Abstract
Precise and cost-effective mapping of crop and other land cover (LC) types is essential for food security, precision agriculture, and water distribution management. However, accurate identification of the extent of crop and LC types is still a challenge. However, the efficiency of machine learning (ML) and deep learning (DL) algorithms were evaluated and assimilated with time-series satellite information and In-situ data for detecting crop and other LC types. Therefore, in the current study, a framework was established on a deep convolutional neural network (CNN) using Sentinel-2 time-series satellite datasets to identify rice crop and other LC types without survey data. The normalized difference vegetation index (NDVI) stack was developed through Google Earth Engine (GEE) and adopted as input features. Three widely used ML algorithms, random forest (RF), support vector machine (SVM) and classification and regression trees (CART), and four DL models (Swin Transformer (ST), HRNet, two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) were employed to perform the classification of rice crop and four non-crop classes. The overall accuracies were determined to be 93.95%, 91.67%, 89.80%, 86.89%, 81.01%, 76.51%, and 72.9% and the kappa coefficients were 92.35%, 89.49%, 87.13%, 83.52%, 76.12%, 70.56%, and 66.56%, of corresponding methods respectively. The findings showed that DL methods outperformed ML methods, while ST methods yielded the highest accuracy, and input data can be prepared for LULC classification without ground survey. Accurate classification of LC types and timely assessment of rice crop estimation can provide valuable statistics for state officials, decision-makers, and planners.
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Data availability
The datasets generated during the current study are available from the corresponding author upon reasonable request.
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All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by MUR. The first draft of the manuscript was also written by MUR and SAM commented on previous versions of the manuscript and also supervise the whole study. Both authors read and approved the final manuscript.
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Rasheed, M.U., Mahmood, S.A. A framework base on deep neural network (DNN) for land use land cover (LULC) and rice crop classification without using survey data. Clim Dyn 61, 5629–5652 (2023). https://doi.org/10.1007/s00382-023-06874-9
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DOI: https://doi.org/10.1007/s00382-023-06874-9