برآورد محتوي رنگدانه‌هاي برگ گندم زمستانه (Triticum aestivum L.) با استفاده از تصاوير ماهواره لندست 8

Estimation of winter wheat (Triticum aestivum L.) leaf pigments content using Landsat‑8 imagery

ﻣﺤﺘﻮاي ﮐﻠﺮوﻓﯿﻞ ﺑﺮگﻫﺎ در ﮔﯿﺎﻫﺎن ﻣﻌﺮف ﭼﮕﻮﻧﮕﯽ ﺳﻼﻣﺘﯽ، وﺿﻌﯿﺖ ﻓﯿﺰﯾﻮﻟﻮژﯾﮑﯽ، ﻓﻌﺎﻟﯿﺖ ﻓﺘﻮﺳﻨﺘﺰي و ﻣﯿـﺰان ﻧﯿﺘﺮوژن آن ﻫﺎﺳﺖ. ﻧﻈﺎرت ﺑﺮ ﻣﺤﺘﻮاي رﻧﮓ داﻧﻪﻫﺎي ﮔﯿﺎﻫﺎن زراﻋﯽ ﺑﻪ ﺷﻨﺎﺳﺎﯾﯽ وﺿﻌﯿﺖ ﺗﻐﺬﯾﻪ ﮔﯿﺎه و ﺗﻨﺶﻫـﺎ ي ﻣﺤﯿﻄـ ﯽ ﻗﺒـﻞ از آﺳﯿﺐ ﺟﺪي ﺑﻪ ﻣﺤﺼﻮل و ﻋﻤﻠﮑﺮد ﮐﻤﮏ ﻣﯽﮐﻨﺪ. اﯾﻦ ﺗﺤﻘﯿﻖ ﺑﻪ ﻣﻨﻈﻮر ﺗﺨﻤﯿﻦ ﻣﺤﺘﻮاي رﻧﮓ داﻧـﻪ ﻫـﺎي ﺑـﺮگ ﮔﻨـﺪم ) Triticum .aestivum L( ﺑﻪ ﻋﻨﻮان ﯾﮑﯽ از ﻣﻬﻢﺗﺮﯾﻦ ﻣﺤﺼﻮﻻت زراﻋﯽ ﺑﻪ ﮐﻤﮏ دادهﻫﺎي ﻣﺎﻫﻮاره ﻟﻨﺪﺳﺖ 8 و ﻣﺪلﻫﺎي آﻣﺎري در ﺷﻬﺮﺳﺘﺎن ﺷﻬﺮﮐﺮد، اﺳﺘﺎن ﭼﻬﺎرﻣﺤﺎل و ﺑﺨﺘﯿﺎري در ﺳﺎل 1396 اﻧﺠﺎم ﺷﺪ. ﻣﻮاد و روشﻫﺎ: ﺑﺮاي اﯾﻦ ﻣﻨﻈﻮر ﻫﺸﺖ ﻣﺰرﻋﻪ زﯾﺮ ﮐﺸﺖ ﮔﻨﺪم زﻣﺴﺘﺎﻧﻪ ﺑﺎ ﻣﺴﺎﺣﺖ ﺑﯿﻦ 10 ﺗـﺎ 60 ﻫﮑﺘـﺎر در ﺳﺮاﺳـﺮ ﺷﻬﺮﺳـﺘﺎن ﺷﻬﺮﮐﺮد، در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪ. ﺳﭙﺲ ﻣﻮﻗﻌﯿﺖ 120 واﺣﺪ ﻧﻤﻮﻧﻪﺑﺮداري ﺑﻪ ﺻﻮرت ﺗﺼﺎدﻓﯽ در ﻣﺰارع ﻣﻮرد ﻣﻄﺎﻟﻌﻪ ﺗﻮﺳﻂ GPS ﺗﻌﯿﯿﻦ ﮔﺮدﯾﺪ. واﺣﺪﻫﺎي ﻧﻤﻮﻧﻪﺑﺮداري ﺑﻪ ﺻﻮرت ﻣﺮﺑﻊﻫﺎي 30 × 30 ﻣﺘﺮي ﻣﻄﺎﺑﻖ ﺑﺎ ﭘﯿﮑﺴﻞﻫﺎي ﻣﺎﻫﻮاره ﻟﻨﺪﺳـﺖ ﺑـﻮد. در ﻫـﺮ واﺣـﺪ 5 ﭘﻼت )0/5 × 0/5 ﻣﺘﺮ( در ﭼﻬﺎر ﮔﻮﺷﻪ و