Sentinel-2 Multi-Temporal Data for Rice Crop Classification in Nepal

Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a “dimension disaster”. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the “dimension disaster” caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.

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Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability

Remote Sensing ◽

10.3390/rs12101620 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1620 ◽

Cited By ~ 1

Author(s):

Weichun Zhang ◽

Hongbin Liu ◽

Wei Wu ◽

Linqing Zhan ◽

Jing Wei

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Rice Paddy ◽

Temporal Data ◽

Model Transferability ◽

Local Classification ◽

Multi Temporal ◽

Sentinel 2

Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

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Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China

Remote Sensing ◽

10.3390/rs12244052 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4052

Author(s):

Zhiwei Yi ◽

Li Jia ◽

Qiting Chen

Keyword(s):

River Basin ◽

Spatial Resolution ◽

Temporal Information ◽

Shiyang River Basin ◽

Spectral Bands ◽

Red Edge ◽

Multi Temporal ◽

Crop Classification ◽

Shortwave Infrared ◽

Sentinel 2

Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.

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CHESTNUT COVER AUTOMATIC CLASSIFICATION THROUGH LIDAR AND SENTINEL-2 MULTI-TEMPORAL DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2020-425-2020 ◽

2020 ◽

Vol V-3-2020 ◽

pp. 425-430

Author(s):

L. Alonso ◽

J. Armesto ◽

J. Picos

Keyword(s):

Castanea Sativa ◽

Temporal Data ◽

Managed Forests ◽

Cultural Economic ◽

Accuracy And Precision ◽

Multi Temporal ◽

Castanea Sativa Mill ◽

Northwestern Spain ◽

Rural Exodus ◽

Sentinel 2

Abstract. Chestnut (Castanea sativa Mill.) managed forests in Galicia (Northwestern Spain) have important cultural, economic and ecosystem values. However, due to rural exodus chestnut stands are being degraded. In order to take restoration and conservation measures knowledge of these forests' location, expanse and stage is needed. The available Spanish official cartography is based on photointerpretation which is inaccurate in terms of chestnut forest location and classification. However, remote sensing has recently been proven to be an effective tool for this purpose. Sentinel 2 multi-temporal classification is recently acquiring importance as a method to classify tree species. This project intends to detect chestnut forests using LiDAR and Sentinel 2 multi-temporal data and to compare these results with those obtained using the official cartography. It also intends to assess how the use of different phenological stages could improve classification results. The results obtained provide an overall accuracy of 76% when a three-month combination is used: (March, July and September) leaf-off stage, flowering and leaf-on stage. Overlapping of the current map and the official cartography lead to an accuracy and precision increase; highlighting the utility of the presented methodology to acquire knowledge about chestnut forests location.

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Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Applied Sciences ◽

10.3390/app10010238 ◽

2019 ◽

Vol 10 (1) ◽

pp. 238 ◽

Cited By ~ 8

Author(s):

Vittorio Mazzia ◽

Aleem Khaliq ◽

Marcello Chiaberge

Keyword(s):

Neural Networks ◽

Land Cover ◽

Machine Learning Algorithms ◽

Series Data ◽

Support Vector ◽

Agricultural Crops ◽

Sensing Applications ◽

Multi Temporal ◽

Crop Classification ◽

Sentinel 2

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks.

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IMPROVED IN-SEASON CROP CLASSIFICATION PERFORMANCE USING ENSEMBLE LEARNING TECHNIQUE: A CASE STUDY OF LEKODA INSURANCE UNIT, UJJAIN, MADHYA PRADESH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-477-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 477-481

Author(s):

V. Pandey ◽

K. K. Choudhary ◽

C. S. Murthy ◽

M. K. Poddar

Keyword(s):

Ensemble Learning ◽

Classification Performance ◽

Agricultural Crop ◽

Learning Techniques ◽

Learning Technique ◽

Multi Temporal ◽

Crop Types ◽

Crop Classification ◽

Sentinel 2

Abstract. The classification of agricultural crop types is an important application of remote sensing. With the improvement in spatial, temporal and spectral resolution of satellite data, a complete seasonal crop growth profile and separability between different crop classes can be studied by using ensemble-learning techniques. This study compares the performance of Random Forest (RF), which is a decision tree based ensemble learning method and Naïve Bayes ( a probabilistic learning technique) for crop classification of Lekoda gram panchayat, Ujjain district, using multi-temporal Sentinel 2 of Rabi 2017&ndash;18. The study area contains seven different classes of crop types, and in each class, we have used 65% of the ground data for training and 35% to test the classifier. The performance of RF classifier was found to be better than NB classifier. Kappa coefficient of RF classifier in mid of the crop season (December&ndash;January) was found to be 0.93. This result indicates that an accurate in-season crop map of the study area can be generated through integrated use of Sentinel 2 temporal data and RF classifier.

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Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

Remote Sensing ◽

10.3390/rs13071391 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1391

Author(s):

Ruben Fernandez-Beltran ◽

Tina Baidar ◽

Jian Kang ◽

Filiberto Pla

Keyword(s):

Crop Yield ◽

Large Scale ◽

Crop Yields ◽

Data Availability ◽

Rice Crop ◽

Estimation Methods ◽

Yield Estimation ◽

Multi Temporal ◽

Sentinel 2

Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data.

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Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102281 ◽

2021 ◽

Vol 96 ◽

pp. 102281

Author(s):

Tobias Gränzig ◽

Fabian Ewald Fassnacht ◽

Birgit Kleinschmit ◽

Michael Förster

Keyword(s):

Spatial Optimization ◽

Optimization Approach ◽

Fractional Coverage ◽

Invasive Shrub ◽

Ulex Europaeus ◽

Multi Temporal ◽

Sentinel 2

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Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

Author(s):

Katsuto Shimizu ◽

Tetsuji Ota ◽

Nobuya Mizoue ◽

Hideki Saito

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Airborne Lidar ◽

Canopy Height ◽

Forest Monitoring ◽

Series Data ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

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