عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TLpq2458933845
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
Synergistic Use of Microwave and Optical Satellite Data for Monsoon Cropland Mapping in India
نام عام مواد
[Thesis]
نام نخستين پديدآور
Khan, Mohammad Abdul Qadir
نام ساير پديدآوران
Mondal, Pinki
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
University of Delaware
تاریخ نشرو بخش و غیره
2020
مشخصات ظاهری
نام خاص و کميت اثر
110
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
University of Delaware
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this work, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. I adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) (also called as radar) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using Machine Learning algorithms of Random Forest (RF) and Support Vector Machine (SVM) within the Google Earth Engine platform. I developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five deferent AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the radar-only approach is 90% and 80 % whereas that of the combined approach is 93% and 90% using RF and SVM respectively It is also observed that overall RF outperformed SVM, however SVM showed improved performance when optical datasets are combined with radar data Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Environmental studies
موضوع مستند نشده
Geographic information science
موضوع مستند نشده
Remote sensing
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Khan, Mohammad Abdul Qadir
مستند نام اشخاص تاييد نشده
Mondal, Pinki
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
