Abstract
Understanding the dynamics of the earth’s surface variation patterns has been critical for climate change adaptation and mitigation. During the last decades, detecting these events through remote sensing allowed us to improve the conventional analysis toward an integrated space–time analysis. This chapter proposes a spatiotemporal exploratory analysis of the information from SPI, SPEI and links its results into remote sensing information of NDVI using computer vision algorithms for pattern recognition and tracking. This analysis was carried out in three phases. First, a 20-year analysis of vegetation-based indices (NDVI) and meteorological drought indices (SPI, SPEI), to identify and compare the water anomalies over Central America dry corridor, using ERA5 climatological information and satellite images for the period 2000–2020. These results are used to assess the spatiotemporal variations of meteorological stress and vegetation water stress. All this is analyzed considering the conditions along the phenological cycle. The implementation of the spatiotemporal drought methodology proposed by Corzo and Vitali, 2018, and its results used as input time series, through LOWESS smoothing proposed by Jong (Remote Sens Environ 115(2):692–702, 2011). The final comparison uses statistical metrics such as spatial correlation. Drought units are identified for each meteorological drought index and are compared among them, and together with the NDVI normalization, a vegetation-based drought index (vegetation condition index or VCI) is estimated. This step allows representing the phenological conditions of vegetative water stress without interferences of temporality and consistency. Finally, the VCI is classified in categorical ranges that allow the comparison of drought units to the SPI in different lags (1, 3, 6) and SPEI (1, 3, 6). By this, establishing meteorological relationships with the vegetative surface dynamics and generating the trajectories (tracking) of each drought cluster observed with the VCI. Finally, a validation of the trajectories are also compared. All validation show that his methodology allows using directly inferred drought from remote sensing as a meteorological drought index, in similar way as SPI. The spatiotemporal changes monitoring and evaluation associated with land cover and water sources, and derivation of drought index based on vegetative condition is an essential component of this chapter’s contribution.
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Keywords
5.1 Introduction
Regarding the WMO (Wilhite 2006), droughts relate to water deficiency in a particular region, and their severity has potential repercussions in diverse contexts (Serda 2013). Recent studies indicate that droughts’ frequency and severity appear to be increasing in some zones due to climate variability and change (Podestá et al. 2016). Also, its occurrence is cyclical and is related to the El Niño Southern Oscillation ENSO (van der Zee Arias et al. 2012), which influences both the irregularity of the precipitation cycle and the spatiotemporal magnitude of impact at scales. Regional and local, limiting the natural resilience capacity of the territory.
Relate to Diaz et al. 2020a, spatiotemporal methodologies applied to droughts have been developed. These have managed to generate a better understanding of spatiotemporal dynamics, strengthening their monitoring and impact reduction (Cai 2014). Several approaches have been proposed to describe the spatiotemporal development of drought. According to Bacanli et al. 2009; and Dracup et al. 1980, propose methodologies that apply to force a significant number of meteorological variables in the drought indexes calculation based on time series disaggregation of relating it to the spatiality of all zone, establishing a unique phenomenon in the zone.
Among the tools developed in recent decades, remote sensing has shown great promise for improving the calculation and monitoring of drought events. For this, vegetation indices and data on drought, humidity, and surface temperature derived from satellite images are used by Sahaar and Niemann (2020); Ghulam et al. 2007). Following (Tadesse et al. 2012), reported significant correlations between the normalized difference vegetation index (NDVI), obtained from MODIS images, calculated every 16 days, and drought. Tadesse et al. (2005) showed that when using NDVI, despite being an effective indicator of humidity and vegetation conditions, there is a lag between the occurrence of the drought event and the change in its values, which indicates that this index is not appropriate to monitor the drought conditions of crops, in real-time conditions (Nihoul 2005)
5.2 Case Study
Central America is one of the world’s regions most exposed to the risks of natural hazards and climate variability making climate hazards the most prevalent, represented mainly by frequent droughts and severe spontaneous floods, affecting agricultural production, with greater intensity in degraded areas (Eckstein et al. 2017).
Belize, Guatemala, Honduras, Panama, El Salvador, constitutes the dry forest ecosystem corridor of Central America (Fig. 5.1). Caused by the geographic location, Central America have more probability to receive strong heat waves, also, influence of global climatic phenomena and hurricane seasons prevent a progressive recovery of its hydric condition in terms of food and environmental security (van der Zee Arias et al. 2012).
The hydrometeorological records shows that Central America had experienced an average temperature change, closing of 0.05 °C yearly, it means 0.5–1 °C of historical anomalies by temperature (Fig. 5.2) in last 40 years. Otherwise, rainfall rates have tended to decrease in 12 mm/year during the same period (Fig. 5.3).
