Your search found 7 records
1 Biggs, Trent; Scott, Christopher; Rajagopalan, Balaji; Turral, Hugh. 2007. Trends in solar radiation due to clouds and aerosols, Southern India, 1952-1997. International Journal of Climatology, 27(11): 1505–1518.
(Location: IWMI-HQ Call no: IWMI 551.5271 G635 BIG Record No: H039743)
Decadal trends in cloudiness are shown to affect incoming solar radiation (SW SFC) in the Krishna River basin (13–20°N, 72–82 °E), southern India, from 1952 to 1997. Annual average cloudiness at 14 meteorological stations across the basin decreased by 0.09% of the sky per year over 1952–1997. The decreased cloudiness partly balanced the effects of aerosols on incoming solar radiation (SW SFC), resulting in a small net increase in SW SFC in monsoon months (0.1–2.9 W m-2 per decade). During the non-monsoon, aerosol forcing dominated over trends in cloud forcing, resulting in a net decrease in SW SFC (-2.8 to -5.5 W m-2 per decade). Monthly satellite easurements from the International Satellite Cloud Climatology Project (ISCCP) covering 1983–1995 were used to screen the visual cloudiness measurements at 26 meteorological stations, which reduced the data set to 14 stations and extended the cloudiness record back to 1952. SW SFC measurements were available at only two stations, so the SW SFC record was extended in time and to the other stations using a combination of the Angstrom and Hargreaves-Supit equations. The Hargreaves-Supit estimates of SW SFC were then corrected for trends in aerosols using the literature values of aerosol forcing over India. Monthly values and trends in satellite measurements of SW SFC from National Aeronautics and Space Administration’s (NASA’s) surface radiation budget (SRB) matched the aerosol-corrected Hargreaves-Supit estimates over 1984–1994 (RMSE = 11.9 W m-2, 5.2%). We conclude that meteorological station measurements of cloudiness, quality checked with satellite imagery and calibrated to local measurements of incoming radiation, provide an opportunity to extend radiation measurements in space and time. Reports of decreased cloudiness in other parts of continental Asia suggest that the cloud-aerosol trade-off observed in the Krishna basin may be widespread, particularly during the rainy seasons when changes in clouds have large effects on incoming radiation compared with aerosol forcing.
2 Rientjes, T. H. M.; Haile, A. T.; Gieske, A. S. M.; Maathuis, B. H. P.; Habib, E. 2011. Satellite based cloud detection and rainfall estimation in the Upper Blue Nile Basin. In Melesse, A. M. (Ed.). Nile River Basin: hydrology, climate and water use. Dordrecht, Netherlands: Springer. pp.93-107.
Remote sensing ; Satellite observation ; Clouds ; Rain ; Estimation ; River basins / Ethiopia / Lake Tana / Upper Blue Nile River Basin
(Location: IWMI HQ Call no: 551.483 G136 MEL Record No: H044024)
3 Ray, K.; Mohapatra, M.; Bandyopadhyay, B. K.; Rathore, L. S. (Eds.) 2015. High-impact weather events over the SAARC Region. Cham, Switzerland: Springer International Publishing; New Delhi, India: Capital Publishing Company. 414p. [Selected papers presented at the SAARC Seminar on High Impact Weather Events over SAARC Region, New Delhi, India, 2-4 December, 2013] [doi: https://doi.org/10.1007/978-3-319-10217-7]
Weather forecasting ; Simulation models ; Remote sensing ; Radar satellite ; Satellite observation ; Assimilation ; Monsoon climate ; Rainfall patterns ; Hail ; Natural disasters ; Thunderstorms ; Cyclones ; Drought ; Temperature ; Clouds ; Early warning systems ; Diagnostic techniques ; Performance evaluation ; Statistical methods ; Agriculture ; Monitoring ; Assessment ; Coastal area ; Case studies / South Asia / India / Bangladesh / Pakistan / Arabian Sea / Bay of Bengal / Uttar Pradesh / Gujarat / Bihar / Delhi / Uttarakhand / Cherrapunji
(Location: IWMI HQ Call no: 551.6 G570 RAY Record No: H047218)
(0.37 MB)
4 Mondal, S.; Jeganathan, C.; Amarnath, Giriraj; Pani, Peejush. 2017. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring. GIScience and Remote Sensing, 54(2):202-229. [doi: https://doi.org/10.1080/15481603.2017.1286726]
Climate change ; Drought ; Clouds ; Noise ; Monitoring ; Vegetation ; Satellite observation ; Satellite imagery ; Land cover mapping ; Remote sensing ; Spatial distribution ; Models ; Statistical methods ; Performance evaluation ; Homogenization ; Agriculture / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H048010)
