This paper describes a synergetic use of satellite radar images and ancillary information to detect flooded areas at their peak and evaluates its potential with mapping. The procedure was tested on the catastrophic flood that occurred in Regione Piemonte in Italy in November 1994. Two ERS-1 synthetic aperture radar (SAR) images were processed, one acquired one month before the flood and the other acquired three days after the event. Visual interpretation and two different thresholding techniques were performed. The flood map derived shows only a small fraction (20%) of the actually flooded lands because of the time delay between the flood peak and the satellite overpass. To overcome this limitation, the authors developed a new procedure to estimate the flooded area at the peak time by integrating the flooded area from SAR imagery with digital topographic data from a GIS technique. This method allowed inundated areas covering 96.7% of the flooded area officially recorded by the local government to be mapped. The proposed procedure is suitable for mapping flooded areas even when satellite data are acquired some days after the event, thus overcoming the constraint of temporal resolution in the application of SAR imagery in hydrology.

Precise phenological calendars, for each cultivated species and variety, are necessary both to highlight anomalous agronomic situations and to feed crop models. This study, conducted in the Italian rice area, focuses on the evaluation of the contribution of remote sensing satellite data to providing phenological information on rice cropping systems. A time series of 5 years (2001–2005) of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites was analysed with the TIMESAT program in order to automatically retrieve key phenological information such as the start of season (emergence), peak (heading) and end of season (maturity). The procedure involved two steps: (1) interpolation and smoothing of MODIS NDVI temporal profile and (2) the analysis of a temporal signal for the extraction of the phenological metrics. The remote sensing estimates were evaluated using information regarding cultivated variety, sowing dates, management and production directly acquired from rice farmers. A good correlation (R ² = 0.92, n = 24) has been observed between estimates derived from satellites and estimates produced with the traditional Growing Degree Days (GDD) method based on thermal unit accumulation. Improved estimates of the maturity stage were obtained using a procedure that integrates satellite and GDD methods; however its application requires spatially distributed information on the cultivated varieties. Satellite derived maps of the retrieved phenological parameters showed an intra-seasonal pattern related to different cultivated varieties. Inter-seasonal analysis allowed the anomalous behaviour of the year 2003 to be highlighted, characterized by rapid growth at the beginning of the spring and an early senescence. The results confirm the potential of remotely sensed data for the monitoring of crop status and for the forcing of crop models in a spatially distributed way.

Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is most appropriate for surface water detection or which thresholds are most robust to separate water from other surfaces in operational contexts. To address this, we conducted analyses on satellite and field spectral data from an agronomic experiment as well as on real farming situations with different soil and plant conditions. Firstly, we review and select NDSIs proposed in the literature, including a new combination of visible and shortwave infrared bands. Secondly, we analyse spectroradiometric field data and satellite data to evaluate mixed pixel effects. Thirdly, we analyse MODIS data and Landsat data at four sites in Europe and Asia to assess NDSI performance in real-world conditions. Finally, we test the performance of the NDSIs on MODIS temporal profiles in the four sites. We also compared the NDSIs against a combined index previously used for agronomic flood detection. Analyses suggest that NDSIs using MODIS bands 4 and 7, 1 and 7, 4 and 6 or 1 and 6 perform best. A common threshold for each NDSI across all sites was more appropriate than locally adaptive thresholds. In general, NDSIs that use band 7 have a negligible increase in Commission Error over those that use band 6 but are more sensitive to water presence in mixed land cover conditions typical of moderate spatial resolution analyses. The best performing NDSI is comparable to the combined index but with less variability in performance across sites, suggesting a more succinct and robust flood detection method.

The scientific community interested in atmospheric chemistry, gas emissions from vegetation fires, and carbon cycling is currently demanding information on the extent and timing of biomass burning at the global scale. In fact, the area and type of vegetation that is burned on a monthly or annual basis are two of the parameters that provide the greatest uncertainty in the calculation of gas and aerosol emissions and burned biomass. To address this need, an inventory of burned areas at monthly time periods for the year 2000 at a resolution of 1 km²has been produced using satellite data and has been made freely available to the scientific community. In this paper, estimates of burned area and number of burn scars for four broad vegetation classes and reported at the country level for the year 2000 are presented using data taken from the inventory. Over 3.5 million km²of burned areas were detected in the year 2000, of which approximately 80% occurred in areas described as woodlands and shrublands. Approximately 17% of the burned area occurred in grasslands and croplands, the remaining 3% occurred in forests. Almost 600,000 separate burn scars were detected. Descriptions of vegetation burning activity are given for ten regions. Finally, monthly burned area estimates are presented for the Central African Republic to illustrate the usefulness of these data for understanding, monitoring and managing vegetation burning activities.