Water and heat regimes modelling for a vast territory using remote-sensing data

Methods have been developed to assess water and heat regime characteristics of a vast agricultural region for vegetation season based on the model of land-surface–atmosphere interaction. The model is adjusted to assimilate estimates of the land-surface and meteorological characteristics derived from Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration (AVHRR/NOAA), Moderate Resolution Imaging Spectroradiometer/Earth Observing System Terra & Aqua (MODIS/EOS Terra and Aqua), and Scanning Enhanced Visible and Infrared Imager/Meteosat-9 (SEVIRI/Meteosat-9) data. The case study has been carried out for the agricultural Central Black Earth region of European Russia of 227,300 km² for the 2009–2012 vegetation seasons. The methods of satellite data processing have been developed or refined, which provide the retrieval of vegetation characteristics, land-surface temperature, and precipitation. The techniques for the assimilation of satellite-based products in the model have been developed. Some major water regime characteristics have been generated such as soil water content, evapotranspiration, and others.     

Landsat TM热红外遥感数据定量反演地下水富集带的温度信息--以甘肃河西地区石羊河流域为例

利用ＬａｎｄｓａｔＴＭ６热红外遥感数据定量反演了干旱地区的地表温度,研究结果表明,区典型地表覆盖类型的地表亮温比地表起初温度低０．４－１Ｋ,遥感反演的地面真实温度与当地３月下旬的实测温度误差在０．８Ｋ以下,这说明用ＬａｎｄｓａｔＴＭ６定量反演干旱区的地表温度是可行的。研究结果表明,地下水富集带地表温度具有异常现象,其地表温度比地表水体高５Ｋ左右,而比其它地表类型低７Ｋ以上,据此,可以利用热红外遥感     

Rainfall and vegetation monitoring in the Savanna Zone of the Democratic Republic of Sudan using the NOAA Advanced Very High Resolution Radiometer

NOAA-6 and NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (4?km ground resolution) data were obtained at three-day intervals throughout each of the four-month periods covering the 1980, 1983 and 1984 growing seasons, between latitudes 10° and 22° North in the Democratic Republic of Sudan. Daily rainfall data for twelve meteorological stations spanning the Savanna Zone were analysed. Rainfall in Sudan during 1980 was below normal, but in 1983 and 1984 there were moderate and severe droughts. The satellite data were used to calculate normalized difference vegetation index (NDVI) values from the visible and near-infrared bands of the satellite data. These were processed into ten-day composite data sets using the AVHRR thermal-infrared channel as a cloud screen and a temporal compositing procedure that reduces cloud contamination and selects viewing angles closest to nadir.

The ten-day composite NDVI values and the integrals of NDVI for each growing season were found to be closely correlated with rainfall. The constants of regressions between NDVI and rainfall were lower in 1983 and 1984 than in 1980, which suggests there was reduced water-use efficiency by the rangeland vegetation in drought years. It was found that July and August NDVI values were closely related to the integrated NDVI values; hence early- and mid-season NDVI data could be used to predict annual primary production. Images showing the geographical distribution of values of NDVI prepared for the three years clearly illustrate the effects of the 1983 and 1984 droughts, compared with the higher rainfall of 1980. The precision of the relationship between rainfall and the vegetation indices for the meteorological stations encourages the view that NOAA AVHRR GAC composite NDVI values can be used to monitor effective rainfall in the Savanna Zone of the Democratic Republic of Sudan     

Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data

National Oceanic and Atmospheric Administration (NOAA) satellite data from the Advanced Very High Resolution Radiometer (AVHRR) sensor were analysed to document the vegetation biomass dynamics associated with the regional desert-locust upsurge in West Africa during 1980/81, which affected an area of some 600 000 km² in Mali, Niger and Algeria. Comparisons were made among locust population survey reports, rainfall records from eighteen stations in the same area, and the satellite data in vegetation index format. The satellite-recorded temporal and spatial distributions of desert vegetation biomass were closely correlated with both the locust population surveys and the available rainfall data. An attempt was made to develop a quantitative relationship between a satellite-derived potential breeding activity factor (PBAF) and the observed desert locust populations. Analysis of the multitemporal satellite data set indicates that, had the NOAA/AVHRR vegetation index data been operationally available in June 1980, effective preventive control measures would have only been necessary for an area of 600 km².     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Estimation of the evapotranspiration using surface and satellite data

Abstract

A method to derive evapotranspiration from a combination of satellite and conventional data is investigated. For this purpose NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) infrared images on clear days of various seasons are used to derive surface temperatures over France. These temperatures are then compared to the shelter-height temperatures collected at the WMO (World Meteorological Organization) standard meteorological stations at the time of satellite overpass. The difference between the two temperatures varies both with season and latitude. To analyse those results we use a model of the soil-vegetation interface, forced by a reconstruction of the surface fluxes derived from the WMO data. The model simulates reasonably well the diurnal and seasonal variations in the difference between satellite surface temperature and surface-air temperature. The corresponding latitudinal variations which occur in summer may be interpreted in terms of evapotranspiration. The limitations of this method are determined by a model sensitivity study; in particular they are due to the role played by tall vegetation.     

