پایش خشکی کشاورزی در مقیاس مزرعه مبتنی بر تصاویر دورسنجی مایکروویو رطوبت خاک

وقوع خشکسالی در کشاورزی صرفا با اندازه‌گیری تغییرات بارش قابل رصد نیست بلکه متغیرهای دیگری همچون رطوبت خاک نیز در آن نقش دارند. در میان روش‌های مختلف دورسنجی، طیف الکترومغناطیس مایکروویو محدودیت‏های فیزیکی سایر امواج رادیومتری در اندازه‏گیری رطوبت خاک را ندارد. با این تفاوت که داده‏های مایکروویو رطوبت خاک غالبا دارای ابعاد پیکسل بسیار بزرگ (بیش از 10 کیلومتر) هستند و این موضوع کاربرد آنها در مقیاس‏های کوچک را با مشکل مواجه می‏سازد. در این پژوهش به منظور محاسبه شاخص خشکی کشاورزی در مقیاس مزرعه، ابتدا با استفاده از داده‏های اندازه‏گیری میدانی رطوبت در محدوده دشت نیشابور طی سال‏های 1396 تا 1398، واسنجی داده‏های بازیابی رطوبت خاک سنجنده amsr2 انجام شد. سپس با کمک تصاویر سنجنده مودیس روابط خطی ریزمقیاس نمایی تصاویر رطوبت خاک استخراج شده و ابعاد تصویر از 25 کیلومتر به 1000 متر کاهش یافت. در گام بعدی از شاخص خشکی کشاورزی smadi که تلفیقی از خصوصیات پوشش گیاهی، رطوبت خاک و دمای سطح زمین است برای پایش خشکی کشاورزی در مقیاس مزرعه استفاده شد. به منظور ارزیابی نتایج، شاخص‏های آماری ضریب تعیین ( r^2)، میانگین قدرمطلق خطا (mae) و ریشه میانگین مربعات خطا (rmse) در سه کاربری اراضی منتخب شامل زراعت دیم (r^1)، مرتع متوسط (r^2) و مرتع فقیر (r^3) بررسی شد. شاخص های mae و rmse در بازه 1.6 تا 4 و شاخص r^2 در بازه 0.73 تا 0.84 قرار گرفت. نتایج نشان داد که الگوریتم استفاده شده در ریزمقیاس نمایی و همچنین برآورد شاخص خشکی کشاورزی smadi به خوبی قادر به بازتاب اندرکنش‏های بین بارش، رطوبت خاک، پوشش گیاهی و تغییرات پروفیل دمایی کانوپی است و این ویژگی کاربرد آن را در تحلیل‏های هواشناسی کشاورزی توجیه و تقویت می‏کند.

Field-Scale Agricultural Drought Monitoring Based on Microwave Imagery of Soil Moisture

Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisturebased algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.Materials and Methods In this study, in order to calculate the agricultural drought index at the fieldscale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the fieldscale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.Results and DiscussionBy visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to insitu soil moisture changes. At mot 80% of the changes in SMADIR1 index can be explained by insitu SMR1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.Conclusion Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.