Monitoring Drought with the Combined Drought Index in Kenya

Balint, Z.; Mutua, Francis; Muchiri, Peris; Omuto, Christian

doi:10.1016/b978-0-444-59559-1.00023-2

Cited by 44 publications

(50 citation statements)

References 17 publications

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“…Five main topics were chosen to illustrate the high potential of information derived from remote sensing. In doing so, strong reference is made to the work of Kastens et al (2005) [7], Zhang et al (2005) [8], Balint et al (2011) [9], Galford et al (2008) [10], Wardlow et al (2007) [11], Sakamoto et al (2005) [12] and Verbesselt et al (2010) [13]. The five selected applications are: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover changes.…”

Section: Introductionmentioning

confidence: 99%

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Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

2013

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Many remote sensing applications are devoted to the agricultural sector.Representative case studies are presented in the special issue "Advances in Remote Sensing of Agriculture". To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations.

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Section: Introductionmentioning

confidence: 99%

“…A suitable drought index should combine information about precipitation, temperature and soil moisture strength and persistence. Such an index was, for example, conceived by [9] and termed Combined Drought Index (CDI).…”

Section: Biomass and Yieldmentioning

confidence: 99%

Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

2013

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“…The ECDI is based on a modified version of the Combined Drought Index (CDI) that was originally developed by the UN FAO Somalia Water and Land Information Management (SWALIM) team [31] to link anomalies of in-situ measurements (rainfall, temperature) and the NDVI. The proposed new method replaces all in-situ observations with state-of-the-art satellite-derived datasets and adds a soil moisture component.…”

Section: Methodsmentioning

confidence: 99%

“…The proposed ECDI is an improvement of the original CDI [31] where soil moisture was not included. The ECDI closes the gap between rainfall and the response of vegetation by introducing a soil moisture component that is retrieved via (passive) microwave remote sensing (Section 3.1.1).…”

Section: Satellite Data and Drought Indicesmentioning

confidence: 99%

“…Deficits in precipitation, soil moisture and vegetation health indicate drought conditions, as well as abnormally high land surface temperatures. Balint and Mutua [31,76] recommend modifying the raw time series of temperature and precipitation as well as the run length to adjust the range of all variables and to avoid a division by zero (Equation (3)). In addition, the temperature and the run length parameter, which counts the successive decades with above normal (temperature) or below normal (rainfall and soil moisture) conditions, are inverted:…”

Section: Computation Of the Monitoring Ecdimentioning

confidence: 99%

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A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations

et al. 2016

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Governments, aid organizations and researchers are struggling with the complexity of detecting and monitoring drought events, which leads to weaknesses regarding the translation of early warnings into action. Embedded in an advanced decision-support framework for Doctors without Borders (Médecins sans Frontières), this study focuses on identifying the added-value of combining different satellite-derived datasets for drought monitoring and forecasting in Ethiopia. The core of the study is the improvement of an existing drought index via methodical adaptations and the integration of various satellite-derived datasets. The resulting Enhanced Combined Drought Index (ECDI) links four input datasets (rainfall, soil moisture, land surface temperature and vegetation status). The respective weight of each input dataset is calculated for every grid point at a spatial resolution of 0.25 degrees (roughly 28 kilometers). In the case of data gaps in one input dataset, the weights are automatically redistributed to other available variables. Ranking the years 1992 to 2014 according to the ECDI-based warning levels allows for the identification of all large-scale drought events in Ethiopia. Our results also indicate a good match between the ECDI-based drought warning levels and reported drought impacts for both the start and the end of the season.

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A data fusion‐based drought index

Azmi

Rüdiger

Walker

2016

Water Resources Research

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Drought and water stress monitoring plays an important role in the management of water resources, especially during periods of extreme climate conditions. Here, a data fusion‐based drought index (DFDI) has been developed and analyzed for three different locations of varying land use and climate regimes in Australia. The proposed index comprehensively considers all types of drought through a selection of indices and proxies associated with each drought type. In deriving the proposed index, weekly data from three different data sources (OzFlux Network, Asia‐Pacific Water Monitor, and MODIS‐Terra satellite) were employed to first derive commonly used individual standardized drought indices (SDIs), which were then grouped using an advanced clustering method. Next, three different multivariate methods (principal component analysis, factor analysis, and independent component analysis) were utilized to aggregate the SDIs located within each group. For the two clusters in which the grouped SDIs best reflected the water availability and vegetation conditions, the variables were aggregated based on an averaging between the standardized first principal components of the different multivariate methods. Then, considering those two aggregated indices as well as the classifications of months (dry/wet months and active/non‐active months), the proposed DFDI was developed. Finally, the symbolic regression method was used to derive mathematical equations for the proposed DFDI. The results presented here show that the proposed index has revealed new aspects in water stress monitoring which previous indices were not able to, by simultaneously considering both hydrometeorological and ecological concepts to define the real water stress of the study areas.

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Monitoring Drought with the Combined Drought Index in Kenya

Cited by 44 publications

References 17 publications

Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations

A data fusion‐based drought index

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