According to the Intergovernmental Panel on Climate Change (IPCC), Central America is categorized as the Latin America region, with most likely to be influenced by climate change impacts and effects (Frieler et al. 2017; Anandhi et al. 2008). Which is supported by increased flooding and prolonged droughts among other adverse natural hazards (hurricanes, earthquakes).
Water supply is linked to intra-annual precipitation fluctuations as well as geographical variability. During El Niño events, annual rainfall can decrease between 38 and 42%, with extended warm periods and more extended dry spells.
High temperature seasons and the dry climate conditions have catastrophic consequences on agricultural development yield and affecting food security (Bae et al. 1981; FAO 2019). Added to the tropical storm season has damaging effects in terms of critical water levels, pollutions preventing the water system near resilience (FAO 2019).
The hazards generated by flooding or droughts mainly, have increased in recent years. Growing food insecurity and water supply availability, increasing climatic trends, and socioeconomic stresses in the region have caused the displacement of people from their homes and communities in 1.6 millions in the last decade (Buttafuoco and Caloiero 2014).
As an example, Fig. 5.4 and 5.5 allows to understand the ecological, socio-ecological, economic, and cultural impacts of land use change on Central America, trends in land use and subsoil use change have been identified, allowing us to establish few grid cells changes mainly in forest and wetlands to woody savannas about 20% reduction of forest structures to pasture (Anderson et al. 2019; Maldonado et al. 2016). Reinforced by agricultural expansion in the region around 46% (van der Zee Arias et al. 2012). This to establish potential land cover that may be affected by drought and may cause multi-country unsustainability.
5.3 Methodology
5.3.1 Data Acquisition
Past drought events required the use of comprehensive and detailed satellite data to assess and monitor past drought events. Both hydrometeorological variables and earth observation data were used (Sexton et al. 2013). As shown in Table 5.1. Where data sets of the best available temporal and spatial resolution are used.
5.3.2 Drought Calculation
Drought calculation needs and indicator that represents a quantitative drought condition according with the magnitude of specific variable. Drought indicator can be used to define drought (dry condition) with any variable like meteorological drought: based on precipitation condition or agricultural drought: based on water balance [P-ETP] (Van Loon 2015).
However, drought indices are not universally accepted to characterize the drought conditions (Okal et al. 2020) because depends of the driver factor and regional context. In this way, the rainfall and vegetation health are significant factors that could show the influence of drought frequency or severity for instance.
5.3.3 Drought Vegetation Monitoring Indexes
The analysis of surface drought conditions (vegetation response, soil moisture) based on remote sensing information involves transforming the data or bands and applying a standardized transformation as a vegetation index. MODIS datasets were used to estimate vegetation indices for the period 2000–2020. Among these indices, the most common and sensitive to vegetative dynamics are NDVI and VCI.
5.3.4 Normalized Difference Vegetation Index NDVI
The normalized difference vegetation index NDVI, is an index that measure of the phenological vegetation condition. It was computed from Terra platform with 250 m spatial resolution from MODIS server using the relation of Infrared (NIR) and Red band (RED) as its shown in (5.1).
This product was corrected using GIS techniques to correct geometrical overlapping and radiometric fluctuations and cloudy pixels were removed (Kogan 1997). Then was categorized to represent drought condition scale as (Rokhmatullah et al. 2018), that represents whether photosynthesis characteristics is above or below of average condition (Table 5.2).
5.3.5 Vegetation Condition Index VCI
Vegetation condition index VCI, is calculated from NDVI product using (5.2). VCI is a pixel-based index by considering of mean and maximum multi-annual variability (Kogan 1997).
where VCIijk is the VCI value for the pixel i during month j for year k, VCIijk is the monthly, VCI value for pixel i in month j for year k. VCI, VCIi, min and VCIi, max are the multiyear minimum and maximum of VCI, respectively, for pixel i (Kogan 1997).
Then was categorized to represent drought condition scale regarding with the vegetation growing season as 30% below, as its shown below on Table 5.3 (Rokhmatullah et al. 2018).
5.3.6 Climatological Drought Indexes
Drought indices are associated numerically with the value of hydroclimatic variables that may reflect changes in their hydrological regime or quantity, such as meteorological, agricultural, and hydrological variables, representing factors that contribute to or counteract the occurrence and propagation of droughts (Habibi et al. 2020). These methodologies, mainly involve a simplification process, in which an anomaly or a normalization of the values of these driving factors is considered to influence the magnitude of other physical variables highly related to water deficits, the most used of which generalize a standardization of the driving source of water such as precipitation (SPI) and water balance (SPEI) (Depsky and Pons 2020; Belayneh et al. 2014).
5.3.6.1 Standardized Precipitation Index SPI
The SPI computation is based on the long-term precipitation records cumulated over a selected time scale. According with Husak, 2007. This methodology allows to fit this kind of data using gamma probabilistic distribution (5.3) and then standardized by normal distribution to obtain deviations from each precipitation record (Blain 2011).
where, α, β are scale and shape parameters that could be obtained using L-moments method. According to the SPI, drought starts when the SPI value is equal or less than −1.0 and ends when the value becomes zero or high (Table 5.4).
