Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

Peng, Yuanyuan; Zhao, Li; Hu, Yueming; Wang, Guangxing; Wang, Lu; Liu, Zhenhua

doi:10.3390/ijgi8100437

Cited by 52 publications

(36 citation statements)

References 41 publications

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“…In order to improve the prediction accuracy of the BP algorithm, GA was introduced to the BP model to optimize the weight and threshold selection of the neural network. GA has the advantages of only requiring fitting information and not tending to a local solution [38,39]. Thus, the combined GA-BPNN model was used to estimate CLQ in this study.…”

Section: Methodsmentioning

confidence: 99%

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Liu

Peng

Xia

et al. 2019

Sensors

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Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

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Section: Methodsmentioning

confidence: 99%

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Liu

Peng

Xia

et al. 2019

Sensors

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“…A greater RPD value indicates a higher accuracy for the quality of prediction models. The RPD > 2 corresponds to the models that can accurately predict the tested property; RPD between 1.4 and 2 indicates the models with a possible improvement, and RPD < 1.4 implies poor prediction ability of the models [46]. Then, the growth stage of rice corresponding to the most relevant vegetation index is determined as the optimal image date for CLQ evaluation.…”

Section: Methodsmentioning

confidence: 99%

The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

Xia

Peng

Liu

et al. 2019

Sensors

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This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

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“…Assessment of soil nutrient status using geospatial technology is of paramount importance for soil productivity, agricultural sustainability and food security Peng et al, [8]. The vegetation spectral response is used to deduce various soil conditions such as nutrient differences, water-holding capacity and eroded locations.…”

Section: Soil Nutrientmentioning

confidence: 99%

“…Evaluation and analysis of soil properties is the main application of geospatial technology in agriculture. Efficient monitoring of soil nutrient contents from geospatial technologies is very for farmland soil productivity, sustainable agricultural development and food security Peng et al, [8]. Remote sensing involves extraction of useful information from images and other forms of pictorial representation of an object captured from a distance.…”

Section: Introductionmentioning

confidence: 99%

Application of Geospatial Technology in Assessment of Spatial Variability in Soil Properties: A Review

Kaur

Singh

et al. 2020

CJAST

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Under the changing climatic scenarios, sustaining agricultural production and enhancing input use efficiency is highly crucial to ensure food security in future. As crop productivity is considerably affected by soil characteristics such as soil organic carbon (SOC), nutrient availability, pH, salinity and soil moisture etc., thus their spatial variability needs to be assessed for site-specific and more efficient management. RS, GIS and GPS can be used quite successfully for assessing spatial variability in these properties. Recently with the advent of highly sophisticated sensors, it is possible to assess various soil properties by observing spectral reflectance in different wavelength bands and computing various spectral indices from the data recorded through satellite remote sensing. Spectral reflectance in different wavelength bands viz. visible, thermal and microwave etc. along with different spectral indices computed from spectral reflectance viz. normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), ratio vegetation index (RVI), soil moisture index (SMI), normalised difference water index (NDWI) and normalized difference salinity index (NDSI) etc. are used to retrieve different soil properties from satellite data. Similarly, various spatial interpolation techniques viz. inverse distance weighting (IDW), ordinary kriging (OK), radial basis function (RBF) and empirical bayes kriging (EBK) etc. are used for spatial interpolation of various soil characteristics. A critical review concluded that geospatial techniques can be used successfully for retrieval and spatial interpolation of various soil properties, which can be highly beneficial in site specific management leading to improved input use efficiency and sustained agricultural productivity for future food security.

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Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

Cited by 52 publications

References 41 publications

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

Application of Geospatial Technology in Assessment of Spatial Variability in Soil Properties: A Review

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