ﻣﺮﮐﺰ ﻣﺮﺑﻊ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪ. ﺟﻤﻊآوري ﻧﻤﻮﻧﻪﻫﺎ ﻫﻢ زﻣﺎن ﺑﺎ ﻋﺒﻮر ﻣﺎﻫﻮاره اﻧﺠـﺎم ﺷـﺪ. ﻧﻤﻮﻧﻪﻫﺎ ﺑﻪ آزﻣﺎﯾﺸﮕﺎه ﻣﻨﺘﻘﻞ و ﻣﺤﺘﻮاي ﮐﻠﺮوﻓﯿﻞ و ﮐﺎرﺗﻨﻮﺋﯿﺪ اﻧﺪازهﮔﯿﺮي ﺷﺪ. دادهﻫﺎي ﺗﺼﺎوﯾﺮ ﻣﺎﻫﻮاره ﻟﻨﺪﺳﺖ 8 ﻣﺮﺑﻮط ﺑﻪ زﻣـﺎن ﻧﻤﻮﻧﻪﺑﺮداري ﭘﺮدازش و ﺷﺎﺧﺺ ﻫﺎي ﮔﯿﺎﻫﯽ ﻣﺤﺎﺳﺒﻪ ﺷﺪ. ﺑﺮاي ﺑﻪ دﺳﺖ آوردن ﻣﺪلﻫﺎي ﺑﺮآورد ﻣﺤﺘـﻮاي ﮐﻠﺮوﻓﯿـﻞ و ﮐﺎروﺗﻨﻮﺋﯿـﺪ ﺑﺮگ ﮔﻨﺪم از روشﻫﺎي رﮔﺮﺳﯿﻮنﻫﺎي ﺧﻄﯽ ﺳﺎده و ﭼﻨﺪﮔﺎﻧﻪ ﮔﺎم ﺑﻪ ﮔﺎم اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. ﯾﺎﻓﺘﻪﻫﺎ: ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد ﮐﻪ در ﺑﯿﻦ ﺷﺎﺧﺺﻫﺎي اﻧﺘﺨﺎب ﺷﺪه، ﺷﺎﺧﺺ PSRI ﺑﺮاي ﺑـﺮآورد ﻣﺤﺘـﻮاي ﮐﻠﺮوﻓﯿـﻞ R2 = 0/408) a و 2-µg.cm 8/38 =RMSE(، ﮐﻠﺮوﻓﯿﻞ R2 =0/400) b و 2-RMSE=3/69 µg.cm( و ﮐﻠﺮوﻓﯿﻞ ﮐﻞ ﺑﺮگ )R2=0/500 و 2-RMSE=10/82µg.cm(، ﺷﺎﺧﺺ R2 =0/480) CRI و 2-RMSE =2/92 µg.cm( ﺑﺮاي ﺑـﺮآورد ﻣﺤﺘـﻮاي ﮐﺎروﺗﻨﻮﺋﯿـﺪ ﺑـﺮگ و ﺷـﺎﺧﺺ R2 =0/603) SIPI و 2-µg.cm 0/17 = RMSE( ﺑﺮاي ﺑﺮآورد ﻧﺴﺒﺖ ﮐﺎروﺗﻨﻮﺋﯿﺪ ﺑﻪ ﮐﻠﺮوﻓﯿﻞ a در رﮔﺮﺳﯿﻮن ﺧﻄﯽ ﺳﺎده، دﻗﺖ ﺑﻬﺘﺮي داﺷﺘﻨﺪ. ﻣﺪلﻫـﺎي ﻣﺒﺘﻨـﯽ ﺑـﺮ رﮔﺮﺳﯿﻮن ﺧﻄﯽ ﭼﻨﺪﮔﺎﻧـﻪ ﮔـﺎم ﺑـﻪ ﮔـﺎم ﻣﺤﺘـﻮاي ﮐﻠﺮوﻓﯿـﻞ a را ﺑـﺎ 0/557= R2 و 2-RMSE=7/73 µg.cm، ﮐﻠﺮوﻓﯿـﻞ b را ﺑـﺎ R2=0/471 و -RMSE = 