Remote sensing applications

All Items

• Investigation of changes in vegetation cover over Mosul and Wasit provinces of Iraq

  ZAHRAA A. AL-RAMAHY, ALAA M. AL-LAMI, DOAA G. MERIE, KHAWLA N. ZEKI

  242-244

• Land Use Land Cover Changes and its Association with Land Surface Temperature over Palakkad District, Kerala

  SRIYANSU NAYAK, B. AJITHKUMAR, P. LINCY DAVIS, SARATH RADHAKRISHNAN

  103-105

• Machine learning approaches for clear-sky Land Surface Albedo (LSA) retrieval using OCM-3 data over diverse Indian landscapes

  ALIYA M. KURESHI, VISHAL N. PATHAK, DISHA B. KARDANI, JALPESH A. DAVE, DHIRAJ B. SHAH, TEJAS P. TURAKHIA, ASHWIN GUJRATI, MEHUL R. PANDYA, HIMANSHU J. TRIVEDI

  454-463

• Prototype Framework for Agricultural Drought Monitoring in Northern Thailand Using Satellite-Based Evaporative Stress Index

  NOPNAPA BOONPIN, PHATCHAREEYA WAIPHARA, CHUPHAN CHOMPUCHAN

  80-87

• Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing

  ABHISHEK DANODIA, N.R. PATEL, SURESH KUMAR, R.P. SINGH, ANURAG SATPATHI, PRAKASH CHAUHAN, A. S. NAIN

  271-278

• Relationship between paddy cultivation and methane emission rate (XCH4) in Haryana

  KARAMVEER NINANIYA, CHANDER SHEKHAR, DINESH CHAHAL, NARENDRA SINGH BISHNOI

  491-493

• Remote sensing based yield estimation of wheat crop at farm scale: A case study of Badsu village of Alwar district, Rajasthan

  SUDESH SINGH CHOUDHARY, MAHESH KUMAR JAT

  407-414

• Sugarcane acreage estimation using satellite imagery and machine learning

  MEGHARANI B. MAYANI , RAJESHWARI L. ITAGI

  505-508

• Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

  ABISHEK MURUGESAN, RUCHA DAVE, AMIT KUSHWAHA, DHARMENDRA KUMAR PANDEY, KOUSHIK SAHA

  517-524

• Thermal sensing of crop: An insight on water stress

  SHWETA POKHARIYAL, N. R. PATEL, R. P. SINGH

  239-241

• Validation of a Simple MODIS Land Surface Temperature-Based Model for Potential Evapotranspiration (PET) using Long-Term Global Dataset

  MOHAMMED EL-SHIRBENY

  71-79