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The primary objective of this study was to assess the water productivity (WP) of the annual (wheat, barley, and corn) and biennial (alfalfa and Rhodes grass) crops cultivated under centerpivot irrigation located over desert areas of the Al-Kharj region in Saudi Arabia. The Surface Energy Balance Algorithm for Land (SEBAL) was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images to obtain evapotranspiration (ET) for assessing WP and irrigation performance (IP) of crops. Crop productivity (CP) was estimated using Normalized Difference Vegetation Index (NDVI) crop productivity models. The predicted CP (CPP) for corn varied from 12 690 to 14 060 kg/ha and from 6000 to 7370 kg/ha for wheat. The for alfalfa and Rhodes grass was 42 450 and 58 210 (kg/ha/year), respectively. The highest predicted WP was observed in wheat (0.80-2.01 kg/m3) and the lowest was in alfalfa (0.38-0.46 kg/m3). The deviation between SEBAL predicted ET (ETP) and weather station recorded ET (ETW) was 10%. The performance of the prediction models was assessed against the measured data. The overall mean bias/error of the predictions of CP, ET, and WP was 9.4%, , and 9.65%, respectively; the root mean square error (RMSE) was 1996 (kg/ha), 2107 (m3/ha), and 0.09 (kg/m3) for CP, ET, and WP, respectively. When CP was converted into variations between the actual and predicted, the variations were 8% to 12% for wheat, 14% to 20% for corn, 17% to 35% for alfalfa, 3% to 38% for Rhodes grass, and 4% for barley.

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution.

The goal of this research was to compare the remote-sensing derived irrigated areas with census-derived statistics reported in the national system. India, which has nearly 30% of global annualized irrigated areas (AIAs), and is the leading irrigated area country in the World, along with China, was chosen for the study. Irrigated areas were derived for nominal year 2000 using time-series remote sensing at two spatial resolutions: (a) 10-km Advanced Very High Resolution Radiometer (AVHRR) and (b) 500-m Moderate Resolution Imaging Spectroradiometer (MODIS). These areas were compared with the Indian National Statistical Data on irrigated areas reported by the: (a) Directorate of Economics and Statistics (DES) of the Ministry of Agriculture (MOA), and (b) Ministry of Water Resources (MoWR). A state-by-state comparison of remote sensing derived irrigated areas when compared with MoWR derived irrigation potential utilized (IPU), an equivalent of AIA, provided a high degree of correlation with R2 values of: (a) 0.79 with 10-km, and (b) 0.85 with MODIS 500-m. However, the remote sensing derived irrigated area estimates for India were consistently higher than the irrigated areas reported by the national statistics. The remote sensing derived total area available for irrigation (TAAI), which does not consider intensity of irrigation, was 101 million hectares (Mha) using 10-km and 113 Mha using 500-m. The AIAs, which considers intensity of irrigation, was 132 Mha using 10-km and 146 Mha using 500-m. In contrast the IPU, an equivalent of AIAs, as reported by MoWR was 83 Mha. There are “large variations” in irrigated area statistics reported, even between two ministries (e.g., Directorate of Statistics of Ministry of Agriculture and Ministry of Water Resources) of the same national system. The causes include: (a) reluctance on part of the states to furnish irrigated area data in view of their vested interests in sharing of water, and (b) reporting of large volumes of data with inadequate statistical analysis. Overall, the factors that influenced uncertainty in irrigated areas in remote sensing and national statistics were: (a) inadequate accounting of irrigated areas, especially minor irrigation from groundwater, in the national statistics, (b) definition issues involved in mapping using remote sensing as well as national statistics, (c) difficulties in arriving at precise estimates of irrigated area fractions (IAFs) using remote sensing, and (d) imagery resolution in remote sensing. The study clearly established the existing uncertainties in irrigated area estimates and indicates that both remote sensing and national statistical approaches require further refinement. The need for accurate estimates of irrigated areas are crucial for water use assessments and food security studies and requires high emphasis.

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop†(increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involvingcrop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fractionby reference ET. The ETfraction was determined using Landsat thermal imagery by selecting the “hot†pixels (zero ET) and “cold†pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.