Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China

Wu, Jiayu; Jin, Shaofei; Zhu, Guannan; Guo, Jia

doi:10.3390/agronomy13061585

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…This study emphasizes the importance of combining data from Optical and SAR images through the multifunctional platform of GEE during the growth phases of rice cultivation (i.e., vegetative, reproductive, and maturation). However, previous studies [28,43] developed vegetation and farmland monitoring methods using remote sensing, relying solely on Optical images such as Landsat and Sentinel-2 databases. Similarly, other research [4,7,13,44,45] has employed Sentinel-1 time series (SAR data), primarily for mapping and monitoring rice crop stands, identifying land use and cover, and identifying water coverage.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Minor soil carbon pool changes can significantly alter atmospheric CO2 concentration, thereby affecting global carbon cycling and climate [2]. The vast majority of carbon stored in soil is organic carbon as the carbon component of organic matter [3][4][5]. Soil organic matter (SOM) is also the primary source of biological nutrients and energy in soil, and the SOM content is often used as a vital soil fertility evaluation indicator [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

Sui,

Jiang,

Lin

et al. 2024

Preprint

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Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

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“…Soil organic matter (SOM) is an important component of soil, which not only provides nutrients for plant growth but also improves soil physical, chemical, and biological conditions [5]. Obtaining the spatial distribution information of the SOM in cultivated land is a key prerequisite for the utilization and management of soil resources and can provide key basic data for agricultural disaster assessment and food security scenario simulations [6,7]. In addition, a large portion of the SOM is composed of organic carbon, which constitutes the largest carbon pool in terrestrial ecosystems [8].…”

Section: Introductionmentioning

confidence: 99%

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Sui,

Jiang,

Lin

et al. 2024

Agronomy

View full text Add to dashboard Cite

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.

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Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China

Cited by 8 publications

References 43 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)

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

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