تخمین مقادیر شن در خاک های استان مازندران با استفاده از فناوری سنجش مجاورتی

شن از مهم‌ترین اجزای بافت خاک است که برای عملیات مدل‌سازی زیست‌محیطی و پهنه‌بندی رقومی خاک، باید مورد توجه واقع شود. از آن جا که این ویژگی، دستخوش تغییرپذیری ‌های مکانی بوده، لذا تشخیص، پهنه‌بندی و پایش آن در مقیاس وسیع و با روش‌های نمونه‌برداری و تحلیل آزمایشگاهی معمول، بسیار هزینه ‌بر و وقت ‌گیر است. با ظهور فناوری مجاورت‌سنجی بازتابی (vnir-drs)، روزنه‌ ای در بررسی این ویژگی خاک ایجاد شده است. طی تحقیق حاضر، از طیف‌سنجی بازتابی مجاورتی، برای بررسی اجزای شن در قسمت ‌هایی از استان مازندران استفاده شد. جمعاً 128 نمونه از عمق 20 سانتی‌متری سطح خاک، براساس روش نمونه‌برداری طبقه‌بندی شدۀ تصادفی و نیز با کمک اطلاعات جانبی همچون: زمین‌شناسی، کاربری اراضی، نقشۀ راه ‌ها، و خاک شناسی استان جمع ‌آوری شد. در ابتدا مجموع نمونه‌ها به دو قسمت برای عملیات واسنجی و اعتبارسنجی تقسیم شد. با بهره‌گیری از تحلیل رگرسیون چندمتغیرۀ plsr و براساس روش اعتبارسنجی متقاطع و عملیات پیش‌ پردازشی چون: میانگین ‌گیری (روش کاهش داده‌های طیفی)، هموارسازی و مشتق اول طیفی براساس الگوریتم ساویتسکی-گولای، مدل نهایی با تعداد 2 و 4 عامل؛ به‌ترتیب: با همبستگی دوطرفۀ پیرسون(rp) حدود 0.83 و0.82 ، ضریب تبیین(r2p) حدود 0.68 و 0.67 ، میانگین مربعات خطای کالیبراسیون(rmsep) حدود 8.68 و 8.83%، و نیز rpdp تقریبی 1.78 و 1.75، rpiqp تقریبی 2.45 و 2.41 (ست اعتبارسنجی مستقل)، به‌عنوان مطلوب ‌ترین مدل به‌منظور برآورد مقادیر شن منطقۀ مورد مطالعه، شناخته شد که نتایج، نشان‌دهندۀ توانایی مناسب مدل در برآورد مقادیر شن منطقه بوده است. درنهایت، قابلیت فن اوری مجاورت‌سنجی، در بررسی اجزای شن منطقه به اثبات رسید. همچنین، از این مدل و نیز دامنه‌های طیفی موثر به‌دست آمده می‌توان به‌عنوان مبنایی برای بررسی مقادیر شن در مقیاس بسیار وسیع، با عملیات بیش ‌مقیاس‌ سازی توسط داده‌های ابرطیفی هوایی- ماهواره‌ ای، بهره برد. که این امر نشان‌دهندۀ اهمیت طیف‌سنجی مجاورتی در تشخیص طول‌موج‌های مفید و نیز ایجاد مدل، به‌منظور استفادۀ آن در داده‌های ماهواره‌ ای، خواهد بود.

Estimating soil sand content in Mazandaran province using proximal sensing technology

