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

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.

Crop Classification based Multi-Temporal Sentinel2 Data in the ShiYang River Basin

2020 ◽  
Author(s):  
Zhiwei Yi
Keyword(s):  
River Basin ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
High Spatial Resolution ◽  
Spectral Information ◽  
Resource Conflicts ◽  
Shiyang River Basin ◽  
Red Edge ◽  
Multi Temporal ◽  
Crop Classification

<p>     Accurate and timely spatial distribution of crops is of great significance to agricultural security issues. They are not only important parameters for regional crop growth monitoring, yield prediction, model simulation, but also important basis for government decision-making to assess food security and predict comprehensive productivity of agricultural resources. The Shiyang River Basin is the inland river basin with the highest development intensity and the most prominent water resource conflicts in arid regions of Northwest China. The timely and accurate acquisition of crop distribution information in the watershed can provide data support for alleviating the water resource conflicts in the Shiyang River Basin. However, there is no publically available in-season crop distribution information with high spatial resolution in the watershed. To solve this problem and generate accurate ,in-season crop-type map with high spatial resolution, this study uses multi-temporal sentinel2 data to generate spectrum, texture, vegetation index, red edge multi-feature sets while exploiting random forest classification algorithm(RF).the study designed Experiments to evaluate what kind of feature is most effctitve and further dig which spectral information is most important for the training RF model to map crop types in Shiyang River Basin. The experimental results show that the spectral information outperforms other features in the highest classification accuracy, followed by vegetation index information and red edge index information, and texture information has no effect on crop classification in the watershed. Temporal information has a significant impact on crop classification. the classification accuracy increases with the progression of time, and the accuracy reaches a plateau (94%)around DOY 218. The highest accuracy for single-date classification is 87%, and for multi-temporal is 94.8%. The classification performance of SWIR1 band of Sentinel2 is better than the visible and near-infrared bands commonly used in crop classification at present, and crop classification based on the Sentinel2 Visible-NIR-SWIR1 three-band combination mode can achieve the effect of full-band classification. The Visible -NIR-SWIR1 data of sentinel2 before DOY218 was used to generate the 2019 crop distribution map of Shiyang River Basin, with an overall accuracy of 93.5%.</p>

Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin

2019 ◽  
Vol 171 ◽  
pp. 36-50 ◽  
Author(s):  
Laura Piedelobo ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Amal Chakhar ◽  
Susana Del Pozo ◽  
...  
Keyword(s):  
Time Series ◽  
River Basin ◽  
Landsat 8 ◽  
Crop Classification ◽  
Sentinel 2

scholarly journals Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

2020 ◽  
Vol 12 (9) ◽  
pp. 1449
Author(s):  
Elahe Akbari ◽  
Ali Darvishi Boloorani ◽  
Najmeh Neysani Samany ◽  
Saeid Hamzeh ◽  
Saeid Soufizadeh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Importance Measure ◽  
Swarm Optimization ◽  
Feature Sets ◽  
Classification Feature ◽  
Spectral Bands ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Sentinel 2

Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.

scholarly journals ASSESSMENTS OF SENTINEL-2 VEGETATION RED-EDGE SPECTRAL BANDS FOR IMPROVING LAND COVER CLASSIFICATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 871-874 ◽  
Author(s):  
S. Qiu ◽  
B. He ◽  
C. Yin ◽  
Z. Liao
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Relative Importance ◽  
East Texas ◽  
Spectral Bands ◽  
Red Edge ◽  
Sentinel 2A ◽  
Land Cover Maps ◽  
Confusion Matrices ◽  
Sentinel 2

The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40 %, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.

scholarly journals MARRYING DEEP LEARNING AND DATA FUSION FOR ACCURATE SEMANTIC LABELING OF SENTINEL-2 IMAGES

2021 ◽  
Vol V-3-2021 ◽  
pp. 101-107
Author(s):  
G. Fonteix ◽  
M. Swaine ◽  
M. Leras ◽  
Y. Tarabalka ◽  
S. Tripodi ◽  
...  
Keyword(s):  
Deep Learning ◽  
European Space Agency ◽  
External Source ◽  
Convolutional Network ◽  
Spectral Bands ◽  
Space Agency ◽  
Semantic Labeling ◽  
Multi Temporal ◽  
Correction Step ◽  
Sentinel 2

Abstract. The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image.