Monitoring global vegetation using Nimbus-7 37 GHz Data Some empirical relations

Abstract

The difference of the vertically and horizontally polarized brightness temperatures observed by the 37 GHz channel of the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite are correlated temporally with three indicators of vegetation density, namely the temporal variation of the atmospheric CO₂ concentration at Mauna Loa (Hawaii), rainfall over the Sahel and the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA-7 satellite. We find the SMMR 37GHz and AVHRR provide complementary data sets for monitoring global vegetation, the 37 GHz data being more suitable for arid and semi-arid regions as these data are more sensitive to changes in sparse vegetation. The 37 GHz data might be useful for understanding desertification and indexing CO₂ exchange between the biosphere and the atmosphere.     

Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983

Normalized difference vegetation index data derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite for the 1983 growing season for the Sahelian Zone of Niger are compared with biomass estimates derived from an empirical grassland productivity model. The model used daily rainfall data to estimate the potential biomass production for fourteen meteorological stations through the growing season. A good general correspondence (r = 0·75) was seen between the productivity model and the satellite-derived integrated NDV1, although specific differences were apparent between actual and potential biomass. The study shows the utility of high-temporal-resolution satellite data for monitoring grassland conditions at a local and regional scale and emphasizes the importance of a maximum value compositing approach to the analysis. The study also shows the potential of the satellite data for quantifying phenological characteristics of vegetation     

Utility of AVHRR data for land degradation assessment: a case study

AVHRR data are widely used to monitor vegetation greenness and to provide a gross measure of primary production throughout the world. This paper examines whether AVHRR data can be used for determining the extent of land degradation in arid rangelands under commercial grazing using models of vegetation dynamics and animal grazing behaviour developed for Landsat-MSS data. These models are applied after large rainfall events and either search for systematic change in average vegetation cover across relatively uniform landscapes with increasing distance from stock watering points or examine the magnitude of vegetation response to rainfall for each pixel.

We applied the models where previous work with Landsat-MSS had demonstrated the extent of grazing impact. An index of vegetation cover using adjusted AVHRR channel 1 values produced trends in wet period average vegetation cover with increasing distance from water similar to, but less pronounced than, those obtained with MSS data. NDVI produced inconsistent and often ambiguous results when compared with the MSS data. AVHRR-derived vegetation indices were unusable in degradation assessment procedures which require pixel-scale vegetation response models. The large AVHRR pixel, even in LAC mode, creates difficulties in detecting grazing impact. Landscape changes as a result of grazing occur at a finer scale and are therefore subsumed within the pixel. Misregistration of multi-temporal images further reduces the ability to detect grazing impact on a pixel basis when such change is occurring within the pixel.

We conclude that despite their cost attractiveness, AVHRR data are inappropriate for the reliable detection of grazing impact using grazing gradient methods in the large paddocks of arid rangelands.     

Suivi du développement végétal au cours de l'été 1984 dans le Sahel Malien

A ground data-collection programme was initiated to establish a calibration between the normalized difference vegetation index (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and grassland biomass. Thirty sites were selected representing a range of Sahclian vegetation communities in the Gourma region of Mali and monitored during the 1984 growing season. The sites were 1?km square and located within larger areas of homogeneous terrain. The herbaceous and woody strata were sampled every fourteen days, and above-ground green biomass and rainfall data were collected. Ground and airborne radiometer data were recorded to facilitate interpretation of the satellite data, and aerial photographs were taken to provide estimates of tree and shrub density. AVHRR LAC and GAC data were acquired and a thermal cloud mask was applied to the data. NDVI values were extracted for the ground sites and correlation analysis performed. Low correlation coefficients were calculated for the ground measured green biomass and satellite NDVI (0,67). The correlation between the maximum NDVI and the total biomass produced during the season was 0,73. A value of 0,05 was determined as the NDVI associated with the minimum vegetation cover identifiable by the satellite (100 kg/ha). Explanation is given for the possible causes for such low correlations, including the very low biomass production associated with the 1984 drought conditions, atmospheric haze and dust and poor locational accuracy of the satellite data     