5.3.6.2 Standardized Precipitation Evapotranspiration Index SPEI
Standardized Precipitation Evapotranspiration Index SPEI is calculated as water balance stablishing the difference between precipitation and potential evapotranspiration PET for the desired month as (5.4), then its normalized by log-logistic probabilistic distribution (5.5) to define the drought indicator (Diaz et al. 2020b), describing the water balance impacts according to the drought severity. The Thornwaite approach is widely applied to compute the PET (Bae et al. 1981).
where, (α, β ,γ) are scale, shape and origin parameters that could be obtained using L-moments method. Then as SPI, drought scale is the same (Table 5.5).
5.3.7 Spatiotemporal Monitoring
The spatiotemporal approach is based on the methodology proposed by Corzo and Vitali, 2018. This methodology allows to perform an articulated analysis between the spatial and temporal changes of the observed drought events within the region, after the extraction of the drought extensions (areas), and its subsequent estimation of its location and temporal marks that allow evaluating its propagation and mobility in space and time, which allows addressing the problem related to its dynamics. (Diaz et al. 2020b). However, this methodology has only been applied under the use of hydroclimatic drought indices such SPI or SPEI, so the proposed methodology proposes three stages, initially correlative analysis for the estimation of related accumulation periods (SPI-n, SPEI-n, VI-n) followed by the analysis of non-contiguous drought areas (NCDA). Finally, applying continuous drought areas (CDA) approach and tracking.
5.3.7.1 Correlation Analysis
To implement the spatiotemporal tracking methodology, it is necessary to identify the relationship between drought areas, magnitude and frequency, which are measurable between climatological and vegetation-based indices. This will allow generating a better understanding of the applicability of satellite indices in spatiotemporal drought monitoring (Jiao et al. 2016). For this purpose, Pearson’s correlation test was used, between the monthly time series of SPI-n, SPEI-n, (where n, represents the lag-time variable) and Vegetation Indices (VI) such as VCI, and NDVI for the period 2000–2020. Considering the time series from January to December (annual cycle), and the growing season (April to September), thus establishing the periods with the highest degree of autocorrelation in different periods of accumulation and significance (Kogan 1997).
5.3.7.2 Non-Contiguous Drought Area Analysis (NCDA)
After the drought indices computation and their spatial representation verification. Threshold analysis is defined. This threshold allows to define a boundary at which the drought will be defined for its integrated analysis (in space and time) for time series and spatial grid.
That means, the threshold values below the water normal condition −1.0, was established to extract the drought true zones in the period 1981–2020 for SPI-n and SPEI-n and below 0.25 and 30% for NDVI and VCI, respectively, as shown in Fig. 5.6:
where, I indicates normal values of the drought index, II, establishes thresholder the drought categories and III represents the drought areas in a cluster.
5.3.7.3 Contiguous Drought Area Analysis (CDA)
Contiguous Drought Area, is based on spatial clustering of drought adjacent cells. These drought cells are identified at each time step (Corzo 2019). If the drought indicator is lower or equal than the threshold, it is binarized between 0 and 1, where a value of 1 indicates that the cell is in drought. Otherwise, 0 is used, indicating no drought (Diaz et al. 2020b).
Image analysis approach was done using a Graphos Technique. Connected Labeling Component technique that consists of generating subgroups of patterns in an image (clusters) based on its cell value to connect it (Fig. 5.7) (Herrera-Estrada et al. 2017).
5.3.7.4 Spatiotemporal Propagation–Tracking Events
Once the contiguous areas are estimated, the largest cluster is extracted at each time step and geospatial properties such as centroid coordinates (x, y), relative area/perimeter are calculated (Diaz et al. 2020b). Then, for each largest event over time, the centroid obtained is analyzed by calculation of overlapping percentage and Euclidean distance to define the trajectory for each largest drought in time (Diaz et al. 2020a) (Fig. 5.8).
where t represents each time step (2000–2020), Ci represents the clusters, and the black spots CDA units.
5.4 Results and Discussion
5.4.1 Climatological Drought Index
Based on ERA5 data, the standardized SPI-n and SPEI-n indices were calculated for accumulations of (n = 1, 3, 6 months) as shown in Fig. 5.9, shows the drought and non-drought values (red and blue, respectively), and their attenuation when considered as oscillations in the more attenuated time series, thus allowing to identify temporally how an event develops as a function of its magnitude in response to the water deficit.
As its shown in the Fig. 5.9, values for drought index SPI and SPEI below −1.0, reflects a moderate to extreme drought pattern, increasing in frequency over the last decade, with an average of 45–70 periods of drought-associated water deficits (Van Loon 2015; Sutanto et al. 2020; Hydrologic remote sensing: capacity building for sustainability and resilience 2007).
