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Predicting soil nutrient contents using Landsat OLI satellite images in rain-fed agricultural lands, northwest of Iran

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Abstract

Soil nutrients are the key factors in soil fertility, which have important roles in plant growth. Determining soil nutrient contents, including macro and micronutrients, is of crucial importance in agricultural productions. Conventional laboratory techniques for determining soil nutrients are expensive and time-consuming. This research was aimed to develop linear regression (LR) models for remote sensing of total nitrogen (TN) (mg/kg), available phosphorous (AP) (mg/kg), available potassium (AK) (mg/kg), and micronutrients such as iron (Fe) (mg/kg), manganese (Mn) (mg/kg), zinc (Zn) (mg/kg), and copper (Cu) (mg/kg) extracted by DTPA in rain-fed agricultural lands in the northwest of Iran. First, 101 soil samples were collected from 0–30 cm of these lands and analyzed for selected nutrient contents. Then a linear regression along with principal component analysis was conducted to correlate soil nutrient contents with reflectance data of different Landsat OLI bands. Finally, the spatial distributions of soil nutrients were drawn. The results showed that there were linear relationships between soil nutrient contents and standardized PC1 (ZPC1). The highest significant determination coefficient with an R2 value of 0.46 and the least relative error (%) value of 11.97% were observed between TN and ZPC1. The accuracy of the other LR’s developed among other soil nutrient contents and remotely sensed data was relatively lower than that obtained for TN. According to the results obtained from this study, although remote sensing techniques may quickly assess soil nutrients, new techniques, technologies, and models may be needed to have a more accurate prediction of soil nutrients.

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Authors and Affiliations

  1. Department of Soil Science, Faculty of Agriculture, Urmia University, Urmia, West Azerbaijan Province, Iran

    Naser Miran, Mir Hassan Rasouli Sadaghiani & Ebrahim Sepehr

  2. Agricultural Research Education and Extension, Dryland Agricultural Research Institute (DARI), Maragheh, Iran

    Vali Feiziasl

  3. Department of Soil Science, Faculty of Agriculture, University of Maragheh, Maragheh, Iran

    Mehdi Rahmati

  4. Department of Soil Science, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

    Salman Mirzaee

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  1. Naser Miran
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  2. Mir Hassan Rasouli Sadaghiani
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  3. Vali Feiziasl
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  4. Ebrahim Sepehr
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  5. Mehdi Rahmati
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Correspondence to Naser Miran.

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Miran, N., Rasouli Sadaghiani, M.H., Feiziasl, V. et al. Predicting soil nutrient contents using Landsat OLI satellite images in rain-fed agricultural lands, northwest of Iran. Environ Monit Assess 193, 607 (2021). https://doi.org/10.1007/s10661-021-09397-0

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