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

Miran, Naser; Rasouli-Sadaghiani, MirHassan; Feiziasl, V; Sepehr, Ebrahim; Rahmati, Mehdi; Mirzaee, Salman

doi:10.1007/s10661-021-09397-0

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…In addition, to measure the trueness or exactness of the predicted results, the sensitivity of the metric has been calculated. Moreover, the sensitivity metrics were evaluated using Equation (7).…”

Section: Accuracy and Sensitivity Assessmentmentioning

confidence: 99%

“…Growing urbanization raises the question of the climate 5 ; this trend indicates a systematic decline in our capacity to make food by concentrating the infrastructure on the most fertile soil resources 6 . Moreover, this urbanization trend has resulted in an increasing dependency on ever bigger returns per unit volume on lands that are still available and on more remote soil farming and natural output 7 . Urban regions are visually identifiable habitats from the foundation and neighboring natural environments in terms of physical characteristics 8 .…”

Section: Introductionmentioning

confidence: 99%

“…6 Moreover, this urbanization trend has resulted in an increasing dependency on ever bigger returns per unit volume on lands that are still available and on more remote soil farming and natural output. 7 Urban regions are visually identifiable habitats from the foundation and neighboring natural environments in terms of physical characteristics. 8 Global change may be detected and monitored using satellite data.…”

Section: Introductionmentioning

confidence: 99%

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An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

Anand

Jain

Shukla

2023

Concurrency and Computation

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Summary The usage of satellite images has grown rapidly in digital applications. Predicting features in the satellite images using conventional techniques is not an easy task as the images captured from satellites are more complex with highly noisy features. Hence, traditional imaging algorithms cannot analyze the image features and specify the soil textures. In this work, the Tarakeswar satellite images were taken to estimate the soil textures and crops yielding. Hence, to find the soil's chemical properties and suitable crops, the novel bat‐based U‐Net feature prediction system (BUFPS) was developed with the required features. The imported satellite images were initially filtered and entered into the classification phase to forecast the present chemical features and suitable crops in specific soils. After detecting the chemical features, soil textures like sandy, silt, and clay were categorized; the crops like jute, rice, lentil, and potato were considered. Finally, the planned model is executed in the MATLAB environment and has gained outstanding results by achieving the lowest error rate of 8% and a high prediction score of 92%. The proposed model has achieved exact soil texture analysis for complex satellite images. The gained texture analysis outcome is quite better than other existing models.

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Section: Accuracy and Sensitivity Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

Anand

Jain

Shukla

2023

Concurrency and Computation

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“…3). According to the past researches that have studied and investigated the amount of Fe and Zn without taking into account the elevations changes, it has been reported that the lack of Fe and Cu are more than that of Zn and Mn in the agricultural lands of northwest Iran (Miran et al, 2021).…”

Section: Classi Cation Of Soil Properties Based On the Niv Indexmentioning

confidence: 99%

Comparative assessment of soil fertility across varying elevations

Aqdam

Asadzadeh

Rezapour

et al. 2023

Environ Monit Assess

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Soil fertility and nutrient management are the main factors that directly affect the yield of the product. To evaluate the soil fertility at different elevations (including 1600-2000, 2000-2400, 2400-2800, and >2800 m), 350 surface soil samples (0-30 cm) were collected from the agricultural lands of northwestern Iran.Then, soil properties, including soil texture (%), CaCO 3 (%), pH, EC (dS.m -1 ), organic matter (OM) (%), and soil nutrients (macro and micro) (mg.kg -1 ), were measured. Finally, the interpretation and classi cation of the soil samples were made using the nutritional value index (NIV) based on the Gomez and Common methods. The comparison of the NIV index based on elevation changes showed that the Gomez method tends to classify the soil properties in the optimal class due to its tendency towards the center of the data. But the Common method is more consistent with reality. After classifying the NIV index using the Common method, it was determined that CaCO 3 (%) and soil salinity is not the main problem for soil fertility in different elevation classes. However, in all elevations, high pH, low OM (%) at elevations >2800 m, total nitrogen (TN) (mg.kg -1 ), available phosphorous (AP) (mg.kg -1 ), and micronutrients de ciencies (except Zn at the elevation of 1600-2000 m) are the main limiting factors for soil fertility of agricultural lands. Therefore, to improve the soil fertility in all the elevations of the agricultural lands in the northwest of Iran, there is a need to improve the condition of macro and micronutrients (mg.kg -1 ), especially TN (mg.kg -1 ). Highlights 1. The NIV is a suitable and practical index for evaluating soil fertility.2. At all elevations, TN and pH were the factors of limited soil fertility.3. The EC and CaCO 3 do not limit soil fertility at any elevation.

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“…Presently, most of the models used for spatial prediction of TN are divided into two categories. The first type is linear models, which are constructed by simulating the linear relationship between the reflectance of remote sensing image bands and the TN content, and thus inverse models, including partial least squares regression (PLSR) [8][9][10], multiple linear regression (MLR) [11,12], and other models. However, due to the multiple and complex relationships between the reflectance of multispectral image bands and soil nutrient content, the constructed linear models are not sufficient to reflect the spatial distribution of nutrients well and are lacking in prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

Liu

Lei

et al. 2022

Sustainability

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In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inversion models of partial least squares regression (PLSR), back propagation neural network (BPNN) and support vector machines (SVM) with remote sensing data to predict the TN content in Datong County, Xining City, Qinghai Province, China. The results showed that the average TN content was 1.864 g/kg, and the coefficient of variation (CV) was 30.596%. The prediction accuracy of the SVM model (R2 = 0.676, RMSE = 0.296) among the three inversion models was higher than that of the BPNN model (R2 = 0.560, RMSE = 0.305) and the PLSR model (R2 = 0.374, RMSE = 0.334). The model with the highest accuracy predicted the spatial distribution of TN, and TN content showed a spatial distribution trend which was high in the northwest and low in the southeast, and gradually decreased from north to south. This study provides reference basis and support for soil fertility evaluations and sustainable agricultural development.

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

Cited by 10 publications

References 31 publications

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

Comparative assessment of soil fertility across varying elevations

Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

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