Moderate Resolution Imaging Spectro-radiometer (MODIS) time-series Normalized Differential Vegetation Index (NDVI) products are regularly used for vegetation monitoring missions and climate change analysis. However, satellite observation is affected by the atmospheric condition, cloud state and shadows introducing noise in the data. MODIS state flag helps in understanding pixel quality but overestimates the noise and hence its usability requires further scrutiny. This study has analyzed MODIS MOD09A1 annual data set over Sri Lanka. The study presents a simple and effective noise mapping method which integrates four state flag parameters (i.e. cloud state, cloud shadow, cirrus detected, and internal cloud algorithm flag) to estimate Cloud Possibility Index (CPI). Usability of CPI is analyzed along with NDVI for noise elimination. Then the gaps generated due to noise elimination are reconstructed and performance of the reconstruction model is assessed over simulated data with five different levels of random gaps (10–50%) and four different statistical measures (i.e. Root mean square error, mean absolute error, mean bias error, and mean absolute percentage error). The sample-based analysis over homogeneous and heterogeneous pixels have revealed that CPI-based noise elimination has increased the detection accuracy of number of growing cycle from 45–60% to 85–95% in vegetated regions. The study cautions that usage of time-series NDVI data without proper cloud correction mechanism would result in wrong estimation about spatial distribution and intensity of drought, and in our study 50% of area is wrongly reported to be under drought though there was no major drought in 2014.
5 Zhao, W.; Duan, S.-B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS [Moderate Resolution Imaging Spectroradiometer]/terra land products and MSG [Meteosat Second Generation] geostationary satellite data. Remote Sensing of Environment, 247:111931. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111931]
Land cover ; Air temperature ; Satellite observation ; Geostationary satellite ; Moderate resolution imaging spectroradiometer ; Clouds ; Solar radiation ; Vegetation ; Regression analysis ; Models / Europe
(Location: IWMI HQ Call no: e-copy only Record No: H049904)
(5.29 MB)
There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature (LST) because of its strong relationships with many surface processes. However, the frequent occurrence of cloud cover induces a large blank area in current thermal infrared-based LST products. To effectively fill this blank area, a new method for reconstructing the cloud-covered LSTs of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) daytime observations is described using random forest (RF) regression approach. The high temporal resolution of the Meteosat Second Generation (MSG) LST product assisted in identifying the temporal variations in cloud cover. The cumulative downward shortwave radiation flux (DSSF) was estimated as the solar radiation factor for each MODIS pixel based on the MSG DSSF product to represent the impact from cloud cover on incident solar radiation. The RF approach was used to fit an LST linking model based on the datasets collected from clear-sky pixels that depicted the complicated relationship between LST and the predictor variables, including the surface vegetation index (the normalized difference vegetation index and the enhanced vegetation index), normalized difference water index, solar radiation factor, surface albedo, surface elevation, surface slope, and latitude. The fitted model was then used to reconstruct the LSTs of cloud-covered pixels. The proposed method was applied to the Terra/MODIS daytime LST product for four days in 2015, spanning different seasons in southwestern Europe. A visual inspection indicated that the reconstructed LSTs thoroughly captured the distribution of surface temperature associated with surface vegetation cover, solar radiation, and topography. The reconstructed LSTs showed similar spatial pattern according to the comparison with clear-sky LSTs from temporally adjacent days. In addition, evaluations against Global Land Data Assimilation System (GLDAS) NOAH 0.25° 3-h LST data and reference LST data derived based on in-situ air temperature measurements showed that the reconstructed LSTs presented a stable and reliable performance. The coefficients of determination derived with the GLDAS LST data were all above 0.59 on the four examined days. These results indicate that the proposed method has a strong potential for reconstructing LSTs under cloud-covered conditions and can also accurately depict the spatial patterns of LST.
