. Earth, vol. 16, no. 1, pp. 988–1007, 2023, doi: 10.1080/17538947.2023.2192004.
B. Suseno, G. Brunel, H. Wijayanto, K. Sadik, F. M. Afendi, and B. Tisseyre, “Reconstructing satellite temporal series data under cloudy conditions: Application in predicting rice growth phases,” Smart Agricultural Technology, vol. 12, p. 101378, 2025, doi: 10.1016/j.atech.2025.101378.
A. Modis et al., “Product User Guide MODIS Surface Reflectance User ’ s Guide,” vol. 1, pp. 1–36, 2020.
Q. Xiong et al., “A cloud detection approach based on hybrid multispectral features with dynamic thresholds for GF-1 remote sensing images,” Remote Sens. (Basel)., vol. 12, no. 3, pp. 1–18, 2020, doi: 10.3390/rs12030450.
S. Jeong, J. Ko, and J. M. Yeom, “Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea,” Science of the Total Environment, vol. 802, p. 149726, 2022, doi: 10.1016/j.scitotenv.2021.149726.
Y. Wang, S. Li, Y. Lin, and M. Wang, “Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors,” pp. 1–21, 2021.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, 2018, doi: 10.1109/CVPR.2018.00474.
A. Hasan, C. Witharana, M. R. Udawalpola, and A. K. Liljedahl, “DETECTION of CLOUDS in MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 46, no. M-2–2022, pp. 103–109, 2022, doi: 10.5194/isprs-Archives-XLVI-M-2-2022-103-2022.
T. Stillinger, D. A. Roberts, N. M. Collar, and J. Dozier, “Cloud Masking for Landsat 8 and MODIS Terra Over Snow-Covered Terrain: Error Analysis and Spectral Similarity Between Snow and Cloud,” Water Resour. Res., vol. 55, no. 7, pp. 6169–6184, 2019, doi: 10.1029/2019WR024932.
H. Ma, Nan?; Sun, Lin?; Zhou, Chenghu?; He, Yawen?; Dong, Chuanxiang?; Qu, Yu?; Yu, “A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 3294–3308, 2023, doi: 10.1109/jstars.2023.3261326.
C. Yin, Meijie?; Wang, Peng?; Hao, Weilong?; Ni, “Cloud detection of high-resolution remote sensing image based on improved U-Net,” Multimed. Tools Appl., pp. 1–18, 2023, doi: https://doi.org/10.1007/s11042-023-14655-z.
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