Monitoring the growth status of rice based on hyperspectral satellite remote sensing data
DOI:
https://doi.org/10.15837/ijccc.2025.1.6910Keywords:
Rice, Chlorophyll, Growth Monitoring, 3D-CNN, Hyperspectral Remote SensingAbstract
This study proposes a novel approach to rice growth monitoring using a 3D Convolutional Neural Network (3D-CNN) model applied to hyperspectral satellite remote sensing data. The model combines spatial, temporal, and spectral information processing to enhance the accuracy of rice growth monitoring over large areas. A new loss function is introduced to address imbalanced yield label distribution. The model’s performance is validated using rice yield data from China’s main rice-growing regions, demonstrating superior predictive capability compared to existing methods. This approach offers a promising tool for improving food security through more accurate and timely crop monitoring.
References
Xia, L., Wu, X. (2022). A review of hyperspectral remote sensing of crops. E3S Web of Conferences, 338, 01029. https://doi.org/10.1051/e3sconf/202233801029
Wang, W., He, H., Ma, C. (2023). An Improved Deeplabv3+ Model for Semantic Segmentation of Urban Environments Targeting Autonomous Driving. International Journal of Computers Communications & Control, 18(6),Article 6.https://doi.org/10.15837/ijccc.2023.6.5879
Anitha, J., Saranya, N. (2022). Cassava Leaf Disease Identification and Detection Using Deep Learning Approach. International Journal Of Computers Communications & Control, 17(2), Article 2. https://doi.org/10.15837/ijccc.2022.2.4356
Luo, Y., Zhang, Z., Chen, Y., Li, Z., Tao, F. (2020). ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth System Science Data, 12(1), 197–214. https://doi.org/10.5194/essd- 12-197-2020
Li, H., Wang, X., Wang, S., Liu, J., Liu, Y., Liu, Z., Chen, S., Wang, Q., Zhu, T., Wang, L., Wang, L. (2024). ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China. Earth System Science Data, 16(4), 1689–1701. https://doi.org/10.5194/essd-16-1689-2024
Zhang, J., Zou, H., Qing, Q., Yang, Y., Li, Q., Liu, Z., Guo, X., Du, Z. (2003). Effect of Chemical Oxidation on the Structure of Single-Walled Carbon Nanotubes. The Journal of Physical Chemistry B, 107(16), 3712–3718. https://doi.org/10.1021/jp027500u
Poonkuntran, S., Abinaya, V., Moorthi, S. M., Oza, M. P. (2022). Efficient Classification of Satellite Image with Hybrid Approach Using CNN-CA. International Journal of Computers Communications & Control, 17(5), Article 5. https://doi.org/10.15837/ijccc.2022.5.4485
Lambert, M.-J., Traoré, P. C. S., Blaes, X., Baret, P., Defourny, P. (2018). Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt. Remote Sensing of Environment, 216, 647–657. https://doi.org/10.1016/j.rse.2018.06.036
Ming-yue, X. I. N., Hong, Y. I. N., Long, C., Mei-ling, Z., Zhi-yong, R. E. N., Jing, M. (2015). Estimation of Rice Canopy LAI with Different Growth Stages Based on Hyperspectral Remote Sensing Data. Chinese Journal of Agrometeorology,] 36(06), 762. https://doi.org/10.3969/j.issn.1000- 6362.2015.06.014
Dong, J., Xiao, X. (2016). Evolution of regional to global paddy rice mapping methods: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 214–227. https://doi.org/10.1016/j.isprsjprs.2016.05.010
CAI Yaotong, L. S., CAI Yaotong, L. S. (2020). Extraction of paddy rice based on convolutional neuralnetwork using multi source remote sensing data. Remote sensing of natural resources, 32(4), 97–104. https://doi.org/10.6046/gtzyyg.2020.04.14
Venkataramani, K., Marshak,C. Z., Bekaert, D., Simard, M., Denbina, M., Handwerger, A. L.,Chan, S. (2023). Harmonizing SAR and Optical Data to Map Surface Water Extent: A Deep Learning Approach. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 3349–3352. https://doi.org/10.1109/IGARSS52108.2023.10282284
Xie, Y., Feng, M., Wang, C., Yang, W., Sun, H., Yang, C., Jing, B., Qiao, X., Saleem Kubar, M.,Song, J. (2020). Hyperspectral monitor on chlorophyll density in winter wheat under water stress. Agronomy Journal, 112(5), 3667–3676. https://doi.org/10.1002/agj2.20306
Wang, X., Shi, X., Ling, F. (2010). Images difference of ASAR data for rice crop mapping in Fuzhou, China. Geo-Spatial Information Science, 13(2), 123–129. https://doi.org/10.1007/s11806-010-0243-8
