Search Results - Sense data.

Monitoring of land use intensification and linkage to soil erosion in Nigeria and Benin by Junge, B., Abaidoo, Robert C., Chikoye, David, Alabi, T.R., Stahr, Karl

“…Land scarcity and soil degradation are some consequences that necessitate enhanced land use management by using remote sensing data. This study presents the analyses of aerial photographs and satellite images for inventory land use and its changes within time as well as for monitoring soil erosion. …”
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A multi-resolution approach to national-scale cultivated area estimation of soybean by King, LeeAnn, Adusei, Bernard, Stehman, Stephen V., Potapov, Peter V., Xiao-Peng, Song, Krylov, Alexander, Di Bella, Carlos Marcelo, Loveland, Thomas R., Johnson, David M., Hansen, Matthew C.

“…Satellite remote sensing data can provide timely, accurate, and objective information on cultivated area by crop type and, in turn, facilitate accurate estimates of crop production. …”
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Cloud-based crop type mapping for Nandi County, Kenya by Guo, Zhe

“…Some previous researchers studied remote sensing in-season crop mapping using machine learning models. …”
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Multi-scale topography assessment for site-specific drought management in Sweden by Knaebel, Karl

“…Topography was analysed at and within the field using different topographic indices i.e., slope, relative height (RE) and the topographic wetness index (TWI). The data required to conduct this study was publicly available and consisted of a highresolution digital elevation model, Sentinel-2 remote sensing data, weather data, field polygon as well as soil texture data. …”
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Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine by Srinet, R., Nandy, S., Padalia, H., Ghosh, Surajit, Watham, T., Patel, N. R., Chauhan, P.

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Climate change and shallow aquifers - unravelling local hydrogeological impacts and groundwater decline-induced subsidence by Guzy, A., Piasecki, A., Witkowski, W. T.

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Funktioner för skattning av skogsbestånds kapitalförräntning by Östman, Per

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Agrarian reform in the Brazilian Amazon: its implications for land distribution and deforestation by Pacheco, P.

“…The study is based on informant interviews, secondary information, agricultural census data, and remote sensing data.…”
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Irrigation: Remote control by Zwart, Sander J., Leclert, Lucie M.C., Bastiaanssen, Wim G.M.

“…Managers of the Office du Niger irrigation scheme in Mali are using remote sensing data to analyse the efficiency of the system without having to physically check the infrastructure. …”
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Spectroscopy and swelling soils: An integrated approach by Kariuki, P.C.

“…In this thesis new methods are explored where the various aspects of swelling soil properties are investigated consisting of engineering, geophysical, reflectance spectroscopy and remote sensing where data from two study areas one in central Kenya and the other insouthern Spain were used. …”
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What satellites see vs what pastoralists know: A mapping story from the Karamoja Cluster by Basel, Ashleigh, Chepngetich, Brenda, Tsoka, Jonathan, Kenduiywo, Benson, Craparo, Alessandro

“…This carousel tells the story of how mapping for climate resilience and peace works best when satellite data is combined with local knowledge. Focusing on the Karamoja Cluster across Uganda, Kenya, South Sudan, and Ethiopia, it shows why understanding mobility, drought, and resource tension requires seeing beyond what remote sensing alone can capture. …”
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Assessing across-scale optical diversity and productivity relationships in grasslands of the Italian Alps by Sakowska, K., MacArthur, A., Gianelle, D., Dalponte, M., Alberti, G., Gioli, B., Miglietta, F., Pitacco, A., Meggio, F., Fava, Francesco P., Julitta, T., Rossini, M., Rocchini, D., Vescovo, L.

“…In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. …”
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Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world by Defourny, Pierre, Bontemps, Sophie, Bellemans, Nicolas, Cara, Cosmin, Dedieu, Gérard, Guzzonato, Eric, Hagolle, Olivier, Inglada, Jordi, Nicola, Laurentiu, Rabaute, Thierry, Savinaud, Mickael, Udroiu, Cosmin, Valero, Silvia, Bégué, Agnès, Dejoux, Jean-François, El Harti, Abderrazak, Ezzahar, Jamal, Kussul, Nataliia, Labbassi, Kamal, Lebourgeois, Valentine, Miao, Zhang, Newby, Terrence, Nyamugama, Adolph, Salh, Norakhan, Shelestov, Andrii, Simonneaux, Vincent, Sibiry Traoré, Pierre C., Traoré, Souleymane S, Koet, Benjamin

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CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting (Preprint) by Paudel, D., Kallenberg, M., Ofori-Ampofo, S., Baja, H., van Bree, R., Potze, A., Poudel, P., Saleh, A., Anderson, W., von Bloh, M., Castellano, A., Ennaji, O., Hamed, R., Laudien, R., Lee, D., Luna, I., Meroni, M., Mutuku, J.M., Mkuhlani, S., Richetti, J., Ruane, A.C., Sahajpal, R., Shai, G., Sitokonstantinou, V., de Souza Noia Junior, R., Srivastava, A., Strong, R., Sweet, L., Vojnovic, P., Athanasiadis, I.N.

“…It features publicly available sub-national yield statistics and relevant predictors—such as weather data, soil characteristics, and remote sensing indicators—that have been pre-processed, standardized, and harmonized across spatio-temporal scales. …”
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Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite derived products with DSSAT crop simulation model by Pazhanivelan, Sellaperumal, Geethalakshmi, Vellingiri, Tamilmounika, R., Sudarmanian, N.S., Kaliaperumal, Ragunath, Ramalingam, Kumaraperumal, Sivamurugan, A.P., Mrunalini, Kancheti, Yadav, Manoj Kumar, Quicho, Emma D.

“…The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. …”
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Nature Benefits in Kenya: an Atlas of Ecosystem and Human Well-Being by Said, Mohammed Yahya, Okwi, P., Ndeng'e, G., Agatsiva, J., Kilele, X.

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Sentinel-1 SAR Time Series-Based Assessment of the Impact of Severe Salinity Intrusion Events on Spatiotemporal Changes in Distribution of Rice Planting Areas in Coastal Provinces... by Hoang-Phi, Phung, Lam-Dao, Nguyen, Pham-Van, Cu, Chau-Nguyen-Xuan, Quang, Nguyen-Van-Anh, Vu, Gummadi, Sridhar, Le-Van, Trung

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Spatial-temporal coupling of malaria vector habitat suitability and biting probability by Aduvukha, G.R., Abdel-Rahman, E.M., Mutanga, O., Odindi, J., Tonnang, H.E.Z.

“…Specifically, relevant remotely sensed data, climatic and topographic variables, and presenceonly malaria vector data were integrated into MaxEnt (maximum entropy), an SDM to assess the distribution of malaria vectors. …”
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Earth observation-based assessment of the water production and water consumption of Nile Basin agro-ecosystems by Bastiaanssen, Wim G.M., Karimi, P., Rebelo, Lisa-Maria, Duan, Z., Senay, Gabriel B., Muttuwatte, L., Smakhtin, Vladimir U.

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A scenario modelling approach to assess management impacts on soil erosion in coffee systems in Central America by Ospina, Alejandra

“…Soil erosion was modelled using the RUSLE (Revised Universal Soil Loss Equation), integrating soil and vegetation cover field data, with remote sensing data. Management scenarios were developed to assess the role of two principal coffee management strategies in mitigating soil erosion: increasing vegetation cover, and soil conservation practices. …”
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