Temporal characteristics of interannual changes may reflect the characteristics of the long-term change in drought. SPI and SPEI variations, in other words, share similar patterns at various time scales, but the lag in the accumulation (lag-time 1, 3, 6) allows the temporal attenuation of the drought magnitude to be appreciated (lower peaks that represents extreme drought magnitude), causing its trend to increase in relative area terms (Entekhabi et al. 2010; Zhang et al. Mar. 2022; Ebrahimi et al. 2010).
On the other hand, the SPI and SPEI monthly variation characteristics clearly reflect the change in the degree of dryness and humidity in each month, especially in the last 20 years (Jiao et al. 2016; Poornima and Pushpalatha 2019; Al-Shujairy et al. 2019). However, when increasing the periods of analysis and looking in contrast at the same accumulations (SPI-6, SPEI-6), drought was more frequent in the months of March to June and October to January (Fig. 5.10), generating extensions in terms of area and deficit more frequent in meteorological drought (SPI) and more prolonged for agricultural drought (SPEI).
5.4.2 Drought Vegetation Monitoring Indexes
NDVI trends in the vegetative cycle is analyzed, considering the phenological growth as illustrated in Fig. 5.11, which allows inferring the vegetative activity as a function of temperature and water stress cycles (Colliander et al. 2017; Hafni et al. 2022). For this purpose, the monthly NDVI series corresponding to the maximum mean NDVI was selected.
For the dry corridor of Central America, a bimodal phenological pattern is observed for the periods January–May and July–October, which are directly related to rainfall patterns (Fig. 5.12). In this way, the vegetation onset depends on the meteorological conditions in the area by year, but not necessarily depends on the vegetation type or crop productivity. (Jong et al. 2011).
However, the derivation of the VCI required a NDVI trend normalization, which is implemented by the LOWESS smoothing technique (locally weighted running line smoother) proposed by (Kogan 1997; Cai et al. 2017).
Which, if NDVI trends (5.6) with noise ε should be adjust to a normal distribution, then each data value (x) is replaced by a combination of adjacent values of the normal series in a window w(x), using a second order polynomial least squares fit (Kogan 1997). Generating a new time series normalized, (Hilda 2017; Prasetyo et al. 2019) allowing a reconstruction of the time series in which the leaf area and photosynthetic activity is easily appreciable (Murakami et al. 2016; Soudani et al. 2012; Gong 2022). This methodology is tested with 3 filters or smoothing window, in which the degree of smoothing is estimated by least squares minimization, thus allowing to reduce by attenuations sudden changes in NDVI as its shown in Fig. 5.13 (Cai et al. 2017).
5.4.3 Spatiotemporal Approach
After obtaining the drought indexes based on hydro climatological data like SPI, SPEI, and remote sensing-based like NDVI and VCI, a correlative analysis was carried out to establish those time accumulations that were related to each other to implement the spatiotemporal analysis. This correlation was generated from the binarization of each index given the threshold set out in the methodology (NCDA).
However, each drought scale proposed, in terms of magnitude varying, their spatial relationship may not fit because each index takes into account different aggregate factors, e.g., VCI standardizes NDVI in phenological response and SPI 3 standardizes precipitation as a function of three-monthly accumulation, which involves highly variable significances (−0.42 to 0.86) as shown in Figs. 5.14 and 5.15.
Therefore, equivalent drought ranges had to be established for their analysis, which implied for each category, moderate, severe, and extreme drought, to find those optimal ranges that had greater significance and representativeness in terms of the area under drought condition (NCDA).
Thus, a relationship range is found between the percentages of the area in representative drought and their spatial autocorrelation (Ghulam et al. 2007; Jiao et al. 2016; Peters et al. 2002). As shown in Table 5.6, the net correlation between climatic and remotely sensed indices varies between (0.39 and 0.89), being the combination SPI-3 (moderate drought)– VCI (extreme) and SPEI 6 (extreme)– VCI (extreme) the most significant one. These results allow validating a priori the viability of NDVI and VCI as indices for spatiotemporal drought monitoring.
Following the maximum significance criterion (Pearson coefficient > 0.6), the non-contiguous drought area percentages (NCDA) are obtained, as shown in Fig. 5.16, which comparatively compares the NCDA results for the SPEI-6-VCI combination, where there is a ratio of 2.4:1 in terms of the magnitude of the area. Locating in a temporal way, months between October to January and May to August with greater areas of drought and annually related to El Niño Southern Oscillation periods in the last two decades, like 2019, 2015, 2014, 2012, 2004, and 2003 (Sheffield et al. 2009).
Binary grids are then generated, and the connected component algorithm is used to find the largest clusters or contiguous drought units (CDA) that are most representative for subsequent monitoring as its shown in Fig. 5.17.
