Drought Monitoring and assessment using earth observation data for Zambia

This study attempts to identify the spatial-temporal extent of the agricultural drought in Zambia using remote sensing data and crop production. IDSI is superior in terms of its performance and drought detection capability and is characterized by better representation of drought severity. Also VCI, IDSI and SPI indices were found to be very useful in monitoring the spatial-temporal extent of agricultural and climatic drought in the country. To validate the VCI and SPI based estimates, the correlation between VCI-CYA and SPI-CYA was analyzed and shown to have a strong positive correlation r2 0.72 and 0.68 respectively. The findings of drought hazard analysis could prove to be extremely useful in promoting integrated drought risk management strategies to mitigate the potential impact of drought on the agricultural sector and accelerate adaptation actions. In addition, it provides a robust foundation for decision-makers to recommend disaster preparedness and mitigation measures to minimize the effects of future droughts

GEE Training Manual on Use of Earth Observation data and Google Earth Engine monitoring and early warning of floods and droughts in Zambia

This training manual supported participants in learning the pre-processing tool to provide the user with enhanced time-series processing capabilities and access to various open-source satellite data, learning basic scripts in Google Earth Engine for activities related to floods and drought in showcasing the application of water resource management. Specifically, the experts will give more focus to Google’s Earth Engine platform to showcase large- and small-scale scientific analysis and visualization of geospatial datasets. The codes and step by step procedure are given in the manual

Insurance as an agricultural disaster risk management tool: evidence and lessons learned from South Asia

Pilot projects in India and Bangladesh demonstrate that index-based weather insurance products, developed using satellite technology, can reduce the financial risks to smallholder farmers from floods and droughts. Scaling up such schemes has the potential to meet the needs of very vulnerable groups, especially women and assist governments in meeting global development goals

Agricultural drought monitoring in Sri Lanka using multisource satellite data

Drought is a complex phenomenon affecting agricultural, environmental, water resources, and socio-economic systems in developing regions. Climate change is going to increase the frequency and intensity of drought events and the associated socio-economic impact, including that on the food security among marginal smallholder farmers. For timely early action, it is important to have robust drought monitoring and warning in place to determine the timely drought situation to assist end-users for the decision-making process. Spatialtemporal remote sensing data provides crucial information on near real-time drought monitoring and early warning. The present study developed a composite index, i.e., Integrated Drought Severity Index (IDSI) that combines inputs from rainfall, vegetation, and temperature to determine agricultural drought progression, intensity, and frequency for entire Sri Lanka between 2001 and 2019. The study has successfully identified 10 drought events of which 2001, 2012, 2017, and 2019 reported severe droughts across the two rainy seasons, namely Yala (May–August) and Maha (October–March). We analyzed various indices meteorological drought Standardized Precipitation Index (SPI) and field-based rice Crop Yield Anomaly Index (CYA). It is evident from the study that the Yala season reported more drought events compared to the Maha season due to changes in monsoon onset and duration and its seasonal variability. The correlation coefficient for SPI with IDSI is 0.70 and IDSI with CYA is 0.68, which explains the reliability of drought monitoring information across Sri Lanka. In terms of sub-national drought events, the North, North Central, North Eastern, Eastern, and South Eastern Provinces which cover the majority of the dry zone of Sri Lanka and districts such as Anuradhapura, Monaragala, Polonnaruwa, Hambantota, Trincomalee, and Ampara are highly prone to agricultural drought impacting agricultural production and the vulnerable rural population. From the basin analysis, both Yan Oya and Malwathu Oya (Aruri Aru) are reported to have severe drought events, which highlights the need for timely action using satellite-derived agricultural drought monitoring to mitigate drought risks and reduce food insecurity

Novel Index for Hydrological Drought Monitoring Using Remote Sensing Approach: Standardized Water Surface Index (SWSI)

Most of the drought indices designed for hydrological drought monitoring use location-specific data, while there are only a handful of indices designed for hydrological drought monitoring using remote sensing data. This study revealed a novel drought index, Standardized Water Surface Index (SWSI), developed for hydrological drought monitoring. The water surface areas required to calculate the SWSI can be extracted from remote sensing data entirely using both the optical (Landsat 5, 7, and 8) and SAR (Sentinel-1). Furthermore, the developed index was applied to five major reservoirs/tanks; Iranamadu, Mahavilachchiya, Kantale, Senanayaka Samudhraya, and Udawalawa, located in Sri Lanka to monitor respective hydrological drought status for the period from 2000 to 2020. Cloud computing platform such as Google Earth Engine (GEE) provides a good basement to use this index effectively, as it can extract long-term water surface area covering a large geographical area efficiently and accurately. The surface water area extraction from satellite data of those tanks shows an accuracy of more than 95%, and in the event of a severe hydrological drought, the water surface area of the tanks is less than 25% of the total and lasts for more than three to four months. It was also determined that in some years, the surface water area of tanks dropped to as low as 7%. The strong correlation observed between the Standardized Precipitation Index (SPI) and SWSI is indicated by the Pearson correlation coefficient ranging from 0.58 to 0.67, while the correlation between the Vegetation Condition Index (VCI) and SWSI ranges from 0.75 to 0.81. Timely drought monitoring over large geographical areas can be more accurately performed with the SWSI index compared to existing hydrological drought monitoring indices. The SWSI could be more useful for areas that do not have measurable field data

Novel Index for Hydrological Drought Monitoring Using Remote Sensing Approach: Standardized Water Surface Index (SWSI)

