Search Results - Sense data.

Drought monitoring: Sudan by Tadesse, Mulugeta, Babiker, S., Schmitter, Petra, Dembélé, Moctar

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Thematic mapper vegetation indices for determining soybean and corn growth parameters by Thenkabail, Prasad S., Ward, A.D., Lyon, J., Merry, C.J.

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Estimation of sediment load for Himalayan rivers: case study of Kaligandaki in Nepal by Chinnasamy, Pennan, Sood, Aditya

“…Multi-source data from Beld observations, remote sensing platforms, surveys and government records were used to set up and run the SWAT model for the Kaligandaki basin from 2000 to 2009. …”
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EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture by Costa-Neto, Germano, Galli, Giovanni, Carvalho, Humberto Fanelli, Crossa, José, Fritsche-Neto, Roberto

“…Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). …”
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TRMM rainfall correction over the Andean Plateau using wavelet multi-resolution analysis by Heidinger, Haline, Yarleque, C., Posadas, A., Quiróz, R.

“…Quantifying rainfall from remotely sensed data is crucial for regions where meteorological stations are scarce. …”
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Statistics from space: Next-generation agriculture production information for enhanced monitoring of food security in Mozambique by Centro de Estudo de Políticas e Programas Agroalimentares (CEPPAG)

“…The Statistics from Space project (SFS) seeks to support the Government of Mozambique to produce and disseminate accurate crop production statistical data leveraging satellite remote sensing data and artificial intelligence augmented analytics. …”
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Water demand and use: Tunisia by Belhaj Fraj, Makram, Bergaoui, Karim, Gafsaoui, Y., Schmitter, Petra, Dembélé, Moctar

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Water demand and use: Kenya by Owusu, Afua, Serede, J., Adamseged, Muluken Elias, Schmitter, Petra, Dembélé, Moctar

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Drought monitoring: Pakistan by Cheema, Muhammad, Adnan, S., Zohaib, Muhammad, Schmitter, Petra, Dembélé, Moctar

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Irrigation performance: Jordan by Amdar, Nafn, Alabsi, K., Schmitter, Petra, Dembélé, Moctar

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Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield by Peralta, Nahuel Raúl, Assefa, Yared, Du, Juan, Barden, Charles J., Ciampitti, Ignacio A.

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Guidelines on national inventory of village forests by Singh, K.D.

“…The emphasis in survey design is on definition of survey area, construction of a sampling framework and use of remote sensing data to supplement the field observations. …”
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Assessing change in national forest monitoring capacities of 99 tropical countries by Romijn, E., Lantican, C.B., Herold, Martin, Lindquist, E., Ochieng, R., Wijaya, A., Murdiyarso, Daniel, Verchot, Louis V.

“…This effect is related to more free and open remote sensing data and availability of techniques to improve forest area change monitoring. …”
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A value chain analysis for timber in four East African countries : an exploratory case study by Hulusjö, Daniel

“…The study was conducted as an exploratory case study focusing on gaining broad initial insights. Data collection was done mainly through semi-structured interviews with actors engaged in activities throughout the chains. …”

Identifiering av fullskiktade bestånd med stereomatchade flygbilder och laserskanning by Jansson, Carl

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Irrigation performance: Iraq by Amdar, Nafn, Talal, G., Schmitter, Petra, Dembélé, Moctar

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Water level status of Indian reservoirs: a synoptic view from altimeter observations by Thakur, P. K., Garg, V., Kalura, P., Agrawal, B., Sharma, V., Mohapatra, M., Kalia, M., Aggarwal, S.P., Calmant, S., Ghosh, Surajit, Dhote, P. R., Sharma, R., Chauhan, P.

“…The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. …”
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Water: tools and functions to estimate actual evapotranspiración using Land Surface Energy Balance Models in R by Olmedo, Guillermo Federico, Ortega Farias, Samuel, Fuente Saiz, Daniel de la, Fonseca Luengo, David, Fuentes Peñailillo, Fernando

“…Currently, it is calculated with classical methods, which are difficult to extrapolate, or with land surface energy balance models (LSEB), such as METRIC and SEBAL, which are based on remote sensing data. This paper describes water, an open implementation of LSEB. …”
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The politics of numbers and additionality governing the national Payment for Forest Environmental Services scheme in Vietnam: A case study from Son La province by Pham Thu Thuy, Ngo, H.C., Đào Thị, L.C., Hoàng, T.L., Fisher, M.R.

“…Our study uses a sampling method that incorporates pre-matching and a before-after-control-intervention approach. Data was collected from government statistics, remote sensing analysis, focus group discussions involving 236 people, surveys with a total of 240 households, and key informant interviews with 45 people. …”
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Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change by Parkes, Ben, Higginbottom, Thomas P., Hufkens, Koen, Ceballos, Francisco, Kramer, Berber, Foster, Timothy

“…Gridded weather datasets, derived from combinations of in-situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. …”
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