Baburao Kamble

Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index

Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper No. 56 can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index. Therefore, the possibility of directly estimating the crop coefficient from satellite reflectance of a crop was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficients procedure using field data obtained during 2007 from representative US cropping systems in the High Plains from AmeriFlux sites. A simple linear regression model ( ) is developed to establish a general relationship between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc) calculated from the flux data measured for different crops and cropping practices using AmeriFlux towers. There was a strong linear correlation between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption.

Operational Remote Sensing of ET and Challenges

Satellite imagery now provides a dependable basis for computational models that determine evapotranspiration (ET) by surface energy balance (EB). These models are now routinely applied as part of water and water resources management operations of state and federal agencies. They are also an integral component of research programs in land and climate processes. The very strong benefit of satellite-based models is the quantification of ET over large areas. This has enabled the estimation of ET from individual fields among populations of fields (Tasumi et al. 2005) and has greatly propelled field specific management of water systems and water rights as well as mitigation efforts under water scarcity. The more dependable and universal satellite-based models employ a surface energy balance (EB) where ET is computed as a residual of surface energy. This determination requires a thermal imager onboard the satellite. Thermal imagers are expensive to construct and more a required for future water resources work. Future moderate resolution satellites similar to Landsat need to be equipped with moderately high resolution thermal imagers to provide greater opportunity to estimate spatial distribution of actual ET in time. Integrated ET is enormously valuable for monitoring effects of water shortage, water transfer, irrigation performance, and even impacts of crop type and variety and irrigation type on ET. Allen (2010b) showed that the current 16-day overpass return time of a single Landsat satellite is often insufficient to produce annual ET products due to impacts of clouds. An analysis of a 25 year record of Landsat imagery in southern Idaho showed the likelihood of producing annual ET products for any given year to increase by a factor of NINE times (from 5% probability to 45% probability) when two Landsat systems were in operation rather than one (Allen 2010b). Satellite-based ET products are now being used in water transfers, to enforce water regulations, to improve development and calibration of ground-water models, where ET is a needed input for estimating recharge, to manage streamflow for endangered species management, to estimate water consumption by invasive riparian and desert species, to estimate ground-water consumption from at-risk aquifers, for quantification of native

Assimilating Remote Sensing-Based ET into SWAP Model for Improved Estimation of Hydrological Predictions

An agro-hydrological simulation model is useful for agriculture monitoring and Remote Sensing provides useful information over large area. Combining both information by data assimilation is used in agro-hydrological modeling and predictions, where multiple remotely sensed data, ground measurement data and model forecast routinely combined in operational mapping procedures. Remote sensing cannot observe input parameters of agro-hydrological models directly. A method to estimate input parameters of such model from Remote Sensing using data assimilation has been proposed by Ines [2002] using the SWAP (Soil, Water, Atmosphere and Plant) model. A Genetic Algorithm (GA) loaded stochastic physically based soil-water-atmosphere-plant model (SWAP) was extended for the discussed problem and used in the study. The objective of this study was to implement a data assimilation scheme to estimate hydrological parameters (e.g soil moisture) of SWAP model. For this study six Landsat TM/ETM satellite images were obtained for part of the Great Plains (Path 29, Row 32) in the states of Nebraska (NE) for the 2006 growing season (May-October). Then a land surface energy balance model (METRIC) was used to map spatiotemporal distribution of evapotranspiration. The ability of METRIC accuracy was compared with the measurements at several flux sites with Bowen Ratio Energy Balance System units. Remotely sensed ET data and ground measurement data from experiment fields were then combined in a data assimilation to estimate parameters of the SWAP model. The system is initialized with a population of random solutions and searches for optima by updating generations. The result shows that the reasonable parameters (sowing date and harvesting date, Ground water level) were successfully estimated. On the basis of estimated parameters, soil moisture is predicted by SWAP model. The agro-hydrological model driven by the observed ET produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating ET measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating regional ET estimated from satellite-based measurements.

