مقایسه توانایی تشخیص تنش آبی با استفاده از ماهواره‌های سنتینل 2 و لندست 9/8 در مزارع نیشکر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری آبیاری و زهکشی، دانشکده مهندسی آب و محیط‎زیست، دانشگاه شهید چمران اهواز

2 گروه مهندسی آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهیدچمران اهواز، خوزستان، ایران

3 مؤسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی ، کرج، ایران

4 دانشکده مهندسی آب و محیط‎زیست، دانشگاه شهید چمران اهواز، اهواز، ایران

5 دانشکده مهندسی آب و محیط‎زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

چکیده

تنش آبی گیاه از مهمترین عوامل تأثیرگذار بر مدیریت آب و نظارت بر وضعیت دسترسی گیاه به آب بوده، که در صورت تشخیص دقیق در کوتاه‎مدت سبب بهبود عملکرد و جلوگیری از هدررفت منابع آب می‎گردد. هدف از پژوهش حاضر، مقایسه قابلیت باندهای فروسرخ ماهواره سنتینل2 و باندهای حرارتی ماهواره لندست 8-9 در تعیین تنش آبی گیاه در کشت و صنعت نیشکر امیرکبیر، واقع در استان خوزستان بود. به‎منظور ارزیابی برآوردها از داده‎های واقعی محاسبه شده تنش براساس حسگرهای دما و رطوبت نسبی نصب شده در نقاط مختلف مزارع و معادله تجربی ایدسو استفاده گردید. برای برآورد تنش آبی براساس باندهای حرارتی لندست8-9 از شاخص دمای سطح زمین (LST) و براساس باندهای فروسرخ سنتینل2 از شاخص تنش رطوبتی (MSI) استفاده گردید. طبق یافته‎ها، باندهای حرارتی ماهواره لندست8-9، به طور میانگین با R2 معادل 92/0-78/0، RMSE  معادل 11/0-08/0، rMBE معادل 14/20-54/14 و r معادل با 96/0-88/0 نسبت به باندهای فروسرخ سنتینل2 به‎طور میانگین با R2 معادل 89/0-74/0، RMSE معادل 15/0-14/0، rMBE  معادل 62/38-53/28 و r معادل با 94/0-86/0 اندکی برآورد بهتری در مقایسه با داده‎های واقعی تنش نشان داد. اما روند تغییرات تنش در هر دو ماهواره تفاوت معنی داری را نشان نمی‌دهد. همچنین، براساس نقشه پراکنش مکانی تنش آبی برآورد شده با استفاده از باندهای حرارتی و فروسرخ به‎ترتیب، بیشترین میزان تنش معادل با 65/0 و 69/0 و مربوط به تاریخ 2 مرداد حاصل گردید. لذا، هر دو ماهواره در برآورد تنش آبی نیشکر عملکرد قابل قبولی داشته و در صورت عدم دسترسی به تصاویر لندست 8-9، استفاده از تصاویر سنتینل2 نیز در برآورد تنش آبی گیاه پیشنهاد می‎گردد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Comparison Sentinel-2 with Landsat 8/9 to Detect Water Stress in Sugarcane Fields

نویسندگان [English]

  • Elahe Zoratipour 1
  • Amir Soltani Mohammadi 2
  • Shadman Veysi 3
  • Saeed Boroomand Nasab 4
  • Abd Ali Naseri 5
1 Ph. D Student of Irrigation and Drainage Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz,
2 Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Khuzestan , Iran.
3 Soil and Water Research Institute (SWRI), Alborz, Iran
4 Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
5 Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
چکیده [English]

Water stress is one of the most important factors affecting the evaluation of water content and the monitoring of plants. If water stress is properly recognised, yield can be improved and waste of water resources prevented in the short term. The aim of this study is to compare the capability of the infrared bands of the Sentinel 2 satellite and the thermal bands of the Landsat 8-9 satellite to determine water stress in the Amir Kabir Sugarcane Agro-Industry Unit in Khuzestan province. Actual calculated stress data based on the empirical Idso equation were used for the evaluation. The land surface temperature index (LST) based on Landsat 8-9 thermal bands and the moisture stress index (MSI) based on Sentinel 2 infrared bands were used to estimate water stress. The results show that the Landsat 8-9 satellite thermal bands with an average, R2 of 0.78-0.92, RMSE of 0.08-0.11, rMBE of 14.54-14.20, and r of 0.88-0.96, showed a slightly better estimate of the actual stress data than the infrared bands of Sentinel 2 with an average R2 of 0.74-0.89, RMSE of 0.14-0.15, rMBE of 28.5-38.6, and r of 0.86-0.94. However, the trend of stress changes for the two satellites is similar to the actual values. Based on the spatial distribution map, the estimated water stress was determined using thermal and infrared bands with the highest stress of 0.65 and 0.69, on July 24, respectively. respectively. Therefore, both satellites performed acceptably to estimate the water stress of sugarcane, and when Landsat 8-9 imagery is unavailable, the use of Sentinel-2 imagery is recommended for crop water stress estimation.

