Remote Sensing Satellites for Digital Earth

Fu, Weinong; Ma, Jianwen; Chen, Pei; Fang, Chen

doi:10.1007/978-981-32-9915-3_3

Cited by 89 publications

(53 citation statements)

References 29 publications

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“…SAR is an active Earth observation system that can be installed on aircraft, satellites, spaceships and other flight platforms. It can observe the earth on all days and in all weather and it has a certain surface penetration ability [22][23][24]. SAR mainly records all kinds of backscatter responses produced by crop canopy biophysical structure.…”

Section: Introductionmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

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The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: 1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; 2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; 3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and 4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).

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Section: Introductionmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

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“…Another shortcoming of the experiment is that only two data sources were compared, mainly as they are freely available compared to other sources that have finer ground sampling distance. In addition, useable remote sensing satellites with higher resolution (e.g., the China-Brazil Earth Resource Satellites, HJ−1A and HJ−1B environment and disaster reduction satellites, and high-definition Earth observation satellite GF−8), could be used for analyzing the cloud cover [63].…”

Section: Limitations and Prospectsmentioning

confidence: 99%

Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

et al. 2021

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Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.

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“…This is due to the possibility of unimpeded receipt of species information on objects and areas of interest, as well as the fact that the necessary information is obtained remotely. Also, the data obtained with the help of satellites and remote sensing systems on the territory of Donetsk and Luhansk regions and in Crimea are the only source of objective and operational information 2 . The information 1.…”

Section: Introductionmentioning

confidence: 99%

“…Москва : Юрайт, 2016. 397 с 2. Fu W., Ma J., Chen P., Chen F. Remote Sensing Satellites for Digital Earth.…”

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Use of Geospatial Information for Decision-Making in Environmental Monitoring of Temporarily Occupied Territories

Butko¹,

Shamrai

2021

Perspective Trajectory of Scientific Research in Technical Sciences

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Remote Sensing Satellites for Digital Earth

Cited by 89 publications

References 29 publications

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

Use of Geospatial Information for Decision-Making in Environmental Monitoring of Temporarily Occupied Territories

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