Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

Mansaray, Lamin R.; Huang, Weijiao; Zhang, Dongdong; Huang, Jingfeng; Li, Jun

doi:10.3390/rs9030257

Cited by 82 publications

(55 citation statements)

References 49 publications

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“…The Landsat 8 OLI consists of eight multispectral bands (30 m), one panchromatic band (15 m), and two thermal bands (100 m). We assembled clear (0-5% cloud cover) Landsat 8 OLI images covering the study area in 2015 (including two adjacent overlapping Landsat footprints, path/row: 123/40 and 123/41) for the experiment (https://www.usgs.gov/) [49]. Radiometric calibration was performed to convert the digital number (DN) value to surface spectral reflectance, and atmospheric correction was conducted by using the FLAASH model of ENVI, version 5.1 [49].…”

Section: Landsat 8 Oli Datamentioning

confidence: 99%

“…We assembled clear (0-5% cloud cover) Landsat 8 OLI images covering the study area in 2015 (including two adjacent overlapping Landsat footprints, path/row: 123/40 and 123/41) for the experiment (https://www.usgs.gov/) [49]. Radiometric calibration was performed to convert the digital number (DN) value to surface spectral reflectance, and atmospheric correction was conducted by using the FLAASH model of ENVI, version 5.1 [49]. The NDVI was computed by band 4 (red) and band 5 (NIR) using the surface spectral reflectance images [50,51].…”

Section: Landsat 8 Oli Datamentioning

confidence: 99%

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RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

et al. 2019

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Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

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Section: Landsat 8 Oli Datamentioning

confidence: 99%

Section: Landsat 8 Oli Datamentioning

confidence: 99%

RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

et al. 2019

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“…The Sentinel-1 A and Sentinel-1B satellites are carrying C-band SAR sensors providing imagery in single (VV or HH) and dual polarisations (VV + VH or HH + HV) with high temporal resolutions (12 days to 6 days in regions towards the poles) and with a long-term monitoring possibility (European Space Agency (ESA, 2013). To our knowledge, only a few studies have used Sentinel-1 time-series data for rice crop mapping (Chen et al, 2016;Nguyen et al, 2016;Mansaray et al, 2017;Nguyen and Wagner, 2017;Torbick et al, 2017;Clauss et al, 2018) and none of them has applied Sentinel-1 for the detection of rice crop establishment methods. Given that most rice crop mapping algorithms depend on the detection of a strong water signal at the start of the growing season, which is typical of TP rice but not of DS rice, a more comprehensive understanding of the observable differences due to TP and DS would also improve the capability of rice mapping systems.…”

Section: Introductionmentioning

confidence: 99%

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Fikriyah

Darvishzadeh

Khan

et al. 2019

International Journal of Applied Earth Observation and Geoinfor

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A B S T R A C TImproved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates with the reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate the possibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season. different management approaches to create favourable rice-growing https://doi.

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“…For example, Blaes et al [20] reported a 5% increase in accuracy by adding ERS and RADARSAT imagery with optical SPOT and Landsat images. Lamin R. Mansaray et al [21] successfully used five Sentinel 1A and one Landsat-8 images for paddy rice field mapping in urban Shanghai and reported a 5% increase in overall accuracy than individuals. Rosenthal and Blanchard [22] and Brisco et al [23] also reported that the combination of optical and radar datasets in crop mapping increased the overall accuracy by 20% and 25%, respectively.…”

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confidence: 99%

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

et al. 2020

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A timely and accurate crop type mapping is very significant, and a prerequisite for agricultural regions and ensuring global food security. The combination of remotely sensed optical and radar datasets presents an opportunity for acquiring crop information at relative spatial resolution and temporal resolution adequately to capture the growth profiles of various crop species. In this paper, we employed Sentinel-1A (S-1) and Sentinel-2A (S-2) data acquired between the end of June and early September 2016, on a semi-arid area in northern Nigeria. A different set of (VV and VH) SAR and optical (SI and SB) images, illustrating crop phenological development stage, were employed as inputs to the two machines learning Random Forest (RF) and Support Vector Machine (SVM) algorithms to automatically map maize fields. Significant increases in overall classification were shown when the multi-temporal spectral indices (SI) and spectral band (SB) datasets were added with the different integration of SAR datasets (i.e., VV and VH). The best overall accuracy (OA) for maize (96.93%) was derived by using RF classification algorithms with SI-SB-SAR datasets, although the SI datasets for RF and SB datasets for SVM also produced high overall maize classification accuracies, of 97.04% and 97.44%. The outcomes indicate the robustness of the RF or SVM methods to produce high-resolution maps of maize for subsequent application from agronomists, policy planners, and the government, because such information is lacking in our study area.

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Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

Cited by 82 publications

References 49 publications

RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

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