Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal

Gerardo, Romeu; Lima, Isabel P. de

doi:10.3390/agriculture12091490

Cited by 10 publications

(4 citation statements)

References 76 publications

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“…From 2017 there is a spike in the number of published articles, with a drop in 2021. The years 2020 [ 5 , 8 , 9 , 26 , 43 , 47 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] and 2022 (until 20 October) [ 2 , 6 , 18 , 28 , 39 , 42 , 44 , 49 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ] showed a higher number of articles published, 20 per year, which represents 16% of the total, 32% combined.…”

Section: Resultsmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

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Section: Resultsmentioning

confidence: 99%

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Fernández-Urrutia

Arbeló

Gil

2023

Sensors

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“…The ecological stoichiometry of plant elements mainly included above-ground biomass carbon (AGC), litter carbon (LitterC), underground biomass carbon (BGC), plant root carbon, and total plant nitrogen (TN) collected from 56 plots. Soil properties mainly include soil formation time, 0-5 cm soil organic carbon density (OCD), and soil salinity index (SI) [53,54]. The soil formation time (SFT) is primarily based on Xue's research in 1994 [46], aligned in the ArcGIS 10.5 platform.…”

Section: Variable Data Source and Analysis Methodsmentioning

confidence: 99%

“…Soil organic carbon density data are sourced from global gridded soil information (Soilgrids) at a 250 m resolution (https://soilgrids.org/ (accessed on 19 October 2023)). Soil salinity index (SI) is computed on Google Earth Engine (GEE) by dividing the red band by the near-infrared band and then multiplying the result by 100 (SI = R N IR × 100) [53]. The environmental variables mainly include latitude and longitude, land cover type (CLCD), temperature (T), precipitation (Pre), and elevation (DEM).…”

Section: Variable Data Source and Analysis Methodsmentioning

confidence: 99%

Plant Diversity Response to Environmental Factors in Yellow River Delta, China

Xu,

Li,

Dou

et al. 2024

Land

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Plant diversity plays a crucial role in maintaining the functionality of a community and providing essential ecosystem services. Studying the plant diversity and its response to environmental factors in the Yellow River Delta, China, as a newly formed coastal land, is beneficial for protecting plant diversity in coastal areas and maintaining ecosystem stability. In this study, 56 sites were sampled to investigate the diversity of shrubs and herbaceous plant community and its response to environmental factors. The results indicate that the plants growing in the Yellow River Delta are predominantly from the Poaceae and Asteraceae families, with dominant communities consisting of species such as Suaeda salsa, Phragmites australis, Setaria viridis, Imperata cylindrica, and Tamarix chinensis. The Shannon–Wiener index, Simpson diversity index, and Pielou’s evenness index exhibit average values of 0.34, 0.21, and 0.25, respectively, within the Yellow River Delta. These values collectively indicate a low diversity in the vegetation community, reflecting a relatively uncomplicated ecological structure in this area. Additionally, there were no significant differences in biodiversity indices under different soil formation times, but under different land cover types, the biodiversity index of cropland was significantly higher than that of impervious land. Soil salinity index exhibited a significant negative correlation with plant diversity (R2 = 0.279, p < 0.001) in the Yellow River Delta. Moreover, elevation (R2 = 0.247, p < 0.001) and temperature (R2 = 0.219, p < 0.001) showed significant positive effects on plant diversity. Regarding the ecological stoichiometry of plant elements, soil organic carbon exhibited a negative effect on the biodiversity index, while litter carbon showed a positive effect. This may be attributed to the unique topographical conditions and soil salinization in the Yellow River Delta. Our findings provide important references for the sustainable management of wetlands in the Yellow River Delta under conditions of soil salinization.

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“…As research advances, an increasing number of scholars are advocating for the integration of remote sensing and farmland monitoring [5,6]. This approach offers a novel means of achieving extensive and swift detection of changes in farmland characteristics, leveraging the benefits of remote sensing, including its broad coverage, brief observation periods, substantial data volume, heightened efficiency, and cost-effectiveness [7].…”

Section: Introductionmentioning

confidence: 99%

Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

Wang,

Qu,

Yang

et al. 2024

Agronomy

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Soil salinization is a global issue confronting humanity, imposing significant constraints on agricultural production in the irrigated regions along the southern bank of the Yellow River. This, in turn, leads to the degradation of the ecological environment and inadequate grain yields. Hence, it is essential to explore the magnitude and spatial patterns of soil salinization to promote efficient and sustainable agricultural development. This study carried out a two-year surface soil sampling experiment encompassing the periods before spring irrigation and the budding, flowering, and maturity stages of sunflower fields in the irrigated area along the southern bank of the Yellow River. It employed deep learning in conjunction with multispectral remote sensing conducted by UAV to estimate soil salinity levels in the sunflower fields. Following the identification of sensitive spectral variables through correlation analysis, we proceeded to model and compare the accuracy and stability of various models, including the deep learning Transformer model, traditional machine learning BP neural network (BPNN), random forest model (RF), and partial least squares regression model (PLSR). The findings indicate that the precision of soil salinity content (SSC) retrieval in saline–alkali land can be significantly enhanced by incorporating the RE band of UAV data. Four SSC inversion models were developed using the most suitable spectral variables, resulting in precise soil salinity inversion. The model order based on accuracy and stability was Transformer > BPNN > RF > PLSR. Notably, the Transformer model achieved a prediction accuracy exceeding 0.8 for both the training and test datasets, as indicated by R2 values. The precision order of the soil salinity inversion model in each period is as follows: before spring irrigation > budding period > maturity period > flowering stages. Additionally, the accuracy is higher in the bare soil stage compared to the crop cover stage. The Transformer model exhibited RMSE and R2 values of 2.41 g kg−1 and 0.84 on the test datasets, with the salt inversion results aligning closely with field-measured data. The results showed that the Transformer deep learning model integrated with RE band data significantly enhances the precision and efficiency of soil salinity inversion within the irrigated regions along the south bank of the Yellow River.

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Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal

Cited by 10 publications

References 76 publications

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Plant Diversity Response to Environmental Factors in Yellow River Delta, China

Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

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