Monitoring and assessment of drought through remote sensing and GIS depend on the factors that cause drought and the factors of drought impact.
Based on the causative factors, drought can be classified into Meteorological, Hydrological and Agricultural droughts. An extensive survey of the definition of droughts by WMO found that droughts are classified on the basis of: (i) rainfall, (ii) combinations of rainfall with temperature, humidity and or evaporation, (iii) soil moisture and crop parameter, (iv) climatic indices and estimates of evapotranspiration, and finally (v) the general definitions and statements.
Drought is a normal, recurrent feature of climate and occurs in all climatic zones, although its characteristics vary significantly from one region to another. Drought produces a complex web of impacts that span many sectors of the economy and reach well beyond the area experiencing physical drought. Drought impacts are commonly referred to as direct or indirect. Reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat are a few examples of direct impacts. The consequences of these impacts illustrate indirect impacts. The remote sensing and GIS technology significantly contributes to all the activities of drought management.
Long before the drought event occurs, the preparedness in terms of identifying the drought prone / risk zone area and the prediction of drought and its intensity is essential.
Drought Prone/Risk zone identification
The drought prone area or risk zone identification is usually carried out on the basis of historic data analysis of rainfall or rainfall and evaporation and the area of irrigation support. The conventional methods lack identification of spatial variation and do not cover man's influence such as land use changes like irrigated area developed and the area affected due to water logging and salinity. The remote-sensing based method for identification of drought prone areas (Jeyaseelan et al., 2002) uses historical vegetation index data derived from NOAA satellite series and provides spatial information on drought prone area depending on the trend in vegetation development, frequency of low development and their standard deviations.
The remote sensing use for drought prediction can benefit from climate variability predictions using coupled ocean/atmosphere models, survey of snow packs, persistent anomalous circulation patterns in the ocean and atmosphere, initial soil moisture, assimilation of remotely sensed data into numerical prediction models and amount of water available for irrigation. Nearly-global seasonal climate anomaly predictions are possible due to the successful combination of observational satellite networks for operational meteorological, oceanographic and hydrological observations. Improved coupled models and near-real time evaluation of in situ and remote sensing data - allows for the first time physically-based drought warnings several months in advance, to which a growing number of countries already relate their policies in agriculture, fisheries and distribution of goods.
The quality of seasonal predictions of temperature and precipitation anomalies by various centres such as the National Climate Research Centre
(NCRC) of United States, the European Centre for Medium Range Weather Forecasts (ECMWF), the India Meteorological Department (IMD), the National Centre for Medium Range Weather Forecast of India (NCMRWF) is a function of the quality and amount of satellite data assimilated into the starting fields (e.g., SST from AVHRR and profiles from TOVS on NOAA satellites, ERS-2 scatterometer winds, SSM/I on DMSP satellites and all geostationary weather satellites: Geostationary Operational Environmental Satellites (GOES), i.e. GOES-East, GOES-West of USA, METeorological SATellite (METEOSAT) of Europe, Geostationary Meteorological Satellites (GMS) of Japan, Indian National Satellites (INSAT) of India etc.). The new assimilation techniques have produced a stronger impact of space data on the quality of weather and seasonal climate predictions.
The potential contribution by existing satellites is by far not fully exploited, since neither the synergy gained by the combination of satellite sensors is used nor all the satellite data are distributed internationally. For example, better information flow is needed from satellite data producers to the intermediary services such as CLIPS (Climate Information and Prediction Services) project of World Meteorological Organisation (WMO), and prediction centres including the European Centre for Medium Range Weather Forecasts (ECMWF), National Centres for Environmental Predictions (NCEP), Japan Meteorological Agency (JMA), India Meteorological Department (IMD), National Centre for Medium Range Weather Forecast, India (NCMRWF) etc. to local services and ultimately to end users. Further the drought predictions need to be improved with El Niño predictions and should be brought down to larger scales.