5.4.4 Drought Tracking
Considering, these grids that represent the largest clusters (Fig. 5.18), the characteristics of centroid location and area are extracted to evaluate whether an event t is related to an event t + 1. (Fig. 5.19) where shows the most representative centroids related to drought area extensions. However, as it is a dynamic phenomenon, the drought that can be visualized in a hydro climatological index is not so perceptible based on vegetation, as the phenological growth cycle only indicates for a certain crop or cover which orientation will have the water deficit. Overlap metrics were employed, excluding events that do not exceed at least 30%.
As shown below in Figs. 5.20 and 5.21, the main trajectories reported according to SPI-SPEI and VCI are presented.
For SPI and SPEI, estimated trajectories emphasize that the average extend is between 8 and 13 km (Sánchez Hernández 2022). The longest drought trajectories mainly have origin in Guatemala and El Salvador, where are related to wind patterns, low atmospheric moisture, and high pressure/temperature (Cook et al. 2018; Dominguez and Magaña 2018; Aguilar et al. 2005). Orographically moderate thermal gradient phenomena in the western of the Central America dry corridor countries, allow decreasing the severity of drought and extension (Sánchez Hernández 2022).
However, the trajectories determined for the indices derived from remote information are mainly characterized by being short compared to the previous ones (average distance less than 3 km), which, although they do not allow the evaluation of cluster migration or large drought events over long distances, they do give an overview of the average direction in which they manifest themselves and their orientation allows the adoption of crop protection technologies or agricultural practices.
As can be seen in Fig. 5.21, the orientation is in a northeast position, and when contrasted with Fig. 5.20, there is a high relationship concerning regional hydro climatological aspects.
5.5 Conclusions
Central American dry corridor, drought impacts and water scarcity is increasing. Spatiotemporal analysis and validation of remote sensing information is a game-changer in this type of study requirements, becoming efficiently applicable in sectors with low data availability or early warming tool for regional monitoring (Gong 2022; Jiang et al. 2017; Zhang et al. 2019).
This study evaluated the drought dynamics using a reproducible drought vegetation index VCI using remote sensing data to 2000–2020, allow identifying the stressed vegetation spatial distribution, associated with drought conditions with cutting-edge technologies.
Some preliminary results allow using remote sensing data as NDVI as drought monitoring data source. However, remote sensing data still have some limitations that should be considered in future studies. Radiometric interferences, satellites position can affect the geometry of source, scale, for instance.
Regarding drought areas, the approximation and adaptation of the spatiotemporal analysis methodology allowed us to observe a greater concentration of drought events in regions that are directly affected by global oscillations such as ENSO, hurricanes (geographical limits with Pacific Ocean).
El Salvador, Nicaragua, Costa Rica and Guatemala for example, in the northwest, show the highest density of events per phenological cycle (January–May) and (July–October). Additionally, due to aspects such as resolution and scale of the index, it is established that the most important migrations of water stress are highly related to the mentioned density, which allows the generation of strategies or opportunities for sustainable agriculture and improvement of drainage systems.
Central America dry corridor countries, over 20 years was found that the relationship between the SPI water deficit on short-term scales together with the VCI values increases proportionally, however, SPEI shows a stronger relationship because of the physical comparison of two observable or measurable agricultural conditions (vegetation condition, evapotranspiration) observed for SPEI 6.
For the years 2000, 2004, 2015, and 2019, longer drought durations and extensions were observed, which lead to extreme events and are highly related to the anthropic migrations of land use reported by FAO (2019); To reduce El Niño’s impact on Central America’s Dry Corridor, build resilience and invest in sustainable agriculture 2022; World Food Programme 2002).
5.6 Recommendations
According to the findings of this chapter, it was validated that the VCI has greater potential for agricultural drought monitoring than NDVI, and that NDVI is highly correlated with water deficit SPI in 3 months lags and SPEI in 6 months lags. However, some interferences may be associated with the scale magnitudes of the products, which is suggested to verify in detail which geo-correlation and de-scaling method allows geostatistics to have more consistent products, as well as methodologies such as Whitaker (Jiao et al. 2016) for time series smoothing.
More research is needed in case we want to apply this methodology at the crop level, which can strengthen the analysis by establishing productivity in terms of stress associated with drought, thus generating more detailed results and greater efficiency in terms of drought to the cross-border productive sector.
Finally, it is recommended the use of matching algorithms based on geostatistics to improve the performance of both satellite-based indices and their trajectory tracking, such as GWR and Multivariate Kriging, mainly (Nejadrekabi et al. 2022; Dutra et al. 2021; Baniya et al. 2019).