Remotely Sensed Evapotranspiration Data Assimilation for Crop Growth Modeling

The agricultural sector will require more water in the near future to provide more food, fibre and fuels (Molden et al., 2007). As population increases and development calls for increased demand of food, a change in diet due to increased prosperity, and a recent focus on biofuels. This population growth coupled with industrialization and urbanization will result in an increasing demand for water and will have serious consequences on the conservation of water resources. Therefore, a rational approach to best water management practices is needed to balance water supply and demand. One approach to check if the supply is adequate to meet the demand is to account for the respective components in the water balance. Doing so provides an opportunity to search for possible ways to save water from one application and allocate it to another. Simulation models are strong in this regard; they can simulate the processes in the real system and predict the state variables at every stage in the simulation. The role of simulation models in understanding the processes in the soil-plant-atmosphere system has increased significantly in recent years. This is attributed to increased computing capabilities available today (Ines et al., 2002). Such mechanistic ecophysiological models integrate knowledge from data collection by various methods (e.g. GPS, field sampling, satellite remote sensing, radar etc.) and laboratory research. Simulations from such models are widely used to predict and simulate crop growth, yield, water requirements and greenhouse gas emissions. For monitoring agricultural crop production, growth of crops is modeled, for example, by using simulation models. Estimates of crop growth often are inaccurate for practical field conditions. Therefore, model simulations must be improved by incorporating information on the actual growth and development of field crops, for example, by using remote sensing data. Numerous researchers have also used remotely sensed data in conjunction with crop growth models via data assimilation for the purpose of improving soil moisture estimation (Entekhabi et al., 1994; Van Dams et al., 1997; Reichle et al. 2001; Ines et al., 2002; Kamble et al., 2008). The objective of data assimilation is to obtain the best estimate of the state of the system by combining observations with the forecast model’s first guess. Genetic algorithms (GA) are designed to search, discover and emphasize good solutions by applying selection and crossover techniques, inspired by nature, to supply solutions (Holland, 1975; Goldberg, 1989). GA operates on pieces of information like nature does on genes in the course of evolution. Changes in the genes of individuals from a given population allow selection of

Satellite-Based Energy Balance Approach to Assess Riparian Water Use

Previous studies across the High Plains and the Arid West of the United States have pro‐ duced widely varying impacts of riparian evapotranspiration (ET) on surface and ground water. Many producers as well as various state agencies have advocated removing all trees along the river basins as a method of riparian control for water reclamation. Although eradi‐ cation of trees might be an effective method for water reclamation in the short-term, it has not been yet proven whether such water savings are possible on a stream level. Mean water use of riparian trees has been reported in relatively few studies, and most of the previous studies have been of short duration. The water use for saltcedar (Tamarix spp.) was estimat‐ ed at 15.9 L d-1 for 10 cm2 sap wood area (swa) (Smith et al. 1998), 56.8 L d-1 for 33 cm2 swa (Nagler et al. 2003), and 29.9 L d-1 for 100 cm2 swa (Owens and Moore, 2007). The water use for Fremont cottonwood (Populus fremontii S. Wats.) varied from 57.6 L d-1 for 33 cm2 swa (Nagler et al. 2003) to as high as 499.7 L d-1 for 833 cm2 swa (Schaeffer et al. 2000). Riparian plant communities are complex ecosystems that, through an intimate relationship with the fluvial dynamics of river systems, are as much described by their continual cycle of disturb‐ ance and succession as by the vegetation that makes up their multi-storied habitats. Current‐ ly, there is uncertainty in the water use of riparian systems due to the narrow and sparse vegetation commonly associated with them. Local, state and federal water management reg‐ ulatory agencies need good quality water use estimates on unmanaged riparian systems. High frequency micrometeorological flux measurements such as Eddy Correlation System (ECS) have been used to estimate water use by balancing fluxes of sensible and latent heat with total energy incident on a riparian area. However, the technique is most effective when