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Topography plays an important role in the distribution and flux of water and energy within the natural landscape. Surface runoff, evaporation and infiltration are hydrologic processes that take place at the ground-atmosphere interface. Quantitative assessment of these processes depends on topographic configuration of the landscape, which is one of several controlling boundary conditions. Wetness index WI provides a description of the spatial distribution of the soil moisture using topographic information.

WI is computed as,.

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As specific drainage area increases and gradient decreases, WI and soil moisture content increase. Wetness index takes into account both a local slope geometry and site location in the landscape, combining data on gradient and specific drainage area. This can lead to higher correlations of soil moisture with WI , hence evapotranspiration, than with specific drainage area and gradient.

Wetness index controls flow accumulation, soil moisture, distribution of saturation zones, depth of water table, evapotranspiration, thickness of soil horizons, organic matter, pH, silt and sand content, plant cover distribution Kulagina et al. A study to understand the relationship between latent heat flux and microtopography 1m resolution of a wheat field was studied using remote sensing-based latent heat flux. The latent heat flux estimation technique is based on the surface energy balance approach using remotely-sensed data Bastiaanssen, The detailed procedure on estimating grid-based latent heat flux for the study area is shown in Melesse and Nangia and Oberg and Melesse The relationship between WI Figure 8 and latent heat flux for a wheat field is strong, since the topography influences the flux and distribution of water.

Grids with higher values of WI are areas receiving most of the flow higher flow accumulation and lower gradient. These areas have higher soil moisture, hence higher rate of evaporation, than areas with lower values of WI. It is also indicated that when water is a limiting factor, plants on higher WI areas grow well with good canopy cover than plants in other zones of the field. This increases the transpiration vegetation latent heat of the crops. Figure 9 shows the scattergram of WI vs. The latent heat seems to increase at higher rate at lower values of WI than at higher values of WI.

This is attributed to the limited available water for evaporation proportional to WI. With the advent of grid-based remotely-sensed rainfall data, the application of crop water balance models for crop monitoring and yield forecasting has gained increased acceptance by various international, national and local organizations around the world. Soil water is a key state variable in hydrological modeling and determines the partitioning of rainfall into runoff and deep percolation, and also controls the rate of evapotranspiration ET.

Although the estimation of actual evapotranspiration ET a is the ultimate goal of many researchers for hydrological and agronomical applications, it is often difficult to quantify and requires expensive instrumentation. However, hydrological modeling techniques are used to estimate ET a. The two basic modeling techniques to estimate ET a are based on either energy balance e.

For monitoring large areas using remotely sensed data, the water balance approach provides an operational advantage in terms of data availability. While the energy balance models are mainly driven by the thermal data, the water balance models are driven by rainfall. Naturally, cloud cover is an issue to provide daily estimates of ETa on rain-fed agriculture from the energy balance models. On the other hand, availability of satellite-derived rainfall data at various temporal and spatial scales makes operational estimation of ET a using a water balance model a relatively easy task for various decision makers in agriculture and natural resources.

The most widely used water balance technique for operational use is the FAO water balance algorithm that produces the crop water requirement satisfaction index WRSI , which is also known as the crop specific drought index CSDI. A value of indicates all the crop demand has been met while values less than 50 generally indicates a severe water shortage that could lead to complete failure of the crop Smith Values between 50 and will indicate different degrees of crop stress and yield reductions from shortage of adequate supply. FAO studies Doorenbos and Pruitt have shown that WRSI can be related to crop production using a linear yield-reduction function specific to a crop.

Meyer et al. Furthermore, Senay and Verdin enhanced the geospatial model by introducing the concept of maximum allowable depletion MAD and soil water stress factor from irrigation engineering for better estimation of ETa as a function of soil water content. The seasonal crop water requirement satisfaction index for a crop is based on the water supply and demand that a crop experiences during a growing season. ETc is calculated from the product of the Penman-Monteith reference evapotranspiration ET o using the standardized FAO equation that uses short grass as the reference crop Allen et al.

Crop coefficients K c , piecewise linear weighting functions, have traditionally been used to adjust for type and growth stage of the crop:.

The key difference between ET c and ET a is that ET a depends on soil moisture that is calculated on a daily basis to provide the daily soil stress correction factor, Ks Senay and Verdin Whenever the soil water content is above the maximum allowable depletion MAD level, which varies by vegetation type, the ET a will balance the ET c resulting in no net water stress. However, when the available soil water falls below the MAD level, the ET a will be lower than ET c , in proportion to the remaining soil water content.