References
Aguilar E et al (2005) Changes in precipitation and temperature extremes in Central America and northern South America, 1961–2003. J Geophys Res Atmos 110(23):1–15. https://doi.org/10.1029/2005JD006119
Al-Shujairy QAT, Al-Hedny S, Al-Barakat H, Hao Y, Hao Z, Fu Y (2019) Drounght analysis by using standarized precipitation index (SPI) and normalized difference vegetation index (NDVI) at Bekasi Regency in 2018. IOP Conf Ser Earth Environ Sci 280(1):012002. https://doi.org/10.1088/1755-1315/280/1/012002
Anandhi A, Srinivas VV, Nanjundiah RS, Nagesh Kumar D (2008) Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 28(3):401–420. https://doi.org/10.1002/joc.1529
Anderson TG, Anchukaitis KJ, Pons D, Taylor M (2019) Multiscale trends and precipitation extremes in the Central American midsummer drought. Environ Res Lett 14(12):124016. https://doi.org/10.1088/1748-9326/ab5023
Bacanli UG, Firat M, Dikbas F (2009) Adaptive neuro-fuzzy inference system for drought forecasting. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-008-0288-5
Bae S, Lee SH, Yoo SH, Kim T (2018) Analysis of drought intensity and trends using the modified SPEI in South Korea from 1981 to 2010. Water (Switzerland) 10(3). https://doi.org/10.3390/w10030327
Baniya B, Tang Q, Xu X, Haile GG, Chhipi-Shrestha G (2019) Spatial and temporal variation of drought based on satellite derived vegetation condition index in Nepal from 1982–2015. Sensors (Switzerland) 19(2). https://doi.org/10.3390/S19020430
Beck HE et al (2017) Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol Earth Syst Sci 21(12):6201–6217. https://doi.org/10.5194/hess-21-6201-2017
Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural networks and wavelet support vector regression models. J Hydrol. https://doi.org/10.1016/j.jhydrol.2013.10.052
Blain GC (2011) Standardized precipitation index based on Pearson type III distribution. Rev Bras Meteorol 26(2):167–180. https://doi.org/10.1590/s0102-77862011000200001
Buttafuoco G, Caloiero T (2014) Drought events at different timescales in Southern Italy (Calabria). J Maps 10(4):529–537. https://doi.org/10.1080/17445647.2014.891267
Cai W (2014) Increasing frequency of extreme El Niño events due to greenhouse warming. Nat Clim Chang 4(2):111–116. https://doi.org/10.1038/nclimate2100
Cai Z, Jönsson P, Jin H, Eklundh L (2017) Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sens 9(12). https://doi.org/10.3390/RS9121271
Colliander A et al (2017) Validation of SMAP surface soil moisture products with core validation sites. Remote Sens Environ. https://doi.org/10.1016/j.rse.2017.01.021
Cook BI, Mankin JS, Anchukaitis KJ (2018) Climate change and drought: from past to future. Curr Clim Chang Reports 4(2):164–179. https://doi.org/10.1007/S40641-018-0093-2
Corzo G (2019) Framework for spatio-temporal multi-objective optimization of preventive drought management measures. PhD research proposal
de Jong R, de Bruin S, de Wit A, Schaepman ME, Dent DL (2011) Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sens Environ 115(2):692–702. https://doi.org/10.1016/J.RSE.2010.10.011
Depsky N, Pons D (2020) Meteorological droughts are projected to worsen in Central America’s Dry Corridor throughout the 21st century. Environ Res Lett 16(1):014001. https://doi.org/10.1088/1748-9326/ABC5E2
Diaz V, Corzo Perez GA, Van Lanen HAJ, Solomatine D, Varouchakis EA (2020a) Characterisation of the dynamics of past droughts. Sci Total Environ https://doi.org/10.1016/j.scitotenv.2019.134588
Diaz V, Corzo Perez GA, Van Lanen HAJ, Solomatine D, Varouchakis EA (2020b) An approach to characterise spatio-temporal drought dynamics. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2020.103512
Dominguez C, Magaña V (2018) The role of tropical cyclones in precipitation over the tropical and subtropical North America. Front Earth Sci 6. https://doi.org/10.3389/FEART.2018.00019/FULL
Dracup JA, Lee KS, Paulson EG (1980) On the definition of droughts. Water Resour Res. https://doi.org/10.1029/WR016i002p00297
Dutra DJ, Elmiro MAT, Coelho CWGA, Nero MA, Temba PDC (2021) Temporal analysis of drought coverage in a watershed area using remote sensing spectral indexes. Soc Nat 33. https://doi.org/10.14393/SN-V33-2021-59505
Ebrahimi M, Matkan AA, Darvishzadeh R (2010) Remote sensing for drought assessment in Arid regions (A case study of central part of Iran, “Shirkooh-Yazd”)
Eckstein D, Hutfils M-L, Winges M (2017) Germanwatch
Entekhabi D et al (2010) The soil moisture active passive (SMAP) mission. Proc IEEE. https://doi.org/10.1109/JPROC.2010.2043918