3/69 µg.cm، ﮐﻠﺮوﻓﯿــﻞ ﮐــﻞ را ﺑــﺎ 0/611= R2 , و -RMSE=10/10 µg.cm، ﮐﺎروﺗﻨﻮﺋﯿــﺪ را ﺑــﺎ R2=0/500 و -RMSE =2/01 µg.cm و ﻧﺴﺒﺖ ﮐﺎروﺗﻨﻮﺋﯿﺪ ﺑﻪ ﮐﻠﺮوﻓﯿﻞ a را ﺑـﺎ R2=0/756, ﺑـﺮآورد RMSE=0/12 µg.cm- و ﮐﺮد. ﺷﺎﺧﺺ ﻫﺎي PSRI و CVI ﺑﺮاي ﺑﺮآورد ﻣﺤﺘﻮاي ﮐﻠﺮوﻓﯿﻞ a و ﮐـﻞ، ﺷـﺎﺧﺺ PSRI ﺑـﺮاي ﺑـﺮآورد ﻣﺤﺘـﻮاي ﮐﻠﺮوﻓﯿـﻞ b، ﺷﺎﺧﺺﻫـﺎي CRI و TCI/OSAVI ﺑـﺮاي ﺑـﺮآورد ﻣﺤﺘـﻮاي ﮐﺎروﺗﻨﻮﺋﯿـﺪ و ﺷـﺎﺧﺺ ﻫـﺎي CIgreen ،EVI ،GNDVI ،SIPI، TCARI و OSAVI ﺑﺮاي ﺑﺮآورد ﻧﺴﺒﺖ ﮐﺎروﺗﻨﻮﺋﯿﺪ ﺑﻪ ﮐﻠﺮوﻓﯿﻞ a ﺑﺮگ ﮔﻨﺪم ﻣﺆﺛﺮﺗﺮﯾﻦ ﺷـﺎﺧﺺ ﻫـﺎ در ﻣـﺪل رﮔﺮﺳـﯿﻮن ﺧﻄـﯽ ﭼﻨﺪﮔﺎﻧﻪ ﮔﺎم ﺑﻪ ﮔﺎم ﺑﻮدﻧﺪ. در ﻣﻄﺎﻟﻌﻪ ﻣﺎ ﺑﺮآورد ﻣﺤﺘﻮاي ﮐﻠﺮوﻓﯿﻞ a، ﮐﻞ، ﮐﺎروﺗﻨﻮﺋﯿﺪ و ﻧﺴﺒﺖ ﮐﺎروﺗﻨﻮﺋﯿﺪ ﺑﻪ ﮐﻠﺮوﻓﯿﻞ a ﺑـﺮگ ﺑـﺮ اﺳﺎس رﮔﺮﺳﯿﻮن ﺧﻄﯽ ﭼﻨﺪﮔﺎﻧﻪ ﮔﺎم ﺑﻪ ﮔﺎم ﻧﺴﺒﺖ ﺑﻪ ﻣﺪل رﮔﺮﺳﯿﻮن ﺧﻄﯽ ﺳﺎده ﻧﺘﺎﯾﺞ ﺑﻬﺘـﺮي را ﻧﺸـﺎن دادﻧـﺪ . ﺑـﺮآورد ﻣﺤﺘـﻮاي ﮐﻠﺮوﻓﯿﻞ b در ﻫﺮ دو روش ﯾﮑﺴﺎن ﺑﻮد. ﻧﺘﯿﺠﻪﮔﯿﺮي: اﺳﺘﻔﺎده از ﺷﺎﺧﺺﻫﺎي ﮔﯿﺎﻫﯽ ﺣﺎﺻﻞ از دادهﻫﺎي ﻟﻨﺪﺳﺖ 8 اﻣﮑﺎن ﺑﺮآورد ﻣﺤﺘﻮاي رﻧﮓ داﻧﻪﻫـﺎي ﺑـﺮگ ﮔﻨـﺪم را در ﻣﻨﻄﻘﻪ ﻣﻮرد ﻣﻄﺎﻟﻌﻪ ﺑﺎ ﻧﺘﺎﯾﺞ ﻧﺴﺒﺘﺎً ﺧﻮﺑﯽ ﻓﺮاﻫﻢ ﻣﯽﮐﻨﺪ. ﭼﻨﯿﻦ اﻃﻼﻋﺎﺗﯽ ﺑﺮاي ﮐﺸﺎورزان ﺑﻪ ﻣﻨﻈﻮر ﭘـﺎﯾﺶ اوﻟﯿـﻪ ﺿـﻌﯿﺖ ﮐﯿﻔﯿـﺖ و ﮐﻤﯿﺖ ﺗﻮﻟﯿﺪ اﻫﻤﯿﺖ داﺷﺘﻪ و ﻣﻨﺠﺮ ﺑﻪ ﯾﮏ ﺑﺮﻧﺎﻣﻪرﯾﺰي ﮐﺎرﺑﺮدي و ﮐﺎرآﻣﺪ در روﻧﺪ ﮐﻮددﻫﯽ ﻣﯽﺷﻮد.