scholarly journals Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

2020 ◽  
Vol 12 (15) ◽  
pp. 2406 ◽  
Author(s):  
Zhongbin Li ◽  
Hankui K. Zhang ◽  
David P. Roy ◽  
Lin Yan ◽  
Haiyan Huang
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Detailed Examination ◽  
Modulation Transfer ◽  
Computationally Efficient ◽  
Red Edge ◽  
Shortwave Infrared ◽  
Sentinel 2 ◽  
Surface Changes

Combination of near daily 3 m red, green, blue, and near infrared (NIR) Planetscope reflectance with lower temporal resolution 10 m and 20 m red, green, blue, NIR, red-edge, and shortwave infrared (SWIR) Sentinel-2 reflectance provides potential for improved global monitoring. Sharpening the Sentinel-2 reflectance with the Planetscope reflectance may enable near-daily 3 m monitoring in the visible, red-edge, NIR, and SWIR. However, there are two major issues, namely the different and spectrally nonoverlapping bands between the two sensors and surface changes that may occur in the period between the different sensor acquisitions. They are examined in this study that considers Sentinel-2 and Planetscope imagery acquired one day apart over three sites where land surface changes due to biomass burning occurred. Two well-established sharpening methods, high pass modulation (HPM) and Model 3 (M3), were used as they are multiresolution analysis methods that preserve the spectral properties of the low spatial resolution Sentinel-2 imagery (that are better radiometrically calibrated than Planetscope) and are relatively computationally efficient so that they can be applied at large scale. The Sentinel-2 point spread function (PSF) needed for the sharpening was derived analytically from published modulation transfer function (MTF) values. Synthetic Planetscope red-edge and SWIR bands were derived by linear regression of the Planetscope visible and NIR bands with the Sentinel-2 red-edge and SWIR bands. The HPM and M3 sharpening results were evaluated visually and quantitatively using the Q2n metric that quantifies spectral and spatial distortion. The HPM and M3 sharpening methods provided visually coherent and spatially detailed visible and NIR wavelength sharpened results with low distortion (Q2n values > 0.91). The sharpened red-edge and SWIR results were also coherent but had greater distortion (Q2n values > 0.76). Detailed examination at locations where surface changes between the Sentinel-2 and the Planetscope acquisitions occurred revealed that the HPM method, unlike the M3 method, could reliably sharpen the bands affected by the change. This is because HPM sharpening uses a per-pixel reflectance ratio in the spatial detail modulation which is relatively stable to reflectance changes. The paper concludes with a discussion of the implications of this research and the recommendation that the HPM sharpening be used considering its better performance when there are surface changes.

scholarly journals Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

2020 ◽  
Vol 12 (7) ◽  
pp. 1176 ◽  
Author(s):  
Yukun Lin ◽  
Zhe Zhu ◽  
Wenxuan Guo ◽  
Yazhou Sun ◽  
Xiaoyuan Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Potential ◽  
Large Scale ◽  
Temporal Frequency ◽  
Vegetation Indices ◽  
Stem Water Potential ◽  
Spectral Bands ◽  
Red Edge ◽  
Sentinel 2

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.

scholarly journals Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2027
Author(s):  
Mukunda Dev Behera ◽  
Surbhi Barnwal ◽  
Somnath Paramanik ◽  
Pulakesh Das ◽  
Bimal Kumar Bhattyacharya ◽  
...  
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Mangrove Forest ◽  
Dominant Species ◽  
Species Level ◽  
Mangrove Species ◽  
Central Wavelength ◽  
Red Edge ◽  
Chlorophyll Absorption ◽  
Sentinel 2

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.

scholarly journals Landsat-8 vs. Sentinel-2: Landuse Landcover Change Analysis and Differences in Gudur Municipality

2021 ◽  
Vol 9 (VI) ◽  
pp. 160-166
Author(s):  
R. Sanjeeva Reddy
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Landsat Imagery ◽  
European Space Agency ◽  
Landsat 8 ◽  
Landsat Data ◽  
Red Edge ◽  
Edge Band ◽  
Sentinel 2

With the recent free availability of moderate to high spatial resolution data (10m-30m), land use analysis became more robust. The launch of Sentinel-2a by the European Space Agency, coupled with the availability of free Landsat data, availed more analysis capabilities for the science community with a wide variety of temporal, spatial, and spectral capabilities. This study compares the synergetic use of Landsat and Sentinel-2 in mapping Land Use Land cover themes in Gudur, explicitly utilizing the red edge band of Sentinel-2. A combination of both sentinel and Landsat data results in higher spatial resolution. Classification of the red edge band produces better resolution than the classification of Landsat Imagery.

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