Fallah A, Rakhshandehroo GR, Berg POS, Orth R (2020) Evaluation of precipitation datasets against local observations in southwestern Iran. Int J Climatol 40(9):4102–4116. https://doi.org/10.1002/joc.6445
FAO (2019) Global report on food crises. Food Secur Inf Netw
Frieler K et al (2017) Assessing the impacts of 1.5 °C global warming—simulation protocol of the inter-sectoral impact model intercomparison project (ISIMIP2b). Geosci Model Dev 10(12):4321–4345. https://doi.org/10.5194/gmd-10-4321-2017
Ghulam A, Qin Q, Zhan Z (2007) Designing of the perpendicular drought index. Environ Geol 52(6):1045–1052. https://doi.org/10.1007/S00254-006-0544-2
Gong F et al (2022) Partitioning of three phenology rhythms in American tropical and subtropical forests using remotely sensed solar-induced chlorophyll fluorescence and field litterfall observations. Int J Appl Earth Obs Geoinf 107. https://doi.org/10.1016/j.jag.2022.102698
Habibi M, Schöner W, Babaeian I (2020) Drought monitoring using standardized precipitation index (SPI), standardized precipitation-evapotranspiration index ( SPEI ) and normalized-difference snow index ( NDSI ) with observational and ERA5 dataset, within the uremia lake basin, Iran. 11543
Hafni DAF et al (2022) Peat fire risk assessment in Central Kalimantan, Indonesia using the standardized precipitation index (SPI). IOP Conf Ser Earth Environ Sci 959(1). https://doi.org/10.1088/1755-1315/959/1/012058
Herrera-Estrada JE, Satoh Y, Sheffield J (2017) Spatiotemporal dynamics of global drought. Geophys Res Lett. https://doi.org/10.1002/2016GL071768
Hilda F (2017) Drought analysis for mitigating Peatland fires using satellite data based on geographic information systems. JOM FTEKNIK 4(2):1–9
Hydrologic remote sensing: capacity building for sustainability and resilience—Google Libros. https://books.google.com.co/books?id=jyINDgAAQBAJ&pg=PA265&lpg=PA265&dq=(Ghulam+et+al.,+2007).&source=bl&ots=-mrnulrLQ8&sig=ACfU3U3G0Xde_zS-aQuR_YAlO-_2XPg58A&hl=es-419&sa=X&ved=2ahUKEwiGjIDt2ej3AhX0SDABHf-vBAUQ6AF6BAgZEAM#v=onepage&q=(Ghulametal.%2C2007)&f=false. Accessed 18 May 2022
Jiang Y et al (2017) Analysis of relationship between meteorological and agricultural drought using standardized precipitation index and vegetation health index. IOP Conf Ser Earth Environ Sci 54(1):012008. https://doi.org/10.1088/1755-1315/54/1/012008
Jiao W, Zhang L, Chang Q, Fu D, Cen Y, Tong Q (2016) Evaluating an enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the continental united states. Remote Sens 8(3):224. https://doi.org/10.3390/RS8030224
Kogan F (1997) Global drought watch from space. https://web.iitd.ac.in/~sagnik/C2.pdf. Accessed 23 Sep 2021
Maldonado T, Rutgersson A, Alfaro E, Amador J, Claremar B (2016) Interannual variability of the midsummer drought in Central America and the connection with sea surface temperatures. Adv Geosci. https://doi.org/10.5194/adgeo-42-35-2016
Murakami H et al (2016) Seasonal forecasts of major hurricanes and landfalling tropical cyclones using a high-resolution GFDL coupled climate model. J Clim. https://doi.org/10.1175/JCLI-D-16-0233.1
Nejadrekabi M, Eslamian S, Zareian MJ (2022) Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province. Int J Environ Sci Technol. https://doi.org/10.1007/S13762-021-03852-8
Nihoul JCJ (2005) Marine ecosystems and climate variation. J Mar Syst. https://doi.org/10.1016/j.jmarsys.2004.06.004
Okal HA, Ngetich FK, Okeyo JM (2020) Spatio-temporal characterisation of droughts using selected indices in Upper Tana River watershed, Kenya. Sci African 7:e00275. https://doi.org/10.1016/j.sciaf.2020.e00275
Peters A, Walter-Shea E, Ji L, Viña A, Hayes M, Svoboda M (2002) Drought Monitoring with NDVI-Based standardized vegetation index. Undefined
Podestá G, Skansi M, Herrera N, Veiga H (2016) Descripción de índices para el monitoreo de sequía meteorológica implementados por el Centro Regional del Clima para el Sur de América del Sur. Rep Técnico CRC-SAS
Poornima S, Pushpalatha M (2019) Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network. Soft Comput. https://doi.org/10.1007/s00500-019-04120-1
Prasetyo Y, Bashit N, Simarsoit Y (2019) Study of correlation of residential and industrial growth pattern in Semarang city to the aquifer capacity changes in the year 2014–2017. IOP Conf Ser Earth Environ Sci 280(1). https://doi.org/10.1088/1755-1315/280/1/012001
