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برآورد رطوبت خاک به کمک تلفیق ویژگی‌های فیزیکی و هیدرولیکی خاک با داده‌های نوری سنجشازدور با استفاده از روش یادگیری ماشین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 7، مهر 1401، صفحه 1575-1591 اصل مقاله (2.67 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.339616.669240 | ||
| نویسندگان | ||
| شکوفه شکری1؛ احمد فرخیان فیروزی* 2؛ ابراهیم بابائیان3 | ||
| 1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 2دانشیار، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، ایران | ||
| 3محقق پسای دکتری دانشگاه ایالتی آریزونا | ||
| چکیده | ||
| رطوبت خاک به‌عنوان متغیری پویا در مکان و زمان، یکی از عوامل اصلی اثرگذار در چرخه آب در طبیعت و تولید محصولات کشاورزی محسوب می‌شود؛ بنابراین برآورد دقیق آن برای مدیریت بهینه منابع آب در بخش کشاورزی حائز اهمیت است. داده‌های انعکاس طیفی سنجشازدور در طول‌موج مادون‌قرمز نزدیک و دور قابلیت زیادی برای برآورد رطوبت خاک دارند و از طرفی ویژگی‌های فیزیکی و هیدرولیکی خاک بر تغییرپذیری مکانی و زمانی رطوبت خاک اثرگذارند. هدف از این پژوهش توسعه و ارزیابی مدل‌های مختلف حاصل از ترکیب متغیرهای سنجشازدور و فیزیکی خاک برای برآورد رطوبت خاک در مزارع کشت‌و‌صنعت امیرکبیر خوزستان با استفاده از روش‌های مختلف یادگیری ماشین بود. بدین منظور 166 نقطه کنترل زمینی و 16 تصویر ماهواره سنتینل-2 در طول دوره رشد گیاه نیشکر در سال 1400 مورداستفاده قرار گرفت. از ترکیب ویژگی‌های فیزیکی/ هیدرولیکی و شاخص‌های سنجشازدور، هفت مدل به‌صورت سلسله مراتبی به دست آمد که با شش الگوریتم یادگیری ماشین شامل درخت تصمیم‌گیری، ماشین بردار خطی، رگرسیون خطی، درخت توسعه‌یافته، درخت کیسه گذاری و شبکه عصبی تلفیق و ارزیابی شدند. نتایج نشان داد ترکیب ویژگی‌های فیزیکی/ هیدرولیکی و شاخص‌های سنجشازدور دقت برآورد رطوبت خاک را افزایش می‌دهد. تقریباً همه مدل‌های به‌دست‌آمده با مقدار cm3 cm-3 RMSE= 0.040-0.060 و R2 حدود 80/0 برآورد قابل قبولی از مقدار رطوبت خاک ارائه دادند. متغیر STR در مقایسه با NIR به دلیل حساسیت بیشتر به مقدار آب خاک، اهمیت بالاتری در برآورد رطوبت خاک از خود نشان داد. بعلاوه، روش رگرسیون خطی گام‌به‌گام با مقدار RMSE برابر cm3 cm-3 0.042 در مقایسه با سایر مدل‌های یادگیری ماشین با دقت بالاتری رطوبت خاک را برآورد کرد. نتایج نشان داد که مدل‌های ارائه‌شده قادر به برآورد تغییرات مکانی و زمانی رطوبت خاک هستند، لذا می‌توان از آن‌ها برای برنامه‌ریزی دقیق آبیاری و مدیریت بهینه آب در مقیاس مزرعه استفاده کرد. | ||
| کلیدواژه‌ها | ||
| مدل‌سازی؛ پارامترهای هیدرولیکی خاک؛ کشت‌و‌صنعت امیرکبیر؛ نیشکر؛ سنجشازدور مادون‌قرمز | ||
| عنوان مقاله [English] | ||
| Estimating Soil Moisture from Fusion of Soil Physical/Hydraulic Properties and Optical Remote Sensing Observations Using Machine Learning | ||
| نویسندگان [English] | ||
| Shokoufeh Shokri1؛ Ahmad Farrokhian Firouzi2؛ Ebrahim Babaeian3 | ||
| 1Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 2Associate Professor, Department of soil science, Faculty of Agriculture , Shahid Chamran University of Ahvaz, Iran | ||
| 3Department of Environmental Science, University of Arizona,, Arizona, USA | ||
| چکیده [English] | ||
| Soil moisture content (SM) is a critical state variable that significantly affects both the hydrological cycle and agricultural production. Therefore, accurate estimation of soil moisture is important for agricultural water resources management. Remote sensing observations in the near- and shortwave infrared have large potential for estimating soil moisture. In addition, soil physical and hydraulic properties affect spatial and temporal variability of soil moisture. The objective of this research was to derive different models for soil moisture estimation in Amir Kabir sugarcane agro-industry fields, Kuzestan province using a combination of soil physical/hydraulic properties and remote sensing observations with machine learning algorithms. Consequently, 166 ground control points and 16 Sentinel-2 satellite images were investigated during the growth period of sugarcane in the year 2021. Six machine learning algorithms including decision tree (DT), support vector machine (SVM), Linear regression, Boosted and Bagged trees, and nural network were used for modeling. Seven models were derived from the combination of soil physical/hydrological properties and remote sensing indices in a hierarchical manner to predict soil moisture content at the field scale. The results indicated that the combination of soil physical/hydraulic properties with remote sensing indices enhances the accuracy of soil moisture estimation. It is observed that almost all developed models performed well for estimating soil moisture, with an RMSE of 0.04-0.06 cm-3cm-3 and an R2 of approximately 0.80. The STR parameter was found to be more sensitive to changes in soil water content than NIR reflectance. Therefore, STR was identified as the most important feature in estimating soil moisture content. Moreover, stepwise linear regression with RMSE value of 0.042 cm3 cm-3 performed the best in soil moisture estimation. According to the results, the models successfully capture the spatiotemporal dynamics of soil moisture and can be used for irrigation scheduling and precision irrigation management at the field scale. | ||
| کلیدواژه‌ها [English] | ||
| Modeling, Soil hydraulic parameters, Sugarcane, Infrared remote sensing | ||
| مراجع | ||
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