Resultados de búsqueda - Sense data.

Assessing the risk of root rots in common beans in east Africa using simulated, estimated and observed daily rainfall data por Farrow, Andrew, Musoni, D, Cook, Simon E., Buruchara, Robin Arani

“…This paper seeks to establish the concept that the analysis of high temporal resolution meteorological data adds value to the investigation of the effect of climatic variability on the prevalence and severity of agricultural pests and diseases. …”
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Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen por Lapaz Olveira, Adrián, Castro Franco, Mauricio, Sainz Rozas, Hernan Rene, Carciochi, Walter, Balzarini, Mónica, Avila, Oscar, Ciampitti, Ignacio, Reussi Calvo, Nahuel Ignacio

“…Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.…”
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Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India por Biggs, Trent W., Thenkabail, Prasad S., Gumma, Murali K., Scott, Christopher A., Parthasaradhi, G.R., Turral, Hugh

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Improving agricultural field parcel delineation with a dual branch spatiotemporal fusion network by integrating multimodal satellite data por Cai, Zhiwen, Hu, Qiong, Zhang, Xinyu, Yang, Jingya, Wei, Haodong, Wang, Jiayue, Zeng, Yelu, Yin, Gaofei, Li, Wenjuan, You, Liangzhi, Xu, Baodong, Shi, Zhihua

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Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production por Chaali, Nesrine, Ramírez Gomez, Carlos Manuel, Jaramillo Barrios, Camilo Ignacio, Garr´e, Sarah, Barrero, Oscar, Ouazaa, Sofiane, Calderon Carvajal, John Edinson

“…However, relying solely on one sensing data type may not fully capture the intricate relationship between soil properties and crop physiological processes. …”
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Sustainable intensification of dairy production can reduce forest disturbance in Kenyan montane forests por Brandt, P., Hamunyela, E., Herold, M., Bruin, S. de, Verbesselt, Jan, Rufino, Mariana C.

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A decadal historical satellite data and rainfall trend analysis (2001–2016) for flood hazard mapping in Sri Lanka por Alahacoon, Niranga, Matheswaran, Karthikeyan, Pani, Peejush, Amarnath, Giriraj

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Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data. por Amarnath, Giriraj, Ameer, Mohamed, Aggarwal, Pramod K., Smakhtin, Vladimir U.

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Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data por Amarnath, Giriraj, Ameer, Mohamed, Aggarwal, Pramod K., Smakhtin, Vladimir U.

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Seasonality and post fire recovery in a wetland dominated region: Insights from satellite data analysis in northern Argentina por Saucedo, Griselda Isabel, Kurtz, Ditmar Bernardo

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Estimation of Biomass Carbon Stocks over Peat Swamp Forests using Multi-Temporal and Multi-Polratizations SAR Data por Wijaya, A., Liesenberg, V., Susanti, A., Karyanto, O., Verchot, Louis V.

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Protocol for the field experiment for variety calibration as an input in the yield estimation por Mathieu, Renaud, Quicho, Emma, Satapathy, Sushree

“…Work package 4 maps rice areas using remote sensing, geographic information system (GIS), and crop modeling. …”
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Ground reference dataset for crop type mapping and monitoring in four districts of Rwanda por Kenduiywo, Benson Kipkemboi, Wahome, Anastasia Mumbi, Sande, Stephen Ngondi, Chemutt, Joseph Kipkoech

“…A critical prerequisite for remote sensing–based crop mapping, however, is the availability of high-quality labelled ground reference data to train and validate Artificial Intelligence (AI) classification models. …”
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Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia por Masolele, Robert N., Sy, Veronique de, Marcos, Diego, Verbesselt, Jan, Gieseke, Fabian, Mulatu, Kalkidan Ayele, Moges, Yitebitu, Sebrala, Heiru, Martius, Christopher, Herold, Martin

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Soil-Landscape Estimation and Evaluation Program (SLEEP) to predict spatial distribution of soil attributes for environmental modeling. por Ziadat, F.M., Dhanesh, Y., Shoemate, D., Srinivasan, R., Narasimhan, B., Tech, J.

“…This demonstrates the potential of SLEEP to support SWAT modeling where soil data is scarce. Keywords: GIS, remote sensing, terrain analyses, watershed, SWAT, inverse distance weighted, Kriging DOI: 10.3965/j.ijabe.20150803.1270 Online first on [2015-03-17]…”
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Assessing the accuracy of satellite radar altimetry for inland water level monitoring por Camici, S., Calmettes, B., Daguzé, J.-A., Piras, F., Thibaut, P., Amarnath, Giriraj, Femenias, P., Tarpanelli, A.

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Mapping above ground carbon storage and sequestration using the InVEST model: A case study of three countries in Kenya por Guo, Zhe

“…This study presents a cost-effective approach to evaluating carbon storage and sequestration in Kenya, utilizing crop type maps derived from remote sensing data and the InVEST (Integrated Valuation of Environmental Services and Tradeoffs) carbon model. …”
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Mapping of soil organic carbon stocks for spatially explicit assessments of climate change mitigation potential por Vågen, Tor-Gunnar, Winowiecki, Leigh Ann

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Paddy rice methane emissions across Monsoon Asia por Ouyang, Zutao, Jackson, Robert B., McNicol, Gavin, Fluet-Chouinard, Etienne, Runkle, Benjamin R.K., Papale, Dario, Knox, Sara H., Cooley, Sarah, Delwiche, Kyle B., Feron, Sarah, Irvin, Jeremy Andrew, Malhotra, Avni, Muddasir, Muhammad, Sabbatini, Simone, Alberto, Ma. Carmelita R., Cescatti, Alessandro, Chen, Chi-Ling, Dong, Jinwei, Fong, Bryant N., Guo, Haiqiang, Hao, Lu, Iwata, Hiroki, Jia, Qingyu, Ju, Weimin, Kang, Minseok, Li, Hong, Kim, Joon, Reba, Michele L., Nayak, Amaresh Kumar, Roberti, Debora Regina, Ryu, Youngryel, Swain, Chinmaya Kumar, Tsuang, Benjei, Xiao, Xiangming, Yuan, Wenping, Zhang, Geli, Zhang, Yongguang

“…Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. …”
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An integrated pan-tropical biomass map using multiple reference datasets por Avitabile, Valerio, Herold, Martin, Heuvelink, G. B. M., Lewis, S.L., Phillips, Oliver L., Asner, G.P., Armston, J., Asthon, P., Banin, Lindsay F., Bayol, N., Berry, N.J., Boeckx, P., Jong, B.H.J. de, Vries, B. de, Girardin, C., Kearsley, E., Lindsell, J., López Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E.T.A., Nagy, L., Qie, L., Quinones, M., Ryan, C.M., Slik, F., Sunderland, Terry C.H., Vaglio Laurin, G., Valentini, R., Verbeeck, H., Wijaya, A., Willcock, S.

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