6 Ali, S.; Cheema, M. J. M.; Waqas, M. M.; Waseem, M.; Awan, Usman Khalid; Khaliq, T. 2020. Changes in snow cover dynamics over the Indus Basin: evidences from 2008 to 2018 MODIS NDSI trends analysis. Remote Sensing, 12(17):2782. (Special issue: Interactive Deep Learning for Hyperspectral Images) [doi: https://doi.org/10.3390/rs12172782]
Snow cover ; Estimation ; Mapping ; Trends ; River basins ; Catchment areas ; Temperature ; Clouds ; Landsat ; Satellite imagery ; Moderate resolution imaging spectroradiometer ; Uncertainty / Pakistan / Indus Basin / Himalayas / Chenab River Catchment / Jhelum River Catchment / Indus River Catchment / Eastern Rivers Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H050209)
(4.20 MB) (4.20 MB)
The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.
7 Shastry, A.; Carter, E.; Coltin, B.; Sleeter, R.; McMichael, S.; Eggleston, J. 2023. Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation. Remote Sensing of Environment, 291:113556. (Online first) [doi: https://doi.org/10.1016/j.rse.2023.113556]
Flooding ; Mapping ; Remote sensing ; Satellite imagery ; Clouds ; Vegetation ; Machine learning ; Hydraulic models ; Neural networks ; Surface water
(Location: IWMI HQ Call no: e-copy only Record No: H051858)
https://www.sciencedirect.com/science/article/pii/S0034425723001074/pdfft?md5=b3f4e78dda3ffc16597de5f66cbd2c21&pid=1-s2.0-S0034425723001074-main.pdf
https://vlibrary.iwmi.org/pdf/H051858.pdf
https://vlibrary.iwmi.org/pdf/H051858.pdf
(10.00 MB) (10.0 MB)
Floods are one of the most devastating natural calamities affecting millions of people and causing damage all around the globe. Flood models and remote sensing imagery are often used to predict and understand flooding. An increasing number of earth observation satellites are producing data at a rate that far outpaces our ability to manually extract meaningful information from it, motivating a surge in research on automatic feature detection in satellite imagery using machine learning and deep learning algorithms to automate flood mapping so that information from large streams of data can be extracted in near-real time and used for disaster response at landscape scale. The development of such an algorithm is predicated on exposure to training datasets that are representative of the full range of diversity in the spatial and spectral signature of surface water as it is sampled by space-based instruments. To address these needs, we developed a semantically labeled dataset of high-resolution multispectral imagery (Maxar WorldView 2/3) strategically sampled to be representative of North American surface water variability along five spatiotemporal strata: latitude, topographic complexity, land use, and day of year. This dataset was utilized to train a convolutional neural network (CNN) to automatically detect inundation extents using the Deep Earth Learning, Tools, and Analysis (DELTA) framework, an open source TensorFlow/Keras interpreter for satellite imagery. Our research objective was to demonstrate the out-of-sample accuracy of our trained CNN at landscape scale. The model performed well, with 98% precision and 94% recall for the water class during validation. We then evaluated the accuracy of our satellite-derived flood maps from trained machine learning model against a hydraulic model. For this, we compared predicted inundation extents against the USGS Flood Inundation Mapping (FIM) Program's flood map library at 17 different locations, where the FIM library provides flood inundation extents based on hydraulic models built for river reaches and corresponding to stage measurements at a nearby USGS gaging site. Compared to the hydraulic model, we estimated the underprediction of flood inundation by optical remote sensing data in our areas of interest to be 62%. We used land use data from National Land Cover Database (NLCD) and cloud masks to estimate that 79% of underprediction was due to these obstructions, with 74% belonging to vegetation, 9% to clouds, and 4% to both. A significant amount of inundation is missed when only optical remote sensing data is considered, and we suggest the use of flood models along with remote sensing data for getting the most realistic flood inundation extents.
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