Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine

Cui, Jiaqi; Guo, Yulong; Xu, Qian; Li, Donghao; Chen, Weiqiang; Shi, Lingfei; Ji, Guangxing; Ling, Li

doi:10.3390/agronomy13020355

Cited by 9 publications

(4 citation statements)

References 47 publications

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“…The noise present in images, commonly known as speckle, is generated during the SAR image-generation process and has a significant impact on image quality and targetextraction capabilities [36]. One strategy to reduce this speckle involves multitemporal stack aggregation, allowing reduction without compromising spatial resolution.…”

Section: Speckle Reduction Filter Applicationmentioning

confidence: 99%

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Medina Medina,

Salas López,

Zabaleta Santisteban

et al. 2024

Agronomy

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One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

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Section: Speckle Reduction Filter Applicationmentioning

confidence: 99%

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Medina Medina,

Salas López,

Zabaleta Santisteban

et al. 2024

Agronomy

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“…GEE provides direct access to global Landsat reflectance data, providing real-time updates and simplified preprocessing. Its powerful server can efficiently process large-scale remote sensing data [32] and is supported by user-friendly built-in tools [33]. GEE overcomes spatiotemporal challenges and maintains high accuracy, precision, and recall.…”

Section: Advantages Of Utilizing Gee For Terrace Interpretation and A...mentioning

confidence: 99%

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Li,

Tian,

et al. 2023

Sustainability

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The Yellow River Basin (YRB) is a crucial ecological zone and an environmentally vulnerable region in China. Understanding the temporal and spatial trends of terraced-field areas (TRAs) and the factors underlying them in the YRB is essential for improving land use, conserving water resources, promoting biodiversity, and preserving cultural heritage. In this study, we employed machine learning on the Google Earth Engine (GEE) platform to obtain spatial distribution images of TRAs from 1990 to 2020 using Landsat 5 (1990–2010) and Landsat 8 (2015–2020) remote sensing data. The GeoDa software (software version number is 1.20.0.) platform was used for spatial autocorrelation analysis, revealing distinct spatial clustering patterns. Mixed linear and random forest models were constructed to identify the driving force factors behind TRA changes. The research findings reveal that TRAs were primarily concentrated in the upper and middle reaches of the YRB, encompassing provinces such as Shaanxi, Shanxi, Qinghai, and Gansu, with areas exceeding 40,000 km2, whereas other provinces had TRAs of less than 30,000 km2 in total. The TRAs exhibited a relatively stable trend, with provinces such as Gansu, Qinghai, and Shaanxi showing an overall upward trajectory. Conversely, Shanxi and Inner Mongolia demonstrated an overall declining trend. When compared with other provinces, the variations in TRAs in Ningxia, Shandong, Sichuan, and Henan appeared to be more stable. The linear mixed model (LMM) revealed that farmland, shrubs, and grassland had significant positive effects on the TRAs, explaining 41.6% of the variance. The random forest model also indicated positive effects for these factors, with high R2 values of 0.984 and 0.864 for the training and testing sets, respectively, thus outperforming the LMM. The findings of this study can contribute to the restoration of the YRB’s ecosystem and support sustainable development. The insights gained will be valuable for policymaking and decision support in soil and water conservation, agricultural planning, and environmental protection in the region.

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Section: Advantages Of Utilizing Gee For Terrace Interpretation and A...mentioning

confidence: 99%

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Li,

Tian,

et al. 2023

Preprint

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The Yellow River Basin (YRB) is a crucial ecological zone and an environmentally vulnerable re-gion in China. Understanding the temporal and spatial trends of terraced-field areas (TRAs) and the factors underlying them in the YRB is essential for improving land use, conserving water re-sources, promoting biodiversity, and preserving cultural heritage. In this study, we employed ma-chine learning on the Google Earth Engine (GEE) platform to obtain spatial distribution images of TRAs from 1990 to 2020 using Landsat 5 (1990－2010) and Landsat 8 (2015－2020) remote sens-ing data. The GeoDa software platform was used for spatial autocorrelation analysis, revealing distinct spatial clustering patterns. Mixed linear and random forest models were constructed to identify the driving force factors behind TRA changes. The research findings reveal that TRAs were primarily concentrated in the upper and middle reaches of the YRB, encompassing provinc-es such as Shaanxi, Shanxi, Qinghai, and Gansu, with areas exceeding 40,000 km2, whereas other provinces had TRAs of less than 30,000 km2 in total. The TRAs exhibited a relatively stable trend, with provinces such as Gansu, Qinghai, and Shaanxi showing an overall upward trajectory. Conversely, Shanxi and Inner Mongolia demonstrated an overall declining trend. When com-pared with other provinces, the variations in TRAs in Ningxia, Shandong, Sichuan, and Henan appeared to be more stable. The linear mixed model (LMM) revealed that farmland, shrubs, and grassland had significant positive effects on the TRA, explaining 41.6% of the variance. The ran-dom forest model also indicated positive effects for these factors, with high R² values of 0.983 and 0.86 for the training and testing sets, respectively, thus outperforming the LMM. The findings of this study can contribute to the restoration of the YRB's ecosystem and support sustainable devel-opment. The insights gained will be valuable for policymaking and decision support in soil and water conservation, agricultural planning, and environmental protection in the region.

show abstract

Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine

Cited by 9 publications

References 47 publications

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

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