Impact of changes in the relationship between salinity and soil moisture on remote sensing data usage in northeast Thailand

Maki, Masayasu; Sritumboon, Supranee; Srisutham, Mallika; Yoshida, Koshi; Homma, Koki; Sukchan, Somsak

doi:10.3178/hrl.16.54

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Additionally, the electrical conductivity of water is considerably higher than that of soil. Satellite remote sensing demonstrates a heightened sensitivity to electrical conductivity [54]. Consequently, the soil moisture in the cultivated land is higher than the normal values, which leads to a positive correlation with the measured data (MD).…”

Section: Discussionmentioning

confidence: 99%

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Zhu,

Tian,

et al. 2024

Remote Sensing

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Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product’s design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains.

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Section: Discussionmentioning

confidence: 99%

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Zhu,

Tian,

et al. 2024

Remote Sensing

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

Gerardo

Lima

2022

Agriculture

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Salinization is a major soil degradation threat in irrigated lands worldwide. In Portugal, it affects several pockets of irrigated agricultural areas, but the spatial distribution and intensity of soil salinity are not well known. Unlike conventional approaches to appraise soil salinity, remote sensing multispectral data have great potential for detecting, monitoring, and investigating soil salinity problems in agricultural areas. This study explores the assessment of soil salinity in irrigated rice cultivation fields using two types of multispectral-based indices calculated from Sentinel-2 satellite imagery: (i) vegetation indices (Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Generalized Difference Vegetation Index and Soil Adjusted Vegetation Index), to monitor the indirect effect of salinity on rice growth; and (ii) salinity indicators, namely those based on visible and near-infrared bands (Normalized Difference Salinity Index) and on shortwave infrared bands (Salinity Index ASTER). The data are for the Lower Mondego Valley (Central Portugal) and the period 2017–2018. Results revealed that salinity indices can be used for mapping soil salinity and constitute a valuable soil salinity assessment tool in rice cultivation areas affected by salinity issues. As there is less reported inventorying of spatial extent of such degradation in irrigated agricultural areas of Portugal, this innovative approach allowed by remote sensing technology can add to understanding the spatial extent of such areas and undertaking more such studies spatially and temporally.

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Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

Hihi,

Katlane,

Kilani

et al. 2023

Atmosphere

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The Kerkennah archipelago in Tunisia is one of the most vulnerable areas where the influence of climate change is undeniable. Soil salinization has emerged as a major consequence of climate variation on this island. In this study, remote sensing techniques were implemented to develop a model for predicting soil salinity from satellite images. Machine learning algorithms, Sentinel-1 and Sentinel-2 data, and ground truth measurements were used to estimate soil salinity. Several algorithms were considered to achieve accurate findings. These algorithms are categorized as polynomial regression, random forest regression, exponential regression, and linear regression. The results demonstrate that exponential regression is the pre-eminent algorithm for estimating soil salinity with high predictive accuracy of R2 = 0.75 and RMSE = 0.47 ds/m. However, spatiotemporal soil salinity maps reveal distinct and clear distribution patterns, highlighting salty areas (i.e., sebkhas) and agricultural parcels. Thus, through the model, we explore areas of moderately high salinity within agricultural lands that could be affected by irrigation practices. The present work demonstrates a reliable model for soil salinity monitoring in the Kerkennah archipelago and inspires more successful technologies such as remote sensing and machine learning to improve the estimation of soil salinity in climate-affected vulnerable areas.

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Impact of changes in the relationship between salinity and soil moisture on remote sensing data usage in northeast Thailand

Cited by 5 publications

References 14 publications

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

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

Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

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