Anonymous

Item talk:Q140356: Difference between revisions

From geokb

Item talk:Q140356 (view source)

Revision as of 20:41, 30 August 2024

13,198 bytes added ,  2 months ago
no edit summary
(Created page with "{ "OpenAlex": { "id": "https://openalex.org/A5014096050", "orcid": "https://orcid.org/0000-0001-8060-9841", "display_name": "Pardhasaradhi Teluguntla", "display_name_alternatives": [ "Pardhasaradhi Teluguntla", "P. Teluguntla", "Pardhasaradhi Gangadhara Rao", "Pardharsadhi Teluguntla", "Pardhasaradhi G. Teluguntla" ], "works_count": 47, "cited_by_count": 1960, "summary_stats": { "2yr_mean_citedness": 5...")
 
No edit summary
 
Line 965: Line 965:
         "id": "https://openalex.org/T10766",
         "id": "https://openalex.org/T10766",
         "display_name": "Urban Heat Islands and Mitigation Strategies",
         "display_name": "Urban Heat Islands and Mitigation Strategies",
         "value": 1e-05,
         "value": "1e-05",
         "subfield": {
         "subfield": {
           "id": "https://openalex.org/subfields/2305",
           "id": "https://openalex.org/subfields/2305",
Line 1,279: Line 1,279:
     "created_date": "2023-07-21",
     "created_date": "2023-07-21",
     "_id": "https://openalex.org/A5014096050"
     "_id": "https://openalex.org/A5014096050"
  },
  "ORCID": {
    "@context": "http://schema.org",
    "@type": "Person",
    "@id": "https://orcid.org/0000-0001-8060-9841",
    "mainEntityOfPage": "https://orcid.org/0000-0001-8060-9841",
    "givenName": "Pardhasaradhi",
    "familyName": "Teluguntla",
    "affiliation": {
      "@type": "Organization",
      "@id": "grid.426886.6",
      "name": "Bay Area Environmental Research Institute"
    },
    "@reverse": {
      "creator": [
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.3390/rs15194894",
          "name": "Crop Water Productivity from Cloud-Based Landsat Helps Assess California\u2019s Water Savings",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "doi",
            "value": "10.3390/rs15194894"
          }
        },
        {
          "@type": "CreativeWork",
          "name": "Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud"
        },
        {
          "@type": "CreativeWork",
          "name": "Hyperspectral Narrowband Data Propel Gigantic Leap in the Earth Remote Sensing"
        },
        {
          "@type": "CreativeWork",
          "name": "Impact of flooded rice paddy on remotely sensed evapotranspiration in the Krishna River basin, India"
        },
        {
          "@type": "CreativeWork",
          "name": "Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud"
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1080/17538947.2019.1651912",
          "name": "A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85071647372"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1080/17538947.2019.1651912"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1080/15481603.2019.1690780",
          "name": "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",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85075351890"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1080/15481603.2019.1690780"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1016/j.isprsjprs.2018.07.017",
          "name": "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",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85051136400"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1016/j.isprsjprs.2018.07.017"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1080/15481603.2018.1482855",
          "name": "Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85048044474"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1080/15481603.2018.1482855"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1016/j.isprsjprs.2017.01.019",
          "name": "Automated cropland mapping of continental Africa using Google Earth Engine cloud computing",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1016/j.isprsjprs.2017.01.019"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85014705870"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.3390/rs9101065",
          "name": "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",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.3390/rs9101065"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85032864011"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1080/17538947.2016.1267269",
          "name": "Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000\u20132015) data",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1080/17538947.2016.1267269"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-85008422868"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1080/17538947.2016.1168489",
          "name": "Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250\u2005m time-series data",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-84965031268"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1080/17538947.2016.1168489"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "name": "Global food security support analysis data at nominal 1 km (GFSAD1km) derived from remote sensing in support of food security in the twenty-first century: Current achievements and future possibilities",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "eid",
            "value": "2-s2.0-85051140400"
          }
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1109/ictsd.2015.7095869",
          "name": "Hot spot analysis using NDVI data for impact assessment of watershed development",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-84931033860"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1109/ictsd.2015.7095869"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "name": "Hyperspectral remote sensing for terrestrial applications",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "eid",
            "value": "2-s2.0-84971639839"
          }
        },
        {
          "@type": "CreativeWork",
          "name": "Inland valley wetland cultivation and preservation for africa\u2019s green and blue revolution using multisensor remote sensing",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "eid",
            "value": "2-s2.0-85054268718"
          }
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.3390/rs70708858",
          "name": "Mapping flooded rice paddies using time series of MODIS imagery in the Krishna River Basin, India",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.3390/rs70708858"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-84937899618"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "name": "HYPERSPECTRAL REMOTE SENSING OF VEGETATION AND AGRICULTURAL CROPS"
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1007/s11269-014-0567-5",
          "name": "Relating Trends in Streamflow to Anthropogenic Influences: A Case Study of Himayat Sagar Catchment, India",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.1007/s11269-014-0567-5"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-84897573872"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.2136/vzj2012.0118",
          "name": "Multidecadal trend of basin-scale evapotranspiration estimated using AVHRR data in the Krishna River Basin, India",
          "identifier": [
            {
              "@type": "PropertyValue",
              "propertyID": "eid",
              "value": "2-s2.0-84881567310"
            },
            {
              "@type": "PropertyValue",
              "propertyID": "doi",
              "value": "10.2136/vzj2012.0118"
            }
          ]
        },
        {
          "@type": "CreativeWork",
          "name": "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)"
        },
        {
          "@type": "CreativeWork",
          "name": "Analysis of spectral measurements in paddy rice field: Implications for land use classification",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "eid",
            "value": "2-s2.0-84858853823"
          }
        },
        {
          "@type": "CreativeWork",
          "name": "Impact of spatial scale on remotely sensed evapotranspiration estimates from heterogeneous land surfaces",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "eid",
            "value": "2-s2.0-84863418522"
          }
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1016/j.agwat.2010.01.027",
          "name": "Mapping agricultural responses to water supply shocks in large irrigation systems, southern India",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "doi",
            "value": "10.1016/j.agwat.2010.01.027"
          }
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.1061/(asce)ir.1943-4774.0000225",
          "name": "Farmers\u2019 Adaptation and Regional Land-Use Changes in Irrigation Systems under Fluctuating Water Supply, South India",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "doi",
            "value": "10.1061/(asce)ir.1943-4774.0000225"
          }
        },
        {
          "@type": "CreativeWork",
          "name": "Water Scarcity Effects on Equitable Water Distribution and Land Use in a Major Irrigation Project\u2014Case Study in India"
        },
        {
          "@type": "CreativeWork",
          "name": "Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder datasets"
        },
        {
          "@type": "CreativeWork",
          "name": "Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India"
        },
        {
          "@type": "CreativeWork",
          "@id": "https://doi.org/10.3910/2009.111",
          "name": "Closing of the Krishna Basin: Irrigation, Streamflow Depletion and Macroscale Hydrology.",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "doi",
            "value": "10.3910/2009.111"
          }
        }
      ]
    },
    "identifier": {
      "@type": "PropertyValue",
      "propertyID": "Scopus Author ID",
      "value": "55135808700"
    }
   }
   }
}
}
Retrieved from "https://geokb.wikibase.cloud/wiki/Special:MobileDiff/1134795"