Temporal paradox in soil potassium estimations using spaceborne multispectral imagery

Zhang, Jing; Ji, Dongli; Du, Dong; Miao, Jinjie; Liu, Hongwei; Bai, Yaonan

doi:10.1016/j.catena.2020.104771

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…The data for both satellites were taken for post-monsoon season to avoid a mismatch in the results. Pre-processing of the satellite image consists of the atmospheric correction, which has been done to get the actual surface reflectance using the Fast line-of-Site Atmospheric Analysis of Spectral Hypercubes (FLAASH) tool in the ENVI 5.0 software (Zhang et al 2020). Layer stacking has been performed on the downloaded image.…”

Section: Satellite Data Extraction For the Preparation Of Indicesmentioning

confidence: 99%

Spatial analysis of soil quality using geospatial techniques in Botanic Garden of Indian Republic, Noida, Uttar Pradesh, India

Babbar

Chauhan

Sharma

et al. 2022

Environmental Sustainability

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Ex-situ conservation places such as botanical gardens require sufficient soil quality to support introduced species from various phytogeographical zones. The soil quality of the Botanic Garden of Indian Republic (BGIR), Noida, Uttar Pradesh, was evaluated to quantify soil nutrients. The dependency of one nutrient on the other nutrients was investigated using Pearson correlation and Multilinear regression analysis (MLRA). At the 0.05 level of significance, the nutrients Log10S and Log10EC (r = 0.97), N and OC (r = 0.98), Mn and OC (r = 0.97), Mn and N (r = 0.92), Ca and pH (r = − 0.91), Cu and Fe (r = 0.94) were found to be associated. Correspondence Analysis (C.A.) has been performed to find the association of soil elements with the soil type of study site. The spatial indices like NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), NPCRI (Normalized Pigment Chlorophyll Index), RDVI (Renormalized Difference Vegetation Index) have shown significant correlation with the Log10S, Mg, Log10Zn, B and Fe respectively (with respective Pearson correlation coefficient r = 0.88, r = − 0.90, r = − 0.93, r = 0.91, r = 0.92 at P < 0.05). ARVI, along with other indices SCI (Soil Composition Index), NDMI (Normalized Difference Moisture Index), and MSAVI (Modified Soil Adjusted Vegetation Index), are also the predictor variables for Log10Zn (r = − 0.89, r = − 0.88 r = 0.92 at P < 0.05 respectively). MAVI2 (Moisture Adjusted Vegetation Index) positively correlates with OC, Mn content (r = 0.91, r = 0.93 respectively). MSAVI is negatively interrelated with Ca (r = − 0.89), SCI is negatively interrelated with Log10 K (r = − 0.98), BSI (Bare Soil Index) is positively associated with pH (r = 0.91), and negatively with Ca (r = − 0.93). At the same time, other indices like SAVI (Soil Adjusted Vegetation Index), SATVI (Soil Adjusted Total Vegetation Index), NDWI (Normalized Difference Water Index), and DVI (Difference Vegetation Index) have failed to explain the presence of soil nutrients based on spectral reflectance. This study is important for understanding the changing nutrient status of soil at the conservation site for successfully establishing plants from different phytogeographical zones.

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Section: Satellite Data Extraction For the Preparation Of Indicesmentioning

confidence: 99%

Spatial analysis of soil quality using geospatial techniques in Botanic Garden of Indian Republic, Noida, Uttar Pradesh, India

Babbar

Chauhan

Sharma

et al. 2022

Environmental Sustainability

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“…Further, the soils structure can influence its spectral brightness. Unstructured soils, for example, reflect approximately 10-15% lighter than well-structured soils, underscoring the intricate interplay of soil properties on its spectral characteristics (Kravtsova, 2005;Jing et al, 2020;Wang et al, 2022;Yang et al, 2021;Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…In a distinct approach (Mirzaee et al, 2016) authors applied geostatistical methods, specifically different types of the kriging method, to predict SOM content in soils. In the study (Jing et al, 2020) authors examined the impact of time gaps between field sampling and the acquisition of Landsat TM/OLI satellite data on soil nutrient predictions, using both MLR and artificial neural network methodologies. According to study (Ahmed and Iqbal, 2014;Fiorio and Demattȩ, 2009), authors employed MLR to correlate soil surface variables with spectral data.…”

Section: Introductionmentioning

confidence: 99%

Application of remote sensing methods for statistical estimation of organic matter in soils

Belenok,

Hebryn-Baidy,

Bіelousova

et al. 2023

Earth sci. res. j.

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The availability of reliable information on the physicochemical properties of soils is a necessary tool for maintaining and improving fertility and effective optimization of agricultural land management in many countries. However, ground-based research methods require significant financial and time resources. It has been established that methods based on remote sensing data are an efficient, accurate, and less costly solution for studying different types of soil cover parameters. This work aims to determine the predicted indicator of humus content in soils in selected regions of the Kyiv region (Ukraine) with the corresponding soil types. For this, the spectral properties of chernozem soils were investigated based on Landsat 8 OLI satellite images. A mosaic of the mean spectral reflectance values for the study period (2013-2015) was created using the Google Earth Engine. The vegetation indices NDSI, NDWI, NDBI, MSAVI, and NDVI were used to identify bare soils. Using multiple linear regression, an optimal F-Comparing Nested Model was created for predicting humus content in soils, including seven parameters. The model's accuracy was estimated with such indicators R=0.95, R2= 0.90, σy = 0.16 %. The approach based on the proposed model can be used to support the adoption of the necessary management decisions to improve soil fertility and maintain balanced land use.

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A critical systematic review on spectral-based soil nutrient prediction using machine learning

Jain,

Sethia,

Tiwari

2024

Environ Monit Assess

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Temporal paradox in soil potassium estimations using spaceborne multispectral imagery

Cited by 7 publications

References 26 publications

Spatial analysis of soil quality using geospatial techniques in Botanic Garden of Indian Republic, Noida, Uttar Pradesh, India

Spatial analysis of soil quality using geospatial techniques in Botanic Garden of Indian Republic, Noida, Uttar Pradesh, India

Application of remote sensing methods for statistical estimation of organic matter in soils

A critical systematic review on spectral-based soil nutrient prediction using machine learning

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