نقشه‌برداری رقومی کربنات کلسیم معادل و درصد رس خاک با استفاده از تصاویر ماهواره‌های لندست8 و پریسما توسط الگوریتم جنگل تصادفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم ومهندسی خاک دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران

2 عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران

چکیده

نقشه‌برداری خصوصیات خاک با استفاده از تصاویر ماهواره‌ای ابرطیفی و چند طیفی در کنار رویکردهای آماری و با استفاده از مدل‌های یادگیری ماشین از جمله جنگل‌های تصادفی پیشرفت زیادی در دقت و صحت نقشه‌های تهیه­شده داشته است. این تحقیق برای بررسی عملکرد تصاویر پریسماو لندست8 در مدل‌سازی کربنات‌کلسیم معادل و درصد رس با مدل جنگل تصادفی، در بخشی از اراضی شهرستان آبیک استان قزوین از مهرماه سال 1399 تا مهرماه 1401 انجام شده است. در ابتدا، با استفاده از 229 داده‌ که از خاک سطحی جمع‌آوری‌شده در منطقه آبیک استان قزوین به مساحت 60 هزار هکتار اندازه­گیری شد، در مرحله بعد مجموعه‌داده‌های طیفی دو ماهواره پریسماو لندست8 ، استخراج  و داده‌های بازتاب خاک به دست آمدند. در این تحقیق از شاخص­های طیفی، شاخص­های مدل رقومی ارتفاع و تجزیه مولفه­های اصلی به عنوان متغییر کمکی استفاده شد. در مرحله بعدی، مدل رگرسیون جنگل تصادفی جهت تخمین ویژگی‌های خاک با استفاده از 80% از داده­ها آموزش داده­شد و از 20% داده­ها برای آزمون مدل استفاده شد. نتایج نشان داد که بهترین دقت در بازیابی ویژگی‌های خاک­سطحی توسط داده‌های پریسما، با استفاده از مجموعه‌داده‌های کمکی تجزیه مولفه‌های اصلی، شاخص‌های طیفی و شاخص‌های مستخرج از مدل رقومی ارتفاع به دست آمد. به طور دقیق‌تر، استفاده از این سه­دسته داده، بیشترین ضریب تبیین، انحراف پیش‌بینی باقی‌مانده و نسبت عملکرد به فاصله بین چارکی و کمترین ریشه میانگین مربعات خطا  و ریشه میانگین مربعات خطا  نرمال شده را برای تخمین کربنات کلسیم معادل و درصد رس خاک نشان داد. بهترین مدل برای تخمین درصد رس، با مدل جنگل‌های تصادفی، شاخص‌های آماری (ضریب تبیین: 90/0؛ریشه میانگین مربعات خطا : 91/4؛ ریشه میانگین مربعات خطا  نرمال شده : 23/0؛ نسبت دامنه بین چارکی: 8/0؛ نسبت انحراف عملکرد: 29/2) و بهترین مدل برای تخمین کربنات کلسیم معادل، با مدل جنگل‌های تصادفی، شاخص‌های آماری (ضریب تبیین: 61/0؛ریشه میانگین مربعات خطا : 72/0؛ ریشه میانگین مربعات خطا  نرمال شده : 20/0؛ نسبت دامنه بین چارکی: 77/0؛ نسبت انحراف عملکرد: 27/1) به دست آمد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Digital mapping of soil properties (Calcium Carbonate and soil clay percentage) using landsat 8 and Prisma satellite images by the random forest algorithm

نویسندگان [English]

  • sajjad teimouri bardyani 1
  • Fereydoon Sarmadian 2
1 Department of Soil Science and Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 soil science department< faculty of agricultural engineering and technology, university of Tehran
چکیده [English]

Mapping soil properties using hyperspectral and multispectral satellite images, along with statistical approaches, and machine learning models such as Random Forests (RF), has shown great progress in accurately preparing agricultural maps. This study aimed to compare the performance of PRISMA and Landsat 8 images in modeling calcium carbonate and clay percentage using a Random Forest model. Firstly, Surface soil data was collected from Abik region of Qazvin province from October 2020 to October 2022. Furthermore, PRISMA and Landsat 8 spectral datasets were extracted from images downloaded from the websites of these two satellites, and soil reflectance data were obtained. The Random Forest regression model was then calibrated to estimate soil properties. The results of this study showed that the best accuracy in estimating soil characteristics using PRISMA data was obtained by using Auxiliary Variables such as principal components analysis, spectral indices, and indices extracted from the digital elevation model. The use of these three data sets provided the uppermost value for following statistical indices for estimating calcium carbonate and soil clay percentage: coefficient of determination (R2), and Ratio of Performance to Inter Quartile range (RPIQ), Ratio Performance Deviation (RPD) and the lowest Root Mean Squared Error (RMSE) and Normalized Root Mean Squared Error (NRMSE). The best model for estimating clay percentage, using the Random Forest model and statistical indices, had an R2 of 0.90, RMSE of 4.11, NRMSE of 0.18, RPIQ of 0.95, and RPD of 2.29. The best model for estimating calcium carbonate, using the Random Forest model and statistical indices, had an R2 of 0.62, RMSE of 0.72, NRMSE of 0.20, RPIQ of 0.77, and RPD of 1.27. The results supported the expectation of the good ability of the PRISMA imager to estimate surface soil properties.