Drought Prevention Phase
Drought monitoring mechanism exists in most of the countries based on ground based information on drought related parameters such as rainfall, weather, crop condition and water availability, etc. Earth observations from satellite are highly complementary to those collected by in-situ systems. Satellites are often necessary for the provision of synoptic, wide-area coverage and frequent information required for spatial monitoring of drought conditions. The present state of remotely sensed data for drought monitoring and early warning is based on rainfall, surface wetness, temperature and vegetation monitoring.
Currently, multi channel and multi sensor data sources from geostationary platforms such as GOES, METEOSAT, INSAT and GMS and polar orbiting satellites such as National Oceanic Atmospheric and Administration (NOAA), EOS-Terra, Defense Meteorological Satellite Program (DMSP) and Indian Remote Sensing Satellites (IRS) have been used or planned to be used for meteorological parameter evaluation, interpretation, validation and integration. These data are used to estimate precipitation intensity, amount, and coverage, and to determine ground effects such as surface (soil) wetness.
Rain is the major causative factor for drought. As the conventional method is based on the point information with limited network of observations, the remote sensing based method provides better spatial estimates. Though the satellite based rainfall estimation procedure is still experimental, the methods can be grouped into 3 types namely Visible and Infrared (VIS and IR) technique, passive microwave technique and active microwave technique.
VIS and IR technique: VIS and IR techniques were the first to be conceived and are rather simple to apply while at the same time they show a relatively low degree of accuracy. A complete overview of the early work and physical premises of VIS and thermal IR (10.5 - 12.5 pm) techniques is provided by Barrett and Martin (1981) and Kidder and Vonder Haar (1995). The Rainfall estimation methods can be divided into the following categories: cloud-indexing, bi-spectral, life history and cloud model. Each of the categories stresses a particular aspect of cloud physics properties using satellite imagery.
Cloud indexing techniques assign a rain rate level to each cloud type identified in the satellite imagery. The simplest and perhaps most widely used is the one developed by Arkin (1979). A family of cloud indexing algorithms was developed at the University of Bristol, originally for polar orbiting NOAA satellites and recently adapted to geostationary satellite imagery. "Rain Days" are identified from the occurrence of IR brightness temperatures (TB) below a threshold.
Bi-spectral methods are based on the very simple, although not always true, relationship between cold and bright clouds and high probability of precipitation, this is characteristic of Cumulonimbus. Lower probabilities are associated with cold but dull clouds (thin cirrus) or bright but warm (stratus) clouds. O'Sullivan et al. (1990) used brightness and textural characteristics during daytime and IR temperature patterns to estimate rainfall over a 10 x 10 pixel array in three categories: no rain, light rain, and moderate/heavy rain. A family of techniques that specifically require geostationary satellite imagery are the life-history methods that rely upon a detailed analysis of the cloud's life cycle, which is particularly relevant for convective clouds. An example is the Griffith-Woodley technique (Griffith et al., 1978). Cloud model techniques aim at introducing the cloud physics into the retrieval process for a quantitative improvement deriving from the overall better physical description of the rain formation processes. Gruber (1973) first introduced a cumulus convection parameterization to relate fractional cloud cover to rain rate. A one-dimensional cloud model relates cloud top temperature to rain rate and rain area in the Convective Stratiform Technique (CST) (Adler and Negri, 1988; Anagnostou et al., 1999).