Leaf chlorophyll content in plants indicates their health, physiological status, their photosynthetic activity and nitrogen content. Monitoring pigment content of crop tissues helps identification of plant nutrition and environmenta stresses before serious damages to crop and yield. This study was conducted to estimate the leaf pigments content of winter wheat (Triticum aestivum L.) as one of the most important crops using Landsat 8 satellite data and statistical models in Shahrekord county, Chaharmahal and Bakhtiari province in 2017. Materials and methods: For this purpose, eight fields under winter wheat cultivation with an area between 10 to 60 hectares throughout Shahrekord county were considered. The location of 120 sampling units was randomly and determined in fields using ground positioning system (GPS). The sampling units were 30 × 30 m squares according to Landsat pixels. In each unit 5 plots (0.5 × 0.5 m) were considered, four plots in the corners and one in the center of the unit. Crop sampling and passing over of satellite were synchoronous. The samples were transferred to the laboratory and their chlorophyll and carotenoid content were measured. The corresponded Landsat 8 data were processed and and vegetation indices were calculated. Simple and multiple stepwise linear regression methods were used to obtain models for estimating chlorophylls and carotenoids content of wheat leaves. Results: The results showed that among the selected indices, PSRI index for estimating chlorophyll a content (R2 = 0.408 and RMSE= 8.38 µg.cm-2), chlorophyll b (R2 = 0.400 and RMSE= 3.69 µg.cm-2) and total chlorophyll (R2 = 0.500 and RMSE= 10.82 µg.cm-2), CRI index (R2 = 0.480 and RMSE= 2.92 µg.cm-2) for estimating leaf carotenoid content and SIPI index (R2 = 0.603 and RMSE = 0.17 µg.cm-2) for estimating carotenoid to chlorophyll a ratio had the best performance in simple linear regression. Models based on multiple stepwise linear regression estimated chlorophyll a content with R2 = 0.557 and RMSE = 7.73 µg.cm-2, chlorophyll b with R2 = 0.471 and RMSE = 3.69 µg.cm-2, total chlorophyll with R2 = 0.611 and RMSE = 10.10 µg.cm-2, carotenoids with R2 = 0.50 and RMSE = 2.01 µg.cm-2 and carotenoid to chlorophyll a ratio with R2 = 0.756 and RMSE = 0.12 µg.cm-2. PSRI and CVI indices for estimating chlorophyll a and total chlorophyll content, PSRI index for estimating chlorophyll b content, CRI and TCI / OSAVI indices for estimating carotenoid content and SIPI, GNDVI, EVI, CIgreen, TCARI and OSAVI for carotenoid to chlorophyll a ratio of wheat leaf were the most effective indices in a stepwise multiple linear regression models. In our study, estimation of chlorophyll a, total, carotenoid content and carotenoid to chlorophyll a ratio based on stepwise multiple linear regression was superior to simple linear regression models. Estimation of chlorophyll b content was the same in both methods. Conclusion: The use of vegetation indices derived from Landsat 8 data makes it possible to estimate the content of wheat leaf pigments with relatively good results in the study area. Such information is important for farmers to initially monitor the quality and quantity of production and leads to a practical and efficient planning in the fertilization process.