Pages that link to "Item:Q50669"
From geokb
The following pages link to Prasad S. Thenkabail, PhD (Q50669):
Displayed 50 items.
- Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download Analysis (Q55779) (← links)
- Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? (Q147240) (← links)
- Remote sensing of global croplands for food security: Way forward (Q148892) (← links)
- Global Map of Rainfed Cropland Areas (GMRCA) and statistics using remote sensing (Q148893) (← links)
- Irrigated areas of India derived from satellite sensors and national statistics: A way forward from GIAM experience (Q148894) (← links)
- Global irrigated area maps (GIAM) and statistics using remote sensing (Q148895) (← links)
- A history of irrigated areas of the world (Q148896) (← links)
- Context, need: The need and scope for mapping global irrigated and rain-fed areas (Q148897) (← links)
- Remote sensing of global croplands for food security (Q148898) (← links)
- Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine (Q149744) (← links)
- New generation hyperspectral sensors DESIS and PRISMA provide improved agricultural crop classifications (Q150464) (← links)
- New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification (Q150706) (← links)
- Hyperspectral Remote Sensing of Vegetation and Agricultural Crops (Q154516) (← links)
- Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm (Q155133) (← links)
- Remote sensing of land resources: Monitoring, modeling, and mapping advances over the last 50 years and a vision for the future (Q233855) (← links)
- Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation (Q234475) (← links)
- Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing (Q234663) (← links)
- Global land cover mapping: a review and uncertainty analysis (Q236769) (← links)
- Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation) (Q238041) (← links)
- A Unified Cropland Layer at 250-m for global agriculture monitoring (Q238580) (← links)
- Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data (Q238601) (← links)
- Fallow-land Algorithm based on Neighborhood and TemporalAnomalies (FANTA) to map planted versus fallowed croplands usingMODIS data to assist in drought studies leading to water and foodsecurity assessments (Q239099) (← links)
- Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data (Q239245) (← links)
- Automated cropland mapping of continental Africa using Google Earth Engine cloud computing (Q239564) (← links)
- Remote sensing sensors and applications in environmental resources mapping and modeling (Q242203) (← links)
- Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission (Q242559) (← links)
- Analysis of the effects of heavy metals on vegetation hyperspectral reflectance properties (Q245922) (← links)
- An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data (Q246103) (← links)
- Hyperspectral remote sensing tools for quantifying plant litter and invasive species in arid ecosystems (Q246198) (← links)
- Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna river basin (India) (Q250981) (← links)
- Hyperspectral remote sensing of vegetation (Q251280) (← links)
- Free access to Landsat imagery (Q251969) (← links)
- Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine (Q253467) (← links)
- Inland valley wetland cultivation and preservation for africa’s green and blue revolution using multi-sensor remote sensing (Q254029) (← links)
- Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud (Q255291) (← links)
- Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud (Q256362) (← links)
- Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA (Q258186) (← links)
- A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades (Q262107) (← links)
- Remote sensing data characterization, classification, and accuracies: advances of the last 50 years and a vision for the future (Q262688) (← links)
- Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR (Q263048) (← links)
- Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system (Q263114) (← links)
- Hyperspectral remote sensing for terrestrial applications (Q264778) (← links)
- Crop water productivity from cloud-Based landsat helps assess California’s water savings (Q265225) (← links)
- Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing (Q266592) (← links)
- Advances in hyperspectral remote sensing of vegetation and agricultural croplands (Q266725) (← links)
- Fifty-years of advances in hyperspectral remote sensing of agriculture and vegetation: Summary, insights, and highlights of volume III (Q268203) (← links)
- Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform (Q270985) (← links)
- Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security (Q271288) (← links)
- A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (Q273589) (← links)
- Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine (Q274041) (← links)
