162

  • Internet havola
  • DOI
  • UzSCI tizimida yaratilgan sana 30-09-2020
  • O'qishlar soni 153
  • Nashr sanasi 02-06-2020
  • Asosiy tilO'zbek
  • Sahifalar98-99
Kalit so'z
English

The article deals with the organization of monitoring of agricultural crops using modern digital technologies and remote sensing data by studying the experience of China, the European Union, the United States and other developed countries, and developed recommendations for conducting monitoring of agricultural crops in Uzbekistan using remote sensing. 
 

Kalit so'z
Русский

В статье рассмотрены вопросы организации мониторинга сельскохозяйственных культур с использованием современных цифровых технологий и материалов дистанционного зондирования путем изучения опыта Китая, Европейского союза, США и других развитых стран, разработаны рекомендации по проведению в Узбекистане мониторинг сельскохозяйственных культур с помощью дистанционного зондирования.

Kalit so'z
№ Muallifning F.I.Sh. Lavozimi Tashkilot nomi
1 Avezbayev S.. и.ф.д.,профессор ТИҚХММИ
2 Avezbayev O.. бош мутахассис Ўзбекистон Республикаси Қишлоқ хўжалиги вазирлиги
№ Havola nomi
1 1. Wu, Bingfang & Meng, Ji-hua & Li, Qiangzi & Yan, Nana & Du, Xin & Zhang, Miao. (2014). Remote sensing-based global crop monitoring: Experiences with China’s CropWatch system. International Journal of Digital Earth. 7. 10.1080/17538947.2013.821185.
2 2. Wu, B. F. 2000. ‘‘Operational Remote Sensing Methods for Agricultural Statistics.’’ Acta Geographica Sinica 55 (1): 2335.
3 3. Li, Congcong & Li, Hongjun & Li, Jiazhen & Lei, Yuping & li, Chunqiang & Manevski, Kiril & Shen, Yan-jun. (2019). Using NDVI percentiles to monitor real-time crop growth. Computers and Electronics in Agriculture. 162. 357-363. 10.1016/j.compag.2019.04.026.
4 4. Wolanin, Aleksandra & Camps-Valls, Gustau & Gómez-Chova, Luis & Mateo-Garcia, Gonzalo & Tol, C. & Zhang, Yongguang & Guanter, Luis. (2019). Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations. Remote Sensing of Environment. 225. 441-457.
5 5. Robinson, Nathaniel & Id, Brady & Allred, & Jones, Matthew & Moreno, Alvaro & Kimball, J. & Naugle, David & Erickson, Tyler & Richardson, Andrew & Thenkabail, Prasad & Kumar, Lalit & Mutanga, Onisimo. (2017). A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sensing. 10.3390/rs9080863.
6 6. Seo, Bumsuk & Lee, Jihye & Lee, Kyung-Do & Hong, Sukyoung & Kang, Sinkyu. (2019). Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA. Field Crops Research. 238. 113-128.
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