Passive microwave technique: Clouds are opaque in the VIS and IR spectral range and precipitation is inferred from cloud top structure. At passive MW frequencies, precipitation particles are the main source of attenuation of the upwelling radiation. MW techniques are thus physically more direct than those based on VIS/IR radiation. The emission of radiation from atmospheric particles results in an increase of the signal received by the satellite sensor while at the same time the scattering due to hydrometeors reduce the radiation stream. Type and size of the detected hydrometeors depends upon the frequency of the upwelling radiation. Above 60 GHz ice scattering dominates and the radiometers can only sense ice while rain is not detected. Below about 22 GHz absorption is the primary mechanism affecting the transfer of MW radiation and ice above the rain layer is virtually transparent. Between 19.3 and 85.5 GHz, frequency range radiation interacts with the main types of hydrometeors, water particles or droplets (liquid or frozen). Scattering and emission happen at the same time with radiation undergoing multiple transformations within the cloud column in the sensor's field of view (FOV). The biggest disadvantage is the poor spatial and temporal resolution, the first due to diffraction, which limits the ground resolution for a given satellite MW antenna, and the latter to the fact that MW sensors are consequently only mounted on polar orbiters. The matter is further complicated by the different radiative characteristics of sea and land surfaces underneath. The major instruments used for MW-based rainfall estimations are the SSM/I, a scanning-type instrument that measures MW radiation over a 1400-km wide swath at four separate frequencies, 19.35, 22.235, 37.0 and 85.5 GHz, the latter extending the spectral range of previous instruments into the strong scattering regime (as regards to precipitation-size particles).
Active microwave: The most important precipitation measuring instruments from space is the PR, precipitation radar operating at 13.8 GHz on board TRMM, the first of its kind to be flown on board a spacecraft. The instrument aims at providing the vertical distribution of rainfall for the investigation of its three-dimensional structure, obtaining quantitative measurements over land and oceans, and improving the overall retrieval accuracy by the combined use of the radar, and the TMI and VIRS instruments.
The estimation of water stress in crop/ vegetation or low rate of evapotranspiration from crop is another indicator of drought. As water stress increases the canopy resistance for vapor transport results in canopy temperature rise in order to dissipate the additional sensible heat. Sensible heat transport (ET) between the canopy (Ts) and the air (Ta) is proportional to the temperature difference (Ts-Ta). Therefore the satellite based surface temperature estimation is one of the indicators for drought monitoring since it is related to the energy balance between soil and plants on the one hand and atmosphere and energy balance on the other in which evapotranspiration plays an important role. Surface temperature could be quite complementary to vegetation indices derived from the combination of optical bands. Water-stress, for example, should be noticed first by an increase in the brightness surface temperature and, if it affects the plant canopy, there will be changes in the optical properties.
During the past decade, significant progress has been made in the estimation of land-surface emissivity and temperature from airborne TIR data. Kahle et al. (1980) developed a technique to estimate the surface temperature based on an assumed constant emissivity in one channel and previously determined atmospheric parameters. This temperature was then used to estimate the emissivity in other channels (Kahle, 1986). Other techniques such as thermal log residuals and alpha residuals have been developed to extract emissivity from multi-spectral thermal infrared data (Hook et al., 1992). Based on these techniques and an empirical relationship between the minimum emissivity and the spectral contrast in band emissivities, a Temperature Emissivity Separation (TES) method has been recently developed for one of the ASTER (Advance Space borne Thermal Emission and Reflection Radiometer) products (ATBD-AST-03, 1996).
In addition, three types of methods have been developed to estimate LST from space: the single infrared channel method, the split window method which is used in various multi-channel sea-surface temperature (SST) algorithms, and a new day/night MODIS LST method which is designed to take advantage of the unique capability of the MODIS instrument. The first method requires surface emissivity and an accurate radiative transfer model and atmospheric profiles which must be given by either satellite soundings or conventional radiosonde data. The second method makes corrections for the atmospheric and surface emissivity effects with surface emissivity as an input based on the differential absorption in a split window. The third method uses day/night pairs of TIR data in seven MODIS bands for simultaneously retrieving surface temperatures and band-averaged emissivities without knowing atmospheric temperature and water vapor profiles to high accuracy. This method improves upon the Li and Becker's method (1993), which estimates both land surface emissivity and LST by the use of pairs of day/night co-registered AVHRR images from the concept of the temperature independent spectral index (TISI) in thermal infrared bands and based on assumed knowledge of surface TIR BRDF (Bi-directional Reflectance Distribution Function) and atmospheric profiles.