Rokhmatullah, Hernina R, Yandi S (2018) Drounght analysis by using standarized precipitation index (SPI) and normalized difference vegetation index (NDVI) at Bekasi Regency in 2018. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/280/1/012002
Sahaar SA, Niemann JD (2020) Impact of regional characteristics on the estimation of root-zone soil moisture from the evaporative index or evaporative fraction. Agric Water Manag 238. https://doi.org/10.1016/J.AGWAT.2020.106225
Sánchez Hernández KA (2021) Biblioteca Jorge Álvarez Lleras Koha › Detalles de: machine learning methods for characterising and tracking spatiotemporal drought events case study: Central America Dry Corridor . https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=22675. Accessed 18 May 2022
Serda M (2013) Synteza i aktywność biologiczna nowych analogów tiosemikarbazonowych chelatorów żelaza. Uniw śląski 343–354. https://doi.org/10.2/JQUERY.MIN.JS
Sexton JO et al (2013) Global, 30-m resolution continuous fields of tree cover: landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int J Digit Earth. https://doi.org/10.1080/17538947.2013.786146
Sheffield J, Andreadis KM, Wood EF, Lettenmaier DP (2009) Global and continental drought in the second half of the twentieth century: severity-area-duration analysis and temporal variability of large-scale events. J Clim. https://doi.org/10.1175/2008JCLI2722.1
Soudani K et al (2012) Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens Environ 123:234–245. https://doi.org/10.1016/J.RSE.2012.03.012
Sutanto SJ, Wetterhall F, Van Lanen HAJ (2020) Hydrological drought forecasts outperform meteorological drought forecasts. Environ Res Lett 15(8). https://doi.org/10.1088/1748-9326/AB8B13
Tadesse T, Wardlow B, Svoboda MD, Hayes MJ (2012) Vegetation outlook (VegOut): predicting remote sensing–based seasonal greenness. Drought Mitigation Center Faculty Publications [Online]. Available: https://digitalcommons.unl.edu/droughtfacpub/102. Accessed 18 May 2022
To reduce El Niño’s impact on Central America’s Dry Corridor, build resilience and invest in sustainable agriculture. https://www.ifad.org/es/web/latest/-/news/to-reduce-el-nino-s-impact-on-central-america-s-dry-corridor-build-resilience-and-invest-in-sustainable-agriculture. Accessed 18 May 2022
van der Zee Arias A, van der Zee J, Meyrat A, Poveda C, Picado L (2012) Estudio de caracterización del Corredor Seco Centroamericano. p 70 [Online]. Available: https://reliefweb.int/sites/reliefweb.int/files/resources/tomo_i_corredor_seco.pdf
Van Loon AF (2015) Hydrological drought explained. Wiley Interdiscip Rev Water. https://doi.org/10.1002/wat2.1085
World Food Programme (2022) Erratic weather patterns in the Central American Dry Corridor leave 1.4 million people in urgent need of food assistance. https://www.wfp.org/news/erratic-weather-patterns-central-american-dry-corridor-leave-14-million-people-urgent-need. Accessed 18 May 2022
Wilhite D (2006) Drought monitoring and early warning: concepts, progress and future challenges. World Meteorogical Organ
Zhang J et al (2022) NIRv and SIF better estimate phenology than NDVI and EVI: effects of spring and autumn phenology on ecosystem production of planted forests. Agric for Meteorol 315:108819. https://doi.org/10.1016/J.AGRFORMET.2022.108819
Zhang A, Jia G, Wang H (2019) Improving meteorological drought monitoring capability over tropical and subtropical water-limited ecosystems: evaluation and ensemble of the microwave integrated drought index. Environ Res Lett 14(4). https://doi.org/10.1088/1748-9326/AB005E
Zhao M, Heinsch FA, Nemani RR, Running SW (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ. https://doi.org/10.1016/j.rse.2004.12.011
Acknowledgements
We would like to thank the IHE-Delft and Escuela Colombiana de IngenieriaJulio Garavito for funding this research. Also, thanks for the support to the hydro informatics research group, Dr. Gerald Augusto Corzo Perez, especially to reviewers and editors for their valuable comments, which helped to improve the quality of this manuscript.
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Hernández, K.A.S., Perez, G.A.C. (2022). A Comparative Analysis of Spatiotemporal Drought Events from Remote Sensing and Standardized Precipitation Indexes in Central America Dry Corridor. In: Singh, V.P., Yadav, S., Yadav, K.K., Corzo Perez, G.A., Muñoz-Arriola, F., Yadava, R.N. (eds) Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management. Water Science and Technology Library, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-031-14096-9_5
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