کلیدواژه‌ها [English]

  • Precision instrumentation
  • remote sensing
  • moisture stress index
  • surface temperature index
  • crop monitoring

EXTENDED ABSTRACT

Introduction

Increasing the water productivity of crops to meet growing demand and global food production is one of the greatest challenges facing global agriculture (Das et al., 2024).  Accurate and timely determination of water stress in agricultural systems can help optimize crop water productivity (King et al.,2020). Water stress is one of the most critical abiotic stressors limiting plant growth, crop yield and quality of food production (Gerhards et al., 2016; Hsiao et al., 1976). The Idso method was developed in the past to normalize and quantitatively assess leaf temperature. Remote sensing (RS) is a powerful and reliable tool that has facilitated the study of canopy water status (Solgi et al., 2023). The main remote sensing techniques for detecting crop stress (water stress and other types of stress) are infrared thermal imaging infrared and short-wave infrared reflectance.

Due to the climatic conditions of the study area, which faces water scarcity, accurate estimation of crop water stress is essential to improve irrigation management. So, the aim of this study was to use the thermal bands from the Landsat 8-9 satellites and the infrared bands from the Sentinel 2 satellite for CWSI prediction and to compare them with the CWSI data calculated from sensor data and the Idso method.

Material and method

Meteorological data were collected from the meteorological station of Amir Kabir Agro Industry, including air temperature (Ta), dew point temperature (Tdew) and relative humidity (RH) during the water critical seasons (July, August, September). Four fields were selected for daily measurements. The measurements, including Tc, Ta and RH, were taken automatically and simultaneously on the days and at the times of the satellite overflight at 10:30 am. In this study, four cloudless Landsat 8-9 satellite images and four sentinel2 were used on four day, Simultaneously. In order to evaluate, actual stress calculated were used, (empirical Idso methed). To estimate the water stress, the land surface temperature index (LST) based on the Landsat 8-9 thermal bands and the moisture stress index (MSI) based on the Sentinel-2 infrared bands were used. Subsequently, the dimensionless moisture stress index (MSI) was determined. Also, in order to focus on agricultural lands and water stress changes at fields, outliers and areas without vegetation should be removed from the images. To assess the accuracy of the estimated CWSI from thermal bands of Landsat 8-9 and infrared bands Sentinel2 with CWSI calculated from the Idso method statistical metrics were used.

Result and Discussion

The results of predicted CWSI of Landsat 8-9 thermal bands compared to actual (calculated) CWSI values on July 7 were R2 of 0.89, RMSE of 0.09, rMBE of 19.12, and r of 0.94. Also, the results of evaluating the Sentinel 2 infrared bands were R2 of 0.74, RMSE of 0.15, rMBE of 1.34, and r of 0.86. Also, on July 24, the results for the thermal and infrared bands were obtained with R2 equal to 0.92 and 0.89, RMSE equal to 0.08 and 0.14, rMBE equal to 14.20 and 6.38, and r equal to 0.96 and 0.94 for the thermal and infrared bands, respectively. In the middle of the studied interval, on August 18, the following results were obtained with increasing temperatures: R2 of 0.82 and 0.84, RMSE of 0.11 and 0.15, rMBE of 19.5 and 28.5 for the thermal and infrared bands, respectively. Also, at the end of the interval, on September 2, results were obtained with R2 equal to 0.78, RMSE equal to 0.08, rMBE equal to 14.54, and r equal to 0.88 for the thermal bands of the Landsat 8-9 satellite, and R2 equal to 0.77, RMSE equal to 0.14, rMBE equal to 30.8, and r equal to 0.88 for the infrared bands of Sentinel2. Based on the spatial distribution map, the estimated water stress was obtained using the thermal and infrared bands with the highest stress of 0.65 and 0.69 on July 24, respectively. Therefore, both satellites performed acceptably in estimating the water stress of sugarcane, and in the unavailability of Landsat 8-9 images, the use of Sentinel 2 images is recommended for estimating crop water stress.

Author Contributions

The authors contributed to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

Data is available on reasonable request from the authors.

Acknowledgements

The authors would like to grateful the Research Council of Shahid Chamran University of Ahvaz for financial support (GN: SCU.WI1401.273). Also, thanks to the CEO and staff of Amir Kabir Sugarcane Agro-Industry who cooperated in the preparation of this research.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

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تحقیقات آب و خاک ایران
دوره 56، شماره 5
مرداد 1404
صفحه 1219-1238

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