Scholarlink

<p>Soil moisture and salinity are important factors influencing crop growth and effective water management. It is critical to ensure reliable and real-time monitoring of these factors at a reasonable cost. Thus, improving the estimation of water and soil salinity is crucial for conserving water and soil resources, enhancing crop production, and ensuring food security. The overall objective of the present study was to estimate soil moisture and salinity using times series of remote sensing-based models. Thus, Fourteen clear-sky Sentinel-2 images, taken from May to September 2022, were acquired for an experimental field in northwest Tunisia for analysis. To quantify spatial soil moisture, we applied the optical trapezoidal model (OPTRAM), which are based on relationships with vegetation indices such as NDVI, SAVI, EVI, and STR, to Sentinel 2 satellite data and evaluated the model for SM estimates using TEROS sensors data. Additionally, time series remote sensing data were used to calculate soil salinityand vegetation indices. Subsequently, a regression model was developed using these indices for precise soil salinity estimation. The results showed that the linear OPTRAM-SM model based on STR-NDVI and STR-SAVI, demonstrated promising performance across the study area. Furthermore, analysis of computed indices for soil salinity assessment revealed that the salinity index S2 had the highest correlation with measured data. Moreover, the development of regression models demonstrated the limitations of using a single environmental covariate as an explanatory variable to estimate soil salinity. These findings highlight the importance of remote sensing in better interpreting soil moisture and salinity at agricultural filed scale, thereby supporting efforts in precision agriculture related to irrigated and crop growth monitoring.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Soil moisture and salinity disaggregation by integrating remote sensing data with the OPTRAM model

  • 期刊论文

  • 发表日期：

    2025年8月2日

查看全文
  • M.’nassri Soumaia,
  • El Amri Asma,
  • Latrech Basma,
  • Lasram Asma,
  • Chaabane Balkis,
  • Allouche Khebour Faiza,
  • Majdoub Rajouene

摘要

Soil moisture and salinity are important factors influencing crop growth and effective water management. It is critical to ensure reliable and real-time monitoring of these factors at a reasonable cost. Thus, improving the estimation of water and soil salinity is crucial for conserving water and soil resources, enhancing crop production, and ensuring food security. The overall objective of the present study was to estimate soil moisture and salinity using times series of remote sensing-based models. Thus, Fourteen clear-sky Sentinel-2 images, taken from May to September 2022, were acquired for an experimental field in northwest Tunisia for analysis. To quantify spatial soil moisture, we applied the optical trapezoidal model (OPTRAM), which are based on relationships with vegetation indices such as NDVI, SAVI, EVI, and STR, to Sentinel 2 satellite data and evaluated the model for SM estimates using TEROS sensors data. Additionally, time series remote sensing data were used to calculate soil salinityand vegetation indices. Subsequently, a regression model was developed using these indices for precise soil salinity estimation. The results showed that the linear OPTRAM-SM model based on STR-NDVI and STR-SAVI, demonstrated promising performance across the study area. Furthermore, analysis of computed indices for soil salinity assessment revealed that the salinity index S2 had the highest correlation with measured data. Moreover, the development of regression models demonstrated the limitations of using a single environmental covariate as an explanatory variable to estimate soil salinity. These findings highlight the importance of remote sensing in better interpreting soil moisture and salinity at agricultural filed scale, thereby supporting efforts in precision agriculture related to irrigated and crop growth monitoring.