Land surface air temperature mapping using TOVS and AVHRR

Surface air temperature is an important variable in land surface hydrological studies. This paper evaluates the ability of satellites to map air temperature across large land surface areas. Algorithms recently have been developed that derive surface air temperature using observations from the TOVS (TIROS Operational Vertical Sounder) suite of instruments and also from the AVHRR (Advanced Very High Resolution Radiometer), which have flown on the NOAA operational sun synchronous satellites TIROS-N NOAA-14. In this study we evaluate TOVS soundings from NOAA-10 (nominal local time of overpass 7:30 a.m./p.m.) and data from AVHRR aboard NOAA-9 (nominal local time 2:30 a.m./p.m.). Instantaneous estimates from the AVHRR and TOVS were compared with the hourly ground observations collected from 26 meteorological stations in the Red River-Arkansas River basin for a 3-month period from May to July 1987. Detailed comparisons between the satellite and ground estimates of surface air temperatures are reported and the feasibility of estimating the diurnal variation is explored. The comparisons are interpreted in the geographical context, i.e. land cover and topography, and in the seasonal context, i.e. early and midsummer. The results show that the average bias over the 3-month period compared with ground-based observations is approximately 2°C or less for the three times of day with TOVS having lower biases than AVHRR. Knowledge of these error estimates will greatly benefit use of satellite data in hydrological modelling.     

Mapping and analysing arctic vegetation: Evaluating a method coupling numerical classification of vegetation data with SPOT satellite data in a probability model

Vegetation and environmental data were collected at 266 sampling points distributed in a regular manner along transects covering the Broggerhalvoya peninsula, on the north-western coast of Spitsbergen. Transects with sampling points were drawn in advance on aerial photographs. The analysis of releves and collection of ground data along transects represent an efficient, representative and precise way of sampling. The vegetation data were classified and 19 plant communities distinguished. The plant communities were subjected to detrended correspondence analysis (DCA). Among the recorded variables, moisture is the one with the highest correlation along axes one and two, and reflects a coincidental moisture and vegetation cover gradient. The vegetation component responsible for this positive correlation is the bryophytes. Likewise, the TWINSPAN classification confirms this gradient in a dendrogram reflecting the hierarchical structure of the plant communities. Plant communities constitute the base of a statistical model that links the communities and the SPOT satellite data. The model then classifies and maps plant communities by means of satellite data, covering the entire Broggerhalvoya peninsula. Satellite data and environmental data were analysed regarding their ability to distinguish the plant communities in a discriminant function analysis (DFA). The results of the DFA indicate that it may be reasonable to include all the information from the different satellite channels when using satellite data for vegetation classification purposes. Among the satellite data the panchromatic channel is the one adding the most unique information to the power of the model in separating plant communities. The classification of satellite data using the probability model indicates that plant communities with less than 30% vegetation cover could be classified with the same degree of confidence or better, as compared with plant communities with more than 30% vegetation cover. The overall percentage of correctly classified releves increased by 13% when using probability level two instead of level one (57.8 to 71.1%). The probability classification model makes it possible to experiment with different probability levels to improve the fit between the vegetation and satellite data classification.     

Satellite and ground-based pasture production assessment in Niger: 1986-1988

Abstract

The standing crop of herbaceous biomass produced during the 2-4?month summer rainy season by the annual grasses in the Sahel zone provides an indication of resource availability for livestock for the following 9-month dry season. Combined use of NOAA advanced very high resolution radiometer (AVHRR) local area coverage (LAC) satellite data and biomass data, obtained through vegetation sampling of 25-100 km₂ areas, allowed the development of a method for biomass assessment in Niger. Vegetation sampling involved both visual estimates and clipped plots (double sampling). The relationship between time-integrated normalized difference vegetation index (NDVI) statistics derived from NOAA AVHRR LAC data (dependent variable) and total herbaceous biomass (independent variable) was obtained through regression analysis. An inverse prediction was used to estimate biomass from the satellite data. Biomass maps and statistics of the grasslands were produced for the end of each rainy season: 1986, 1987 and 1988. This information is being used for planning purposes by the pastoral resource managers of the Government of Niger.     

Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data

The first year of Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for derivation of biophysical variables in Senegal, West Africa. The dynamic range of the two MODIS vegetation indices (VIs)—the continuity vegetation index (CVI) and the enhanced vegetation index (EVI)—is generally much larger than for the NOAA AVHRR normalized difference vegetation index (NDVI) data, indicating the importance of the change in near-infrared wavelength configuration from the NOAA AVHRR sensor to the MODIS sensor. Senegal is characterized by a pronounced gradient in the vegetation density covering a range of agro-climatic zones from arid to humid and it is found that the MODIS CVI values saturate for high VI values while the EVI demonstrates improved sensitivity for high biomass. Compared to NOAA AVHRR the MODIS VIs generally correlate better to the MODIS fraction of absorbed photosynthetically active radiation (fAPAR) absorbed by vegetation canopies and the leaf area index (LAI; the one-sided green leaf area per unit ground area). CVI is found to correlate better to both fAPAR and LAI than is the case for EVI because of the larger dynamic range of the CVI data. This suggests that the problem of background contamination on VIs from soil is not as severe in Senegal as has been found in other semi-arid African areas.     

NDVI from low altitude aircraft and composited NOAA AVHRR data for scaling Arctic ecosystem fluxes

The relationship between normalized difference vegetation index (NDVI) patterns obtained from high spatial resolution aircraft and low spatial resolution satellite data (Advanced Very High Resolution Radiometer (AVHRR)) was investigated with the intent of using multilevel data to scale carbon flux models in Arctic tundra ecosystems. Despite variable illumination conditions during the aircraft missions and maximum value compositing of the AVHRR data, the difference between 3?km average aircraft and AVHRR NDVI values was generally constant along each flight transect. However, the magnitude of the offset differed between flight dates and small lakes had a greater effect on area averaged aircraft NDVI values than on the satellite values. A cloud index was calculated using incident solar radiation measured by the aircraft and this index was used to identify periods when the aircraft NDVI values may have been biased by cloud cover. Removal of NDVI values based on a cloud index threshold did not appear to be justified given the marginal improvement in the relationship between the two NDVI datasets. If the systematic difference between AVHRR and aircraft NDVI values can be determined, then the scaling of carbon flux models based on the NDVI should be a viable approach in Arctic ecosystems.     

Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day-night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9-16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.     

Growing period and drought early warning in Africa using satellite data

Vegetation growing periods for 1983-84 were determined for 28 sites in Ethiopia using data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of meteorological satellites. Results offer promise for drought early warning from space. A strong correlation (r=0·99) was found between rates of change of the normalized difference vegetation index (NDVI) derived from the AVHRR data and threshold values of a soil moisture index at the beginning and ends of growing periods. The moisture index (P + S)/ET_p relates precipitation P, stored soil moisture S and potential evapotranspiration ET_p in a simple moisture balance (LPG) model that requires inputs of standard monthly meteorological data. A moisture threshold of (P + S)/ET_p = 0·5 was used to identify the beginning and end of the growing periods and to calibrate the time series of NDVI responses. Trends also detected in values of the NDVI during vegetation growth cycles suggest useful minima exist at the beginning and end of growing periods. Below respective minima of 0·10 and 0·22, growing periods are unlikely to have been initiated or to continue during a declining growth stage. Correlation analysis indicated a relation between moisture index and NDVI, with NDVI lagging in time, in most cases, by 5 or less weeks during the initial growth stage and 6 or more weeks during declining growth     

Effectiveness of normalized difference water index in modelling Aedes aegypti house index

The application of remotely sensed data to public health has increased in Argentina in the past few years, especially to study vector-borne viral diseases such as dengue. The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation as well as the brightness temperature (BT) for many years. Another environmental variable obtained from satellites is the normalized difference water index (NDWI) for remote sensing of the status of the vegetation liquid water from space. The aim of the present article was to test the effectiveness of NDWI together with other satellite and meteorological data to develop two forecasting models, namely the SATMET (satellite and meteorological variables) model and the SAT (satellite environmental variables) model. The models were developed and validated by dividing the data file into two sets: the data between January 2001 and April 2004 were used to construct the models and the data between May 2004 and May 2005 were used to validate them. The regression analysis for the SATMET and SAT models showed an adjusted R ² of 0.82 and 0.79, respectively. To validate the models, a correlation between the estimates and the observations was obtained for both the SATMET model (r?=?0.57) and the SAT model (r?=?0.64). Both models showed the same root mean square error (RMSE) of 0.04 and, therefore, the same forecasting power. For this reason, these models may have applications as decision support tools in assisting public health authorities in the control of Aedes aegypti and risk management planning programmes.