مقایسه الگوریتم های بازتاب سطحی و بازتاب بالای جوّ در تصاویر چندسنجنده ای برای تهیه نقشه ی سری زمانی غلظت کلروفیل آ در تالاب دهستان تیاب

تالاب‌ها جزء اکوسیستم‌های مابین خشکی و دریایی به‌شمار می‌آیند. شناسایی و نظارت‌بر آلودگی‌های ساحلی و دریایی برای به حداقل رساندن اثرات مخرب آن‌ها برای جامعه‌ی ساحلی امری ضروری و حیاتی است. پایش کلروفیل آ که رنگدانه‌ی اصلی فیتوپلانکتون‌های آب‌های ساحلی است با استفاده از عملیات میدانی زمان‌بر و هزینه‌بر است اما فناوری نوین سنجش‌ازدور با بهره‌گیری از سنجنده‌های دارای توان تفکیک مکانی و طیفی بالا امکان پایش در مقیاس کلان را میسر ساخته است. در این پژوهش از داده‌های بازتاب سطحی و داده‌های بازتاب بالای جو ماهواره‌های سنتینل2 و لندست8 و الگوریتم oc2 به‌منظور تخمین سری زمانی غلظت کلروفیل آ در تالاب دهستان تیاب استفاده شد. هدف از پژوهش حاضر مقایسه داده‌های ورودی به الگوریتم oc2، ارزیابی آن با داده‌های میدانی و درنهایت تخمین غلظت سری زمانی کلروفیل آ در منطقه مطالعاتی است. نتایج پژوهش نشان داد که الگوریتم oc2 با داده‌های بازتاب سطحی زمین دارای همبستگی به‌مراتب بالاتری در ماهواره‌های لندست 8 و سنتینل 2 است که به‌موجب آن مقدار r2 به‌ترتیب در داده‌های بازتاب سطحی زمین ماهواره‌ی لندست8 و سنتینل2 برابر با 0/91 و 0/64 برآورد گردید. این درحالی است که مقدار r2 به‌ترتیب در داده‌های بازتاب بالای جو برابر با 0/12 و 0/54 است. نتایج پژوهش بیانگر این است که ورودی الگوریتم oc2 حتماً باید از نوع داده‌ی بازتاب سطحی زمین و تصحیح اتمسفری شده باشد.

Comparison of reflectance algorithms in multisensor images for time series mapping of ChlorophyllA Concentration in Tiab Rural Wetland

Extended AbstractIntroductionThe oceans cover about 70% of the earth’s surface and contain the most water on Earth, as well as important marine ecosystems. In generally, global waters are classified into two types of water (the first case and the second case). In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However Case2 waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter and Total suspended matter. Coastal wetlands are considered as the Case2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities.  Phytoplankton are floating or scattered singlecelled algae that travel primarily through water waves. Chlorophylla considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale. The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll A based on blue and green bands (OC2) in Landsat8 and Sentinel2 data, evaluating the results using Field data and finally the time series mapping of chlorophylla concentration.Materials & MethodsIn this study, Landsat 8, Sentinel 2 satellite time series data and field data collected from the study area were used. First, the satellite images used in ENVI 5.3.1 software were converted to Surface Reflectance and Top of Atmosphere Reflectance. Then, MATLAB 2018a software was used for image processing and coding. to estimate the chlorophyllA concentration, the biooptical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results two statistical parameters R2 and RMSE were used. Results & DiscussionBased on the analysis of field data, the concentration of chlorophyllA in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere Reflectance Sentinel 2 and Landsat 8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat 8 and Sentinel 2 satellites systems. The OC2 algorithm had amounts R2 (0.91 and 0.64) and RMSE (0.13 and 0.33) in Landsat 8 and Sentinel 2 Surface Reflectance data, respectively, while Landsat 8 and Sentinel 2 Top of Atmosphere Reflectance data had amounts R2 (0.12 and 0.53) and RMSE (0.45 and 0.51), respectively. The time series of chlorophyllA concentration estimated using surface reflectance data (Landsat 8) corresponds to the natural conditions of the region, However, the time series of chlorophylla concentrations using the surface reflectance data (Sentinel 2) during the seasons estimated the chlorophyll concentration to be uniformly and downward. The reason for this poor performance in the Sentinel 2 is the lack of sufficient field data for calibration. ConclusionIn this study, we tried to evaluate and compare the reflectance algorithms (Landsat 8 and Sentinel 2) in the OC2 algorithm. Preliminary results indicate that the type of satellite data used (surface reflectance and Top Atmosphere reflectance) is of great importance for entering the OC2 biooptical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to highatmosphere reflectance data. In general, the results showed that 10 field data is enough to calibrate with Landsat 8 data, but for Sentinel 2 data, more than 10 numbers field data must be calibrated to obtain a good result.