Because of the difficulties in correcting both atmospheric effects and surface emissivity effects, the development of accurate LST algorithms is not easy. The accuracy of atmospheric corrections is limited by radiative transfer methods and uncertainties in atmospheric molecular (especially, water vapor) absorption coefficients and aerosol absorption/scattering coefficients and uncertainties in atmospheric profiles as inputs to radiative transfer models. Atmospheric transmittance/radiance codes LOWTRAN6 (Kneizys et al., 1983), LOWTRAN7 (Kneizys et al., 1988), MODTRAN (Berk et al,, 1989), and MOSART (Cornette et al., 1994) have been widely used in development of SST and LST algorithms and the relation between NDVI and emissivities are used.
Soil moisture in the root zone is a key parameter for early warning of agricultural drought. The significance of soil moisture is its role in the partitioning of the energy at the ground surface into sensible and latent (evapotranspiration) heat exchange with the atmosphere, and the partitioning of precipitation into infiltration and runoff.
Soil moisture can be estimated from : (i) point measurements, (ii) soil moisture models and (iii) remote sensing. Traditional techniques for soil moisture estimation/ observation are based on point basis, which do not always represent the spatial distribution. The alternative has been to estimate the spatial distribution of soil moisture using a distributed hydrologic model. However, these estimates are generally poor, due to the fact that soil moisture exhibits large spatial and temporal variation as a result of inhomogeneities in soil properties, vegetation and precipitation. Remote sensing can be used to collect spatial data over large areas on routine basis, providing a capability to make frequent and spatially comprehensive measurements of the near surface soil moisture. However, problems with these data include satellite repeat time and depth over which soil moisture estimates are valid, consisting of the top few centimetres at most. These upper few centimetres of the soil is the most exposed to the atmosphere, and their soil moisture varies rapidly in response to rainfall and evaporation. Thus to be useful for hydrologic, climatic and agricultural studies, such observations of surface soil moisture must be related to the complete soil moisture profile in the unsaturated zone. The problem of relating soil moisture content at the surface to that of the profile as a whole has been studied for the past two decades. The results of the study indicated following four approaches : (i) regression, (ii) knowledge based, (iii) inversion and (iv) combinations of remotely sensed data with soil water balance models.
Passive microwave sensing (radiometry) has shown the greatest potential among remote sensing methods for the soil moisture measurement. Measurements at 1 to 3 GHz are directly sensitive to changes in surface soil moisture, are little affected by clouds, and can penetrate moderate amounts of vegetation. They can also sense moisture in the surface layer to depths of 2 to 5 cm (depending on wavelength and soil wetness). With radiometry, the effect of soil moisture on the measured signal dominates over that of surface roughness (whereas the converse is true for radar). Higher frequency Earth-imaging microwave radiometers, including the Scanning Multichannel Microwave Radiometer (lowest frequency 6.6 GHz) launched on the Seasat (1978) and Nimbus-7 (1978-87) satellites, and the Special Sensor Microwave Imager (lowest frequency 19.35 GHz) launched on the DMSP satellite series have been utilized in soil moisture studies with some limited success. The capabilities of these higher frequency instruments are limited to soil moisture measurements over predominantly bare soil and in a very shallow surface layer (<5 cm). At its lowest frequency of 19.35 GHz the SSM/I is highly sensitive to even small amounts of vegetation, which obscures the underlying soil. Large variations in soil moisture (e.g., flood/no-flood) in sparsely vegetated regions and qualitative river flooding indices, are all that have been shown feasible using the SSM/I.