Most of the drought indices designed for hydrological drought monitoring use location-specific data, while there are only a handful of indices designed for hydrological drought monitoring using remote sensing data. This study revealed a novel drought index, Standardized Water Surface Index (SWSI), developed for hydrological drought monitoring. The water surface areas required to calculate the SWSI can be extracted from remote sensing data entirely using both the optical (Landsat 5, 7, and 8) and SAR (Sentinel-1). Furthermore, the developed index was applied to five major reservoirs/tanks; Iranamadu, Mahavilachchiya, Kantale, Senanayaka Samudhraya, and Udawalawa, located in Sri Lanka to monitor respective hydrological drought status for the period from 2000 to 2020. Cloud computing platform such as Google Earth Engine (GEE) provides a good basement to use this index effectively, as it can extract long-term water surface area covering a large geographical area efficiently and accurately. The surface water area extraction from satellite data of those tanks shows an accuracy of more than 95%, and in the event of a severe hydrological drought, the water surface area of the tanks is less than 25% of the total and lasts for more than three to four months. It was also determined that in some years, the surface water area of tanks dropped to as low as 7%. The strong correlation observed between the Standardized Precipitation Index (SPI) and SWSI is indicated by the Pearson correlation coefficient ranging from 0.58 to 0.67, while the correlation between the Vegetation Condition Index (VCI) and SWSI ranges from 0.75 to 0.81. Timely drought monitoring over large geographical areas can be more accurately performed with the SWSI index compared to existing hydrological drought monitoring indices. The SWSI could be more useful for areas that do not have measurable field data

Spatial variability of rainfall trends in Sri Lanka from 1989 to 2019 as an indication of climate change

Analysis of long-term rainfall trends provides a wealth of information on effective crop planning and water resource management, and a better understanding of climate variability over time. This study reveals the spatial variability of rainfall trends in Sri Lanka from 1989 to 2019 as an indication of climate change. The exclusivity of the study is the use of rainfall data that provide spatial variability instead of the traditional location-based approach. Henceforth, daily rainfall data available at Climate Hazards Group InfraRed Precipitation corrected with stations (CHIRPS) data were used for this study. The geographic information system (GIS) is used to perform spatial data analysis on both vector and raster data. Sen’s slope estimator and the Mann–Kendall (M–K) test are used to investigate the trends in annual and seasonal rainfall throughout all districts and climatic zones of Sri Lanka. The most important thing reflected in this study is that there has been a significant increase in annual rainfall from 1989 to 2019 in all climatic zones (wet, dry, intermediate, and Semi-arid) of Sri Lanka. The maximum increase is recorded in the wet zone and the minimum increase is in the semi-arid zone. There could be an increased risk of floods in the southern and western provinces in the future, whereas areas in the eastern and southeastern districts may face severe droughts during the northeastern monsoon. It is advisable to introduce effective drought and flood management and preparedness measures to reduce the respective hazard risk levels

A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale

This study reports a comprehensive review on drought indices used in monitoring meteorological, agricultural, hydrological, and socio-economic drought. Drought indices have been introduced as an important approach to quantitative and qualitative calculations of drought’s severity and impact. There were 111 drought indices reviewed in this study, which fall into two categories: traditional (location-specific/model) and remote sensing (RS). Out of 111 indices, 44 belong to the traditional indices and 67 belong to the RS section. This study shows that meteorological drought monitoring has the highest number (22) of traditional indices, about 20% overall, while the lowest (7) agricultural drought monitoring is 6.3%. The specialty is that when considering remote sensing-based drought indices, 90% are used for agricultural drought monitoring and 10% for hydrological and meteorological drought monitoring. However, the study found that advances in satellite technology have accelerated the design of new drought indices and that replacing traditional location-specific data with satellite observation makes it easier to calculate more spatial distribution and resolution

Inundations in the Sri Lanka: monitoring and analysis from MODIS [Moderate Resolution Imaging Spectroradiometer] and ALOS [Advanced Land Observing Satellite] instrument

Sri Lanka is facing severe flood events during monsoon rainfall in each year all over the country. The rapid development of remote sensing and widely available satellite images can be used effectively to map the flood inundation in past years. This study is focused on the mapping of flood inundation together with flood recurrent based on both optical (MODIS) and microwave (ALOS/PALSAR) satellite images. In the first stage MODIS images with spatial resolution of 500m and temporal interval of eight day was used to map flood recurrent areas for risk assessment using images from 2000 to 2011. In the second state 16 satellite images from ALOS PALSAR images between 2006 and 2011 was analyzed by using pixel threshold value to map the flooded and non-flooded areas. The flood recurrent products from both MODIS and PALSAR images were generated to represent the repetition of flood inundated areas. The analysis of the results indicated that the PALSAR image based flood inundation mapping is much accurate and useful in the context of spatial variability than the temporal variability. The accurate land-cover map is also important to assess the flood damages and evaluate the future development and the cultivation planning. But there is no such an accurate and detailed land-cove map available for Sri Lanka to assess the flood damages. Thus, this study was focused on the preparation of land-cover map with GIS and RS approach. The land-cover classification was carried out by image fusion of optical (LANDSAT) and microwave (ALOS/PALSAR) under High Pass Filtering (HPF) technique. Unsupervised image classification method was used to classify the fused image in to different land-cover classes. Accuracy assessment of land-cover classification was conducted using existing ground truth information and Google Earth with as resulted in the overall accuracy as 71.16% and the Kappa statistics as 62.83%