10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product

Tran, Khuong H.; Zhang, Hankui K.; McMaine, John; Zhang, Xiaoyang; Luo, Dong

doi:10.1016/j.jag.2022.102692

Cited by 35 publications

(22 citation statements)

References 94 publications

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“…The Sentinel-2 image is tiled using the Military Grid Reference System (MGRS) grid and projected in the Universal Transverse Mercator (UTM) map projection, with each tile covering 109 × 109 km. The Sentinel-2 MSI has ten bands designed for land applications, including four 10 m bands (band 2-blue (490 nm), band 3-green (560 nm), band 4-red (665 nm), and band 8-nearinfrared (NIR) (842 nm)), four 20 m vegetation red edge bands (band 5 (705 nm), band 6 (740 nm), band 7 (783 nm), and band 8A (865 nm)), and two 20 m shortwave infrared bands (band 11 (1610 nm) and band 12 (2190 nm)) [63,89]. The 20 m Scene Classification (SCL) band is also produced by the Sen2Cor algorithm and included in the Sentinel-2 data to provide a classification map at 20 m pixels (snow, ice, cloud, cloud shadows, saturation, vegetation, not vegetated, and water), which will be nearest-neighbor resampled to 10 m to define cloud-free observations in our study.…”

Section: Sentinel-1 Sar Datamentioning

confidence: 99%

“…Although the supervised classification method is able to map waterbodies effectively, the ground truth dataset is required to train the algorithm for classifying other unknown pixels and validate classification results [62]. However, collecting training and testing samples usually involves field visits, which are expensive, time-consuming, and challenging in remote and inaccessible regions [63]. This process is even more complex or unable to be conducted in areas with high water dynamics or severe flood events such as the VMD.…”

Section: Introductionmentioning

confidence: 99%

“…It has been demonstrated as the most simple and efficient method for surface water mapping [65,70,71], even for complex waterbodies areas (e.g., submerged vegetation, sedimented, and turbid water) [62]. It should be noted that recent methods using classical machine learning (support vector machine, decision tree, and random forest) and deep learning (deep neural networks, convolutional neural networks, and recurrent neural network) have been increasingly applied in many research activities with high overall accuracy, such as LULC classification, crop monitoring, and water detection [55,62,63,72,73]. However, these methods are commonly supervised learning, which is computationally costly, hard to employ for large scales, and require ground truth samples [74][75][76].…”

Section: Introductionmentioning

confidence: 99%

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Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Tran

Menenti

2022

Remote Sensing

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The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.

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Section: Sentinel-1 Sar Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Tran

Menenti

2022

Remote Sensing

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“…This research used the Random Forest (RF) algorithm to identify different types of crops at first. RF is a robust voting decision algorithm by integrating multiple independent decision trees, and it is widely used for land cover classification in previous studies [14,40,41]. This algorithm is now well-integrated into the GEE platform with the classification library of "ee.Classifier.smileRandomForest()".…”

Section: Crop Type Identification and Rotation Mappingmentioning

confidence: 99%

A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery

2022

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Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation of “small farmers in a big country”. However, crop rotation mapping for smallholder agriculture systems remains challenging due to the diversity of crop types, complex cropping practices, and fragmented cropland. This research established a sub-seasonal crop information identification framework for crop rotation mapping based on time series Sentinel-2 imagery. The framework designed separate identification models based on the different growth seasons of crops to reduce interclass similarity caused by the same crops in a certain growing season. Features were selected separately according to crops characteristics, and finally explored rotations between them to generate the crop rotation map. This framework was evaluated in the study area of Shandong Province, China, a mix of single-cropping and double-cropping smallholder area. The accuracy assessment showed that the two crop maps achieved an overall accuracy of 0.93 and 0.85 with a Kappa coefficient of 0.86 and 0.80, respectively. The results showed that crop rotation practice mainly occurred in the plains of Shandong, and the predominant crop rotation pattern was wheat and maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation Index (SAVI), Green Chlorophyll Vegetation Index (GCVI), red-edge, and other spectral bands during the peak growing season enabled better performance in crop mapping. This research demonstrated the capability of the framework to identify crop rotation patterns and the potential of the multi-temporal Sentinel-2 for crop rotation mapping under smallholder agriculture system.

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“…Due to the influence of clouds, rain, and haze, the features generated by optical satellite images suffer the problem of discontinuity, which significantly increases the difficulty of crop classification. Different feature composites, such as the median composite [15,29,30], the percentile composite [19,31,32], and temporal interpolation [3,33], have been proposed to overcome or solve the discontinuity problem in optical satellite images. Many previous studies have indicated that the median composite was generally superior to the mean composite because the latter does not reflect the actual physical observation [33] and is susceptible to extreme values [29].…”

Section: Introductionmentioning

confidence: 99%

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

Zeng

Tian

et al. 2022

Remote Sensing

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Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.

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10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product

Cited by 35 publications

References 94 publications

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

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