Extended AbstractIntroductionSoil is considered to be dynamic and complex both spatially and temporally and thus, many physical, chemical and biological properties should be determined before assessing its quality. To reach this purpose, a large sample must be collected for laboratory tests which is both timeconsuming and costly and requires lots of attention and precision. Compared to other components of soil, sand is closely related with the quality of soil and crop growth. Therefore, environmental modeling and digital soil mapping projects should pay special attention to this part of soil texture. However, largescale detection, mapping and monitoring of sand content using common traditional sampling and usual laboratory analytical procedures are both timeconsuming and costly due to the vast spatial variability of sand. Compared to laboratorybased and field spectroscopy, spaceborne and airborne remote sensing have a lower level of accuracy due to atmospheric effects, compositional and structural effects, lower spatial and spectral resolution, geometric distortions and spectral mixing. Hence, an appropriate technology is required to overcome these imperfections and study spatially variable factors. Lab Diffuse reflectance Spectroscopy (LDRS) which utilizes fundamental vibration, overtones and a combination of functional groups has been introduced as a promising tool for soil investigation. The present research uses proximal soil sensing technology to study sand content. Materials and Methods128 samples were collected from a soil depth of 20cm in accordance with stratified randomized sampling method and supplementary data (geology, pedology, land use, etc.). The samples were then divided into two subsets: calibration subset with 96 and validation subset with 32 samples. Afterward, definitive calibration model was developed and reviewed with two & four latent variables in accordance with R, R2, RMSE, RPD and RPIQ indices using multivariate regression analysisPLSR method, LOOCV crossvalidation technique and preprocessing algorithms such as spectral averaging (spectral reduction method), smoothing and 1st derivative (SavitzkyGolay algorithm). Results & DiscussionThe estimating model indicated that out of the seven latent variables, the first two and four variables can provide the best estimate of the volume of sand in 96 calibration samples and the 32 validation subset. Since more than 60% of the variance of sand variable and 95% of the variance of spectral variables can be concentrated in these selected factors, the predicting model was calibrated based on the first four LVs and the full LOOCV procedure. The best model was calibrated with these features: Rc=0.76, R2C=0.57, RMSEc= 9.77 and SEc of about 9.82. The correlation coefficients (R) between sand contents and the effective spectral bands were calculated and equaled UV390nm= 0.46, Vis510 to 540nm about 0.53, 680 to 690 about 0.55, NIR 950 to 970 about 0.67 and 1100nm= 0.70, SWIR 1410 nm=0.76, 1860 to 1900 about 0.76, 2180 to 2220 about 0.77 indicating that the selected spectral bands (spectral ranges) with the maximum R contents were the most effective independent predictors in the present modeling process. Furthermore, the most influential spectral domains in the modeling process were determined as follows: UV390 nm, Vis440540 nm, NIR 740990 nm, SWIR 14301890, 1930, 21902240, 23302440 nm which was in agreement with previous studies. The quality of the calibrated sand predicting model was evaluated with Hotelling, Adjusted leverage and residual variances tests. The model was validated based on 32 independent samples. General characteristics of the validation process for LV=4 were Rp= 0.82, R2p= 0.67, RMSEp= 8.83, SEp= 8.92 and bias= 0.93 and Rp= 0.83, R2p= 0.68, RMSEp= 8.68, SEp= 8.72 and bias= 1.26 for LV=2. ConclusionResults indicate that the final model was capable of predicting sand contents and thus for two factors (LV=2): RPDc= 1.51, RPIQc= 2.44, RPDp= 1.78 and RPIQp= 2.45 were obtained while for four factors (LV=4): RPDc= 1.54, RPIQc= 2.48, RPDp= 1.75 and RPIQp= 2.41 were reached. A RPIQ of more than 2 shows that the model is capable of estimating soil sand content in Mazandaran province using data collected through diffuse reflectance spectroscopy. Since a new generation of hyperspectral remote sensors with high spectral resolution is now available, results of the present study can be the starting point for more accurate mapping of sand particles in soil texture using RS platforms. However, proximal spectroscopy must be more thoroughly investigated. Determining and detecting the key wavelengths in the modeling process can enhance the upscaling operation and the new airborne/satellite hyperspectral sensors and thus result in more precise mapping of the soil texture. Finally, the VNIRDRS technology was proved to be potentially capable of estimating soil sand content in Mazandaran province. The present model and key spectral domains identified in the present study can make a basis for future studies investigating the sand content in very largescale samples using airborne/satellite hyperspectral data. This shows the importance of LDRS and its role in identifying optical wavelengths which will be used in spaceborne data (upscaling process).