Assessing soil productivity potential in arid region using remote sensing vegetation indices

文献类型： 外文期刊

第一作者： Fadl, Mohamed E.

作者： Fadl, Mohamed E.;AbdelRahman, Mohamed A. E.;AbdelRahman, Mohamed A. E.;El-Desoky, Ahmed I.;Sayed, Yasser A.

作者机构：

关键词： Remote sensing; soil productivity rating; SAVI; NDVI; EVI

期刊名称：JOURNAL OF ARID ENVIRONMENTS （ 影响因子：2.7； 五年影响因子：2.7 ）

ISSN： 0140-1963

年卷期： 2024 年 222 卷

页码：

收录情况： SCI

摘要： Remote sensing techniques offer practical benefits, particularly in sensitive ecosystems or areas with limited accessibility. However, field surveys allow for more accurate and detailed information about soil properties and productivity. Therefore, it is often recommended to combine remote sensing techniques with field surveys in order to obtain comprehensive and reliable results. The underlying basis of this study involves analyzing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI), as indicators of soil productivity. By utilizing biomass density as an additional indicator, the study aims to provide valuable insights into the productivity potential of agricultural areas. The results demonstrate a positive association between the Soil Productivity Rating (SPR) and wheat yield values for the year 2022, as evidenced by a coefficient of determination (r 2 ) value of 0.8214. This value indicates a moderately strong correlation between the SPR classes and wheat yields. Throughout the Ripening period, the NDVI and EVI indices exhibited a relatively strong correlation coefficient (r 2 = 0.987 and 0.873, respectively). On the other hand, the SAVI index displayed moderate to strong accuracy in estimating crop yield, with a coefficient of determination (r 2 ) ranging from 0.819 to 0.908. These results suggest that the NDVI index serves as the most dependable predictor of yield during all vegetation periods. This study provides a comprehensive understanding of soil productivity, but further research using controlled trial patterns with differential reference plants is needed for validation and improvement.

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