Browsing by Author "Vinod, N.T."

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    Estimation of Vertisols Soil Nutrients by Hyperion Satellite Data: Case Study in Deccan Plateau of India
    (Springer, 2022) Vinod, N.T.; Shetty, A.; Shrihari, S.
    Soil nutrients are essential for agricultural purpose. There are efforts for estimation of topsoil properties using visible and near infrared reflectance (VIS–NIR) of Hyperion satellite. Notwithstanding this, there should be more research on variety of soils and fields of practicable size especially in the Indian context, necessary for using this on a practical basis. To bridge this gap, estimation of selected soil nutrients (nitrogen (N), potassium (K), copper (Cu) and iron (Fe)) from small sized and randomly scattered vertisols fields taken up in Deccan plateau of India using Hyperion satellite data. The nutrient index (NI) for Fe was estimated to be higher (NI = 2.76) than other nutrients in the study area, which influences the spectral behavior. The pretreatment of Hyperion reflectance data by Savitzky-Golay filter (window size 15, second-order derivative) and partial least square regression (PLSR) analysis resulted in low to moderate estimations of soil nutrients. The variable importance projection (VIP) for each soil nutrient has been estimated. The important wavelengths were identified in the mid infrared region for nitrogen. For Potassium, the wavelengths were identified in the visible, near infrared and the mid infrared regions. The near infrared and midinfrared region for iron. Lastly for Cu, in the green region and mid infrared region were identified. The prediction accuracy for N, K, Fe, and Cu were estimated to be medium, with coefficients of determination values as 54%, 45%, 40%, and 41%, respectively. The vertisols in the study region demonstrated low reflectance that is deficient in humus, due to low permeability and moisture stress throughout the drought. Hence the presence of soluble nutrients concentration is low compared to other soils. In this study considering the results of R2, the iron has good prediction, then other soil properties. Thus, the present research implied that, Hyperion satellite data provides moderate potential to estimate the Indian vertisols soil nutrients. © 2022, Indian Society of Remote Sensing.
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    Spatial Variability of Organic Carbon and Soil pH by Geostatistical Approach in Deccan Plateau of India
    (Springer Science and Business Media Deutschland GmbH, 2023) Vinod, N.T.; Shetty, A.; Shrihari, S.
    Proper soil nutrient management is necessary to meet India’s rising population without degrading the environment. However, the state of soil organic carbon (SOC) and soil pH is a concern, especially in Indian vertisols that are productive when well managed. Due to a lack of scientific knowledge and poor soil management among the small-scale farmers (most Indian farmers hold less than 2 ha), the crop yield has declined. The current study examines the correlation between soil pH and SOM and their spatial variability in vertisols (Black-Cotton soil). Geostatistics and conventional statistics are used to produce the spatial distribution maps, with the R software and the SpaceStat. Sixty-eight soil samples at the root zone level (0–15 cm depth) are collected from Gulbarga Taluk, Karnataka, India. The random sampling method is adopted according to the agriculture fields distribution, and each sample consists of five subsamples. The soil pH was estimated by pH meter and SOC by Walkley and black method. The violin plots indicate that most soil pH samples range from 8.5 to 7.5 and SOM 0.20–0.50%. The Pearson correlation indicated a negative correlation between the two parameters (r = −0.21). In semivariogram analysis, the spherical and exponential models were best fitted for soil pH and SOM, respectively. The ordinary kriging accomplished by a traditional estimator is adapted for generating spatial distribution maps. In line with the negative correlation of soil pH and SOC, the predictable maps are the mirror images. The spatial variability maps give an overview of how extrinsic and intrinsic factors affect the availability of soil pH and SOC. In this region, the parent materials, fertilizers application, and agricultural practices are affecting the soil variability. Small scale farmers should assess these spatial variability maps before applying the fertilizers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.