Your search found 10 records
1 D'Urso, G.; Querner, E. P.; Morabito, J. A. 1992. Integration of hydrological simulation models with remotely sensed data: An application to irrigation management. In Feyen, J.; Mwendera, E.; Badji, M. (Eds.), Advances in planning, design and management of irrigation systems as related to sustainable land use: Proceedings of an International Conference organized by the Center for Irrigation Engineering of the Katholieke Universiteit Leuven in cooperation with the European Committee for Water Resources Management, Leuven, Belgium, 14-17 September 1992. Vol. 2. Leuven, Belgium: Center for Irrigation Engineering. pp.463-472.
(Location: IWMI-HQ Call no: 631.7.1 G000 FEY Record No: H014427)
2 Gieske, A.; Abeyou, W. W.; Getachew, H. A.; Alemseged, T. H.; Rientjes, T. 2008. Non-linear parameterization of Lake Tana's flow system. In Abtew, W.; Melesse, A. M. (Eds.). Proceedings of the Workshop on Hydrology and Ecology of the Nile River Basin under Extreme Conditions, Addis Ababa, Ethiopia, 16-19 June 2008. Sandy, UT, USA: Aardvark Global Publishing. pp.128-145.
(Location: IWMI HQ Call no: 551.48 G136 ABT Record No: H044319)
(1.11 MB)
3 Asian Institute of Technology (AIT). 2004. Near real time agriculture activity monitoring using multi-temporal Modis earth observation satellite data. Final report. Unpublished final report submitted to the committee, Royal Thai Government Joint Research Project. 68p.
(Location: IWMI HQ Call no: P 8095 Record No: H044511)
(0.26 MB)
4 Chemin, Yann. 2012. A distributed benchmarking framework for actual ET models. In Irmak, A. (Ed.). Evapotranspiration - remote sensing and modeling. Rijeka, Croatia: InTech. pp.421-436.
(Location: IWMI HQ Call no: e-copy only Record No: H044675)
(0.29 MB) (300.42KB)
With the various types of actual ET models being developed in the last 20 years, it becomes necessary to inter-compare methods. Most of already published ETa models comparisons address few number of models, and small to medium areas (Chemin et al., 2010; Gao & Long, 2008; García et al., 2007; Suleiman et al., 2008; Timmermans et al., 2007). With the large amount of remote sensing data covering the Earth, and the daily information available for the past ten years (i.e. Aqua/Terra-MODIS) for each pixel location, it becomes paramount to have a more complete comparison, in space and time. To address this new experimental requirement, a distributed computing framework was designed, and created. The design architecture was built from original satellite datasets to various levels of processing until reaching the requirement of various ETa models input dataset. Each input product is computed once and reused in all ETa models requiring such input. This permits standardization of inputs as much as possible to zero-in variations of models to the models internals/specificities.
5 Chemin, Yann. (Ed.) 2012. Remote sensing of planet earth. Rijeka, Croatia: InTech. 240p.
(Location: IWMI HQ Call no: IWMI Record No: H044692)
(28.13 MB) (28.13MB)
(Location: IWMI HQ Call no: 363.34 G000 CEN Record No: H044954)
(0.41 MB)
7 Amarnath, Giriraj; Sharma, Bharat. 2013. Manual of the Training on Application of Remote Sensing and GIS in Flood Inundation Mapping for Spate Irrigation Assessment in Sudan, jointly organized by IWMI, the Hydraulic Research Institute (HRS), Sudan, and the International Fund for Agricultural Development (IFAD), held at Wad Medani, Sudan, 15-17 January 2013. Colombo, Sri Lanka: International Water Management Institute (IWMI). 96p.
(Location: IWMI HQ Call no: e-copy only Record No: H045698)
(3.34 MB)
(Location: IWMI HQ Call no: 621.3678 G000 CAR Record No: H046804)
(0.76 MB)
9 Chemin, Yann H. 2014. Remote sensing raster programming. 3rd ed. Raleigh, NC, USA: Lulu Press Inc. 111p.
(Location: IWMI HQ Call no: IWMI Record No: H046885)
(0.87 MB)
(Location: IWMI HQ Call no: e-copy only Record No: H052989)
(3.46 MB) (3.46 MB)
Purpose and Methods: Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference. Results: The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier. Conclusion: The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics.
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