Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sun, Chuanliang; Bian, Yan Shan; Zhou, Tao; Pan, Jianjun

doi:10.3390/s19102401

Cited by 116 publications

(80 citation statements)

References 63 publications

(87 reference statements)

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“…The work by Ban et al suggested that apart from speckles, the single parameter, high incidence angle SAR system used in their study did not provide sufficient differences to differentiate some crop species [15]. Thus, due to the limited viewing angles and orbits of available SAR data in most study cases, the sole use of SAR data may not be sufficient for crop type classification, especially in complex cropping systems.By the synergic use of microwave Sentinel-1 features and optical Sentinel-2 features, the accuracy of crop discrimination can be potentially improved [15][16][17][18]. However, despite the existing studies to combine SAR and optical images for crop classification, few studies (1) explored the performance of individual InSAR products (such as coherence, amplitude dispersion, and master versus slave intensity ratio) in crop type identification.…”

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confidence: 99%

“…By the synergic use of microwave Sentinel-1 features and optical Sentinel-2 features, the accuracy of crop discrimination can be potentially improved [15][16][17][18]. However, despite the existing studies to combine SAR and optical images for crop classification, few studies (1) explored the performance of individual InSAR products (such as coherence, amplitude dispersion, and master versus slave intensity ratio) in crop type identification.…”

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confidence: 99%

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Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

et al. 2020

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Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

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Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

et al. 2020

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“…These data, which are distributed free of charge, have an average revisit time of two days between 0 and 45 degrees latitude [18]. As a result, Sentinel-1 data have been widely used for land cover classification [19][20][21], monitoring of phenology [22], and biomass or production estimation. The interferometric wide-swath (IW) mode that offers VV (vertical transmit and receive) and VH (vertical transmit, horizontal receive) polarization data is normally used as the default acquisition mode [23,24].In addition to C-band SARs, the high sensitivity of the sigma naught of X-band sensors has been confirmed, and the potential of the X-band for identifying and forecasting crop growth using indices such as LAI has widely been confirmed [25,26].…”

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confidence: 99%

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Sonobe

2019

Remote Sensing

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The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.

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“…More detailed information of the SAR data used in this study is provided in Table 1. We preprocessed these SAR data with SARscape 5.2, including multi-look, coregistration, speckle filtering (a 5 × 5 window Lee filter [34]), geocoding, and radiometric calibration [14]. These images were geocoded using the ASTER GDEM and their digital numbers (DN) were converted to decibel (dB) scale backscatter coefficients.…”

Section: Remote Sensing Variablesmentioning

confidence: 99%

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

et al. 2019

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Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

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Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Cited by 116 publications

References 63 publications

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

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