Pietro Ceccato

Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain.

This paper outlines the first part of a series of research studies to investigate the potential and approaches for using optical remote sensing to assess vegetation water content. It first analyzes why most methods used as approximations of vegetation water content (such as vegetation stress indices, estimation of degree of curing and chlorophyll content) are not suitable for retrieving water content at leaf level. It then documents the physical basis supporting the use of remote sensing to directly detect vegetation water content in terms of Equivalent Water Thickness (EWT) at leaf level. Using laboratory measurements, the radiative transfer model PROSPECT and a sensitivity analysis, it shows that shortwave infrared (SWIR) is sensitive to EWT but cannot be used alone to retrieve EWT because two other leaf parameters (internal structure and dry matter) also influence leaf reflectance in the SWIR. A combination of SWIR and NIR (only influenced by these two parameters) is necessary to retrieve EWT at leaf level. These results set the basis towards establishing operational techniques for the retrieval of EWT at top-of-canopy and top-of-atmospheric levels.

Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach

This paper describes the methodology used to create a spectral index to retrieve vegetation water content from remotely sensed data in the solar spectrum domain. A global sensitivity analysis (GSA) using radiative transfer models is used to understand and quantify vegetation water content effects on the signal measured at three levels: leaf, canopy, and atmosphere. An index is then created that optimises retrieval of vegetation water content (in terms of water quantity per unit area at canopy level) and minimises perturbing effects of geophysical and atmospheric effects. The new index, optimised for the new SPOT-VEGETATION sensor, is presented as an example. Limitations and robustness of the index are also discussed.

Validation of satellite rainfall products over East Africa's complex topography

An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA‐CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM‐3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3‐hourly to 10‐daily) resolution. These included the NOAA‐CPC African rainfall estimation algorithm, GPCP one‐degree‐daily (1DD), TRMM‐3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10‐day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM‐3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM‐3B42 from the second group performed reasonably well.

Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data. Part 2. Validation and Applications.

The Global Vegetation Moisture Index (GVMI) was developed to retrieve vegetation water content from local to global scale rapidly and reliably using SPOT-VEGETATION data. This paper validates the GVMI with field measurements of vegetation water content measured over four different ecosystems in Senegal. Two of the sites show exact concordance between GVMI-derived and field-measured water content. The remaining two sites show differences in value but provide identical evolution over time. Comparison between ecosystems illustrates that GVMI-derived water content is consistent with field measurements of water content expressed as a quantity of water per unit area. Additional study shows that GVMI is not related to the vegetation moisture content expressed as a percentage of water per quantity of biomass. Comparison between the GVMI and NDVI methods also illustrates that the NDVI provides different information (vegetation greenness), which is not directly related to the quantity of water in the vegetation. Potential applications of the new GVMI are also discussed.

Validation of high-resolution satellite rainfall products over complex terrain

High‐resolution satellite rainfall products, at daily accumulation and 0.25° spatial resolution, are evaluated using station networks located over two different parts of Africa. The first site is located over Ethiopia with a very complex terrain. The second site, located over Zimbabwe, has a less rugged topography. The evaluated satellite rainfall products are the NOAA‐CPC African rainfall estimation algorithm (RFE), TRMM‐3B42, the CPC morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratorys blended product. These products perform reasonably well over both regions in detecting the occurrence of rainfall. However, performances are poor in estimating the amount of rainfall in each pixel. The correlation coefficients are low and random errors high. The performance was better over Zimbabwe as compared with Ethiopia. Comparing the different products, CMORPH and TRMM‐3B42 showed a better performance over Ethiopia, while RFE, CMORPH, and TRMM‐3B42 preformed relatively better over Zimbabwe.

A contextual algorithm for AVHRR fire detection

Abstract A contextual algorithm for fire detection with NOAA-AVHRR-LAC data was developed. Unlike ‘traditional’ fire detection algorithms (e.g., multichannel thresholds), the decision to record a fire is made by comparing a fire pixel with the pixels in its immediate neighbourhood. The algorithm is self-adaptive and therefore very consistent over large areas as well as through seasons. The algorithm appears to operate successfully in most areas of the world. This Letter presents the contextual approach and describes the algorithm.

A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa.

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.

An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria Early Warning Systems are advocated as a means of improving the opportunity for preparedness and timely response.Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization.The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHOs Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.

Individual, household and environmental risk factors for malaria infection in Amhara, Oromia and SNNP regions of Ethiopia

We assessed malaria infection in relation to age, altitude, rainfall, socio-economic factors and coverage of control measures in a representative sample of 11437 people in Amhara, Oromia and SNNP regions of Ethiopia in December 2006-January 2007. Surveys were conducted in 224 randomly selected clusters of 25 households (overall sample of 27884 people in 5708 households). In 11538 blood slides examined from alternate households (83% of those eligible), malaria prevalence in people of all ages was 4.1% (95% CI 3.4-4.9), with 56.5% of infections being Plasmodium falciparum. At least one mosquito net or one long-lasting insecticidal net (LLIN) was present in 37.0% (95% CI 31.1-43.3) and 19.6% (95% CI 15.5-24.5) of households, respectively. In multivariate analysis (n=11437; 82% of those eligible), significant protective factors were: number of LLINs per household (odds ratio [OR] (per additional net)=0.60; 95% CI 0.40-0.89), living at higher altitude (OR (per 100 m)=0.95; 95% CI 0.90-1.00) and household wealth (OR (per unit increase in asset index)=0.79; 95% CI 0.66-0.94). Malaria prevalence was positively associated with peak monthly rainfall in the year before the survey (OR (per additional 10 mm rain)=1.10; 95% CI 1.03-1.18). People living above 2000 m and people of all ages are still at significant risk of malaria infection.

Validation and Intercomparison of Satellite Rainfall Estimates over Colombia

Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation m...