کلیدواژه‌ها [English]

  • Random Forest
  • clay percentage
  • Calcium carbonate
  • PRISMA hyperspectral satellite

Digital Mapping of Soil Properties (Calcium carbonate and soil clay percentage) Using Landsat 8 and PRISMA Satellite Images by the Random Forest Algorithm

EXTENDED ABSTRACT

Introduction

Land management is recognized as a major challenge in solving global issues such as food, water, energy, environment, biodiversity and human health. To have accurate information about soil, soil mapping and assessment are essential. Imaging sensors including Landsat 8 and PRISMA with a wide range of spatial, temporal and spectral resolutions are important tools for soil mapping. In this research, the zoning of soil properties using PRISMA and Landsat 8 data and the effect of different inputs on the accuracy and correctness of maps have been investigated using the RF algorithm.

Material and Methods

In this study, soil properties in a region of Abik City in the Qazvin province of Iran were delineated using an RF algorithm based on laboratory data and remote sensing images. L2D surface images from PRISMA and Landsat 8 satellites, as well as a 30-meter digital elevation model, were employed for soil mapping. The remote sensing data underwent preprocessing steps, including the removal of problematic bands, geometric correction, and image calibration using ENVI 5.6 software. Additionally, 12 spectral indices, such as NDVI, and 20 indices were derived by combining two or more bands in the images with the assistance of SAGA GIS software and the digital elevation model.To analyze the spatial diversity of hyperspectral images, the principal component analysis (PCA) method was used and also the effect of different inputs on the RF algorithm were investigated. The random forest algorithm has very high accuracy in predicting soil properties due to its ability to model non-linear relationships between auxiliary variables and the target soil. This algorithm provides the possibility to determine the relative importance of environmental auxiliary variables simply. In this research, to validate the performance of the random forest model using data obtained from laboratory analysis and remote sensing images, RMSE, RPD, RPIQ and R2 statistics were calculated from the measured values of soil properties.

Results and discussion

The results show that the simultaneous use of spectral indices and principal component analysis does not improve accuracy, and the use of Digital elevation model indices has a great effect on improving the results. In general, using principal component analysis to improve the quality of generated calcium carbonate maps is more effective than using spectral indices. But it cannot be said which of the maps obtained from the analysis of the main components of satellite images or the maps obtained from spectral indices extracted from satellite images are more accurate and precise. Also, the results did not show that the derivatives of Landsat 8 or PRISMA images are more effective for preparing maps of soil CaCO3 values, but the predicted values using PRISMA and Landsat 8 satellite spectra were consistent with the trend of soil calcium carbonate changes in the region.

Also, the results showed that soil clay percentage maps that PRISMA-based models provide better results compared to Landsat8-based models. The accuracy of PRISMA satellite images for preparing the clay percentage map in the study area is higher than that of the Landsat8 satellite, due to the typical absorption characteristics in the SWIR spectral region. The use of the digital height model and the indicators derived from it increases the performance of the models. Also, the maps obtained from the analysis of the main components of satellite images are less accurate than the maps obtained from spectral indices extracted from satellite images. The values of clay percentage predicted using PRISMA and Landsat8 satellite spectra were consistent with the trend of changes in soil texture and soil clay percentage in the region.

Conclusion

The results showed that PRISMA images are more accurate than Landsat 8 due to their higher number of bands and greater spectral resolution. However, the high cost of PRISMA and its limited access to complete time series images are limitations of this method. The combined use of spectral indices, principal component analysis, and digital height models significantly improved the model's performance. However, a large number of inputs can limit the model's inputs, and optimizing the selection of features from each category can identify the best features and improve the model's performance.

 

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تحقیقات آب و خاک ایران
دوره 55، شماره 3
خرداد 1403
صفحه 381-399

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