The vegetation condition reflects the overall effect of rainfall, soil moisture, weather and agricultural practices and the satellite based monitoring of vegetation plays an important role in drought monitoring and early warning. Many studies have shown the relationships of red and near-infrared (NIR) reflected energy to the amount of vegetation present on the ground (Colwell, 1974). Reflected red energy decreases with plant development due to chlorophyll absorption in the photosynthetic leaves. Reflected NIR energy, on the other hand, will increase with plant development through scattering processes (reflection and transmission) in healthy, turgid leaves. Unfortunately, because the amount of red and NIR radiation reflected from a plant canopy and reaching a satellite sensor varies with solar irradiance, atmospheric conditions, canopy background, and canopy structure/ and composition, one cannot use a simple measure of reflected energy to quantify plant biophysical parameters nor monitor vegetation on a global, operational basis. This is made difficult due to the intricate radiant transfer processes at both the leaf level (cell constituents, leaf morphology) and canopy level (leaf elements, orientation, non-photosynthetic vegetation (NPV), and background). This problem has been circumvented somewhat by combining two or more bands into an equation or 'vegetation index' (VI). The simple ratio (SR) was the first index to be used (Jordan, 1969), formed by dividing the NIR response by the corresponding 'red' band output. For densely vegetated areas, the amount of red light reflected approaches very small values and this ratio, consequently, increases without bounds. Deering (1978) normalized this ratio from -1 to +1, with the normalized difference vegetation index (NDVI), by taking the ratio between the difference between the NIR and red bands and their sum. Global-based operational applications of the NDVI have utilized digital counts, at-sensor radiances, 'normalized' reflectances (top of the atmosphere), and more recently, partially atmospheric corrected (ozone absorption and molecular scattering) reflectances. Thus, the NDVI has evolved with improvements in measurement inputs. Currently, a partial atmospheric correction for Raleigh scattering and ozone absorption is used operationally for the generation of the Advanced Very High Resolution Radiometer. The NDVI is currently the only operational, global-based vegetation index utilized. This is in part, due to its 'ratioing' properties, which enable the NDVI to cancel out a large proportion of signal variations attributed to calibration, noise, and changing irradiance conditions that accompany changing sun angles, topography, clouds/shadow and atmospheric conditions. Many studies have shown the NDVI to be related to leaf area index (LAI), green biomass, percent green cover, and fraction of absorbed photo synthetically active radiation (fAPAR). Relationships between fAPAR and NDVI have been shown to be near linear in contrast to the non-linearity experienced in LAI - NDVI relationships with saturation problems at LAI values over 2. Other studies have shown the NDVI to be related to carbon-fixation, canopy resistance, and potential evapotranspiration allowing its use as effective tool for drought monitoring.
Remote sensing use for drought impact assessment involves assessment of following themes such as land use, persistence of stressed conditions on an intra-season and inter-season time scale, demographics and infrastructure around the impacted area, intensity and extent, agricultural yield, impact associated with disease, pests, and potable water availability and quality etc. High resolution satellite sensors from LANDSAT, SPOT, IRS, etc. are being used.
Remote sensing use for drought response study involves decision support for water management, crop management and for mitigation and alternative strategies. High resolution satellite sensors from LANDSAT, SPOT, IRS, etc. are being used. In India, for long term drought management, action plan maps are being generated at watershed level for implementation.
The normalised difference vegetation index (NDVI) and temperature condition index (TCI) derived from the satellite data are accepted world-wide for regional monitoring.
The ongoing program on Africa Real-Time Environmental Monitoring using Imaging Satellites (ARTEMIS) is operational at FAO and uses METEOSAT rainfall estimates and AVHRR NDVI values for Africa.
The USDA/NOAA Joint Agricultural Weather Facility (JAWF) uses Global OLR anomaly maps, rainfall map, vegetation and temperature condition maps from GOES, METEOSAT, GMS and NOAA satellites.
Joint Research Centre (JRC) of European Commission (EC) issues periodical bulletin on agricultural conditions under MARS-STAT (Application of Remote sensing to Agricultural statistics) project which uses vegetation index, thermal based evapotranspiration and microwave based indicators. Agricultural Division of Statistics, Canada issues weekly crop condition reports based on NOAA AVHRR based NDVI along with agro meteorological statistics. National Remote Sensing Agency, Department of Space issues biweekly drought bulletin and monthly reports at smaller administrative units for India under National Agricultural Drought Assessment and Monitoring System (NADAMS) which uses NOAA AVHRR and IRS WiFS based NDVI with ground based weather reports. Similar programme is followed in many countries world-wide.
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