Yu, L., Liu, Y., Liu, T., Yan, F. (2020). Impact of recent vegetation greening on temperature and precipitation over China. Agricultural and Forest Meteorology, 295, 108197. https://doi.org/10.1016/j.agrformet.2020.108197
Hu, Q., Yin, H., Friedl, M. A., You, L., Li, Z., Tang, H.,Wu, W. (2021). Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates. Remote Sensing of Environment, 258, 112365. https://doi.org/10.1016/j.rse.2021.112365
Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., Han, J., Xie, J. (2021). Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches. Agricultural and Forest Meteorology, 297, 108275. https://doi.org/10.1016/j.agrformet.2020.108275
Zhou, Q., Ismaeel, A. (2021). Integration of maximum crop response with machine learning regression model to timely estimate crop yield. Geo-Spatial Information Science, 24(3), 474–483. https://doi.org/10.1080/10095020.2021.1957723
Tseng, G., Cartuyvels, R., Zvonkov, I., Purohit, M., Rolnick, D., Kerner, H. (2024). Lightweight, Pre-trained Transformers for Remote Sensing Timeseries arXiv:2304.14065. arXiv. https://doi.org/10.48550/arXiv.2304.14065
Zhang, G., Xiao, X., Dong, J., Kou, W., Jin, C., Qin, Y., Zhou, Y., Wang, J., Menarguez, M. A., Biradar, C. (2015). Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS Journal of Photogrammetry and Remote Sensing, 106, 157–171. https://doi.org/10.1016/j.isprsjprs.2015.05.011
Zhao, X., Nishina, K., Izumisawa, H., Masutomi, Y., Osako, S., Yamamoto, S. (2023). Monsoon Asia Rice Calendar: A gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images. Earth SystemScience Data 43, 43–55. https://doi.org/10.5194/essd-2023-283
dela Torre, D. M. G., Gao, J., Macinnis-Ng, C., Shi, Y. (2021). Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine. Geo-Spatial Information Science, 24(4), 695–710. https://doi.org/10.1080/10095020.2021.1984183
Torre, D. M. G. dela, Gao, J., Macinnis-Ng, C. (2021). Remote sensing-based estimation of rice yields using various models: A critical review. Geo-Spatial Information Science. https://www.tandfonline.com/doi/abs/10.1080/10095020.2021.1936656
Mishra, B., Busetto, L., Boschetti, M., Laborte, A., Nelson, A. (2021). RICA: A rice crop calendar for Asia based on MODIS multi year data. International Journal of Applied Earth Observation and Geoinformation, 103, 102471. https://doi.org/10.1016/j.jag.2021.102471
Nalepa, J., Tulczyjew, L., Myller, M., Kawulok, M. (2019). Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
(arXiv:1907.11935v1). http://arxiv.org/abs/1907.11935
Jonak, M., Mucha, J., Jezek, S., Kovac, D., Cziria, K. (2024). SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using superresolution. Agricultural Systems, 216, 103876. https://doi.org/10.1016/j.agsy.2024.103876
Ujoh, F., Igbawua, T., Ogidi Paul, M. (2019). Suitability mapping for rice cultivation in Benue State, Nigeria using satellite data. Geo-Spatial Information Science, 22(4), 332–344. https://doi.org/10.1080/10095020.2019.1637075
Han, X., Kuang, Q., Jin, M., Xie, Z., Zheng, L. (2009). Synthesis of Titania Nanosheets with a High Percentage of Exposed (001) Facets and Related Photocatalytic Properties. Journal of the American Chemical Society, 131(9), 3152–3153. https://doi.org/10.1021/ja8092373
Clifton, K. E., Bradbury, J. W., Vehrencamp, S. L. (1994). The fine-scale mapping of grassland protein densities. Grass and Forage Science, 49(1), 1–8. https://doi.org/10.1111/j.1365- 2494.1994.tb01970.x
The size of the land carbon sink in China | Nature. (n.d.). Retrieved November 20, 2023, from https://www.nature.com/articles/s41586-021-04255-y
Zhang, M., Liu, L., Jin, Y., Lei, Z., Wang, Z., Jiao, L. (2024). Tree-shaped multiobjective evolutionary CNN for hyperspectral image classification. Applied Soft Computing, 152, 111176. https://doi.org/10.1016/j.asoc.2023.111176
Nalepa, J., Myller, M., Imai, Y., Honda, K., Takeda, T.,Antoniak, M. (2020). Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders. IEEE Geoscience and Remote Sensing Letters, 17(11), 1948–1952. https://doi.org/10.1109/LGRS.2019.2960945
R, S. K., P, M., R, J., S, K. M., S, B. (2022). Video Saliency Detection by using an Enhance Methodology Involving a Combination of 3DCNN with Histograms. International Journal of Computers Communications and Control, 17(2), Article 2. https://doi.org/10.15837/ijccc.2022.2.4299
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