Runoff and deep drainage out of the root zone are assumed to occur in excess of field capacity. The soil water content is obtained through a simple mass balance equation where the level of soil water is monitored in a root-zone soil layer defined by the water holding capacity WHC of the soil and the crop root depth, i. The key input data to the water balance model are precipitation, potential evapotranspiration PET , and soil water holding capacity, and crop coefficient. While the key crop coefficient values are obtained from the FAO publication Allen, , the other three main datasets are spatially distributed are described in brief below.

Precipitation is the single most important input of the model. NASA generates daily rainfall data at 25 km resolution from the Tropical Rainfall Mapping Mission TRMM; satellite systems that covers the globe between 60 degrees latitude north and south of the equator.


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Another important model parameter for the crop water balance model is the soil water holding capacity. Daily and dekadal day model outputs from the crop water balance are posted in a website Africa, Central America, Afghanistan.

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Similar products are available for western and eastern Africa, central America, Haiti and Dominican Republic and Afghanistan. Spatial distribution of maize crop water requirement satisfaction index WRSI for southern Africa as of the 2 nd dekad of March Normally, the region's crop growing season spans from September through April.

The exact growing season depends on the geographic location. For example, for much of Zimbabwe, the growing season is from November to April. Onset of rains map is a surrogate for the start of season SOS of the crop growing period.

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The SOS is defined with a simple rainfall accounting criteria: a total of 25 mm rainfall received in one dekad followed by a total of 20 mm in the following two consecutive dekads. The SOS map by itself provides critical information on the performance of the season, especially if there is a significant delay in its establishment. In addition, the SOS map is used to initialize the crop water balance model, i. The end of the growing season EOS is dependent on the location and it varies from a minimum of 9 dekads 3 months in arid and semi-arid regions to 18 dekads 6 months in mountainous and wetter regions.

Figure 11 shows the soil water index SWI which shows the level of soil water in the root zone as defined by the soil water holding capacity WHC of the top 1 m of soil. The SWI image shows 4 broad classes for qualitative interpretations.

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This is generally a trigger level for drought early warning. The image is interpreted along with a weekly forecast rainfall. If the 7-day forecast rainfall is not promising, the areas with lowest SWI category are expected to go into the crop wilting phase. This is considered critical and becomes a potential candidate for highlighting it as a drought polygon if the data is corroborated with field information. In this regard, a large area of southern Africa falls in this region by the 2 nd dekad of March, because of a dry spell in February and March.

Figure 12 shows the spatial distribution of maize crop water requirement satisfaction index WRSI for southern Africa as of the 2 nd dekad of March The extended WRSI is composed of two data sources: 1 observed demand and supply from the SOS till the 2 nd dekad March, and 2 extended demand and supply from the 2 nd dekad of March till the end of the growing season. The extended demand and supply is based on climatological rainfall and potential evapotranspiration data.

The WRSI values are expressed as index from 0 to Regions showing WRSI values between the 50 and 95 are at different stages of yield reduction due to water shortage.

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The exact yield reduction is determined by the prevailing management practices in the region. Thus, using historical data from an administrative district, it is possible to formulate a mathematical relationship between WRSI and yield, which would allow the possibility of using WRSI to forecast yield beginning the mid crop growing season. Although the product assumes a predominant crop type in the region, the results are also indicative of other cereal crops growing in the same region and season. This is another way of looking at the model output to cancel out some of the potential wrong model assumptions such as the assumed length of the growing season.

This relative description is corroborated by field reports in that there was a wide-spread yield reduction in much of the southern regions and an improved crop performance in Tanzania.

The advances made in spaceborne remote sensing in the last 50 years, from sputnik 1 to Worldview-1, has been phenomenal. The present trends point to increasing several innovations. First, availability of data from multiple sensors with wide array of spatial, spectral, and radiometric characteristics. These data will be available from multiple sources. Second, significant advances have been made in harmonizing and synthesizing data from multiple sources that facilitates the use of data from these sensors of widely differing characteristics and sources.

We also expect vendors to market data from multiple sources by harmonizing and by adding value. Third, availability of data from a constellation such as from Rapideye at very high resolution of 6. This will certainly require innovations in data handling, storage, and backup.

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But for applications, a combination of very high spatial resolution and frequent coverage is very attractive. Fifth, for many environmental and natural resource applications global wall-to-wall coverage is essential and here satellites like Landsat will continue to play most important role. Sixth, data availability in hyperspectral and hyperspatial sensors brings in new challenges in data mining, processing, backup, and retrieval. Seventh, the advances made in data synthesis, presentation, and accessibility through systems such as Google Earth will bring in new users and multiply applications of remote sensing in environmental sciences and natural resources management.

The authors expect that the future needs of the spatial data will be met overwhelmingly by spaceborne remote sensing. National Center for Biotechnology Information , U. Journal List Sensors Basel v.