An Improved Fmask Method for Cloud Detection in GF-6 WFV Based on Spectral-Contextual Information

Yang, Xiaomeng; Sun, Lin; Tang, Xinming; Ai, Bo; Xu, Hanwen; Wen, Zhen

doi:10.3390/rs13234936

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…In the study of the time series combination of corn, the optical image in June could not be collected because of cloud coverage. This problem is significant in the field of remote sensing [35][36][37][38]. In this paper, SAR images were used instead, and two series of images were selected, namely, GF-6/GF-3 and Sentinel-1/Sentinel-2.…”

Section: Discussionmentioning

confidence: 99%

Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images

Meng

Liu

et al. 2023

Land

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Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data.

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Section: Discussionmentioning

confidence: 99%

Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images

Meng

Liu

et al. 2023

Land

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Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images

Tang

Yao

Chen

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

Wang,

2024

Agronomy

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The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.

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An Improved Fmask Method for Cloud Detection in GF-6 WFV Based on Spectral-Contextual Information

Cited by 3 publications

References 24 publications

Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images

Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images

Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images

Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects

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