Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton

Tailoring nitrogen (N) fertilizer applications to cotton (Gossypium hirsutum L.) in response to leaf N status may optimize N use efficiency and reduce off-site effects of excessive fertilizer use. This study compared leaf and canopy reflectance within the 350 to 950 nm range in order to identify reflectance ratios sensitive to leaf chlorophyll (Chl), and hence N status, in cotton. Plants were grown outdoors in large pots using half-strength Hoagland's (control) solution until some three-row plots received a restricted supply of N. Treatments comprised control, 20% of control N at first flower bud (square) onward; 0 and 20% of control N at first flower onward; and 0% of control N at fruit-filling onward. Despite leaf N values ranging from 51 to 19 g kg-1 across treatments and sampling dates, a weak correlation was obtained between Chl and N (r2 = 0.32, df = 70). In general, N stress led to increased reflectance at 695 +/- 2.5 nm (R695) and decreased reflectance at R410, and changes in leaf N were best correlated with either R695 or R755 in leaves and either R410 or R700 in canopies. The strongest associations between leaf constituent and canopy reflectance ratio were Chl vs. R415/R695 (r2 = 0.72), carotenoids vs. R415/R685 (r2 = 0.79), and N vs. R415/R710 (r2 = 0.70). The R415 measure appears to be a more stable spectral feature under N stress, as compared with more pronounced changes along the reflectance red edge (690-730 nm). Multiple regression identified a three-waveband canopy reflectance model that explained 80% of the variability in leaf N. Results indicate that remote sensing of N status in cotton is feasible using narrow-waveband reflectance ratios that involve the violet or blue region of the spectrum (400 to 450 nm) and the more commonly featured red-edge region.     

Assessment of vegetation stress using reflectance or fluorescence measurements

Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral-resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400-800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content. For separation of a wide range of treatment levels, hyperspectral (5-10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters providing optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.     

Hyperspectral reflectance response of freshwater macrophytes to salinity in a brackish subtropical marsh

Coastal freshwater wetlands are threatened by increased salinity due to relative sea level rise and reduced freshwater inputs. Remote radiometric measurement of freshwater marsh canopies to detect small shifts in water column salinity would be useful for assessing salinity encroachment. We measured leaf hyperspectral (300-1100 nm) reflectance of freshwater macrophytes (cattail, Typha latifolia and sea oxeye, Borrichia frutescens) in a field study in a subtropical brackish (2.5-4.5 parts per thousand salinity, per thousand) marsh to determine salinity effects on visible and near-infrared spectral band reflectance and to identify reflectance indices sensitive to small (1 per thousand) changes in wetland salinity. For sea oxeye, floating-position water band index [fWBI = R(900)/minimum(R(930) - R(980)), where R(lambda) = reflectance at band lambda], normalized difference vegetation index [NDVI = (R(774) - R(681))/(R(774) + R(681))], and a proposed wetland salinity reflectance ratio (WSRR = R(990)/R(933)) were sensitive to salinity with R2 of 40, 35, and 65%, respectively (p < 0.01). For cattail, NDVI and photochemical reflectance index [PRI = (R(531) - R(570))/(R(570) + R(531))] were sensitive to salinity with R2 of 29 and 33%, respectively (p 相似文献   

Optical properties of intact leaves for estimating chlorophyll concentration

Changes in leaf chlorophyll content can serve as relative indicators of plant vigor and environmental quality. This study identified reflectance, transmittance, and absorptance wavebands and band ratios within the 400- to 850-nm range for intact leaves that could be used to estimate extracted leaf chlorophyll per unit leaf area (areal concentration) with minimal error. Leaf optical properties along with chlorophyll a, b, and a + b concentrations were measured for the planar-leaved sweetgum (Liquidambar styraciflua L.), red maple (Acer rubrum L.), wild grape (Vitis rotundifolia Michx.), and switchcane [Arundinaria gigantea (Walter) Muhl.], and for needles of longleaf pine (Pinus palustris Miller). Generally, reflectance, transmittance, and absorptance corresponded most precisely with chlorophyll concentrations at wavelengths near 700 nm, although regressions were also strong in the 550- to 625-nm range. A power function was superior to a simple linear function in yielding low standard deviations of the estimate (s). When data were combined among the planar-leaved species, s values were low at approximately 50 mumol/m2 out of a 940 mumol/m2 range in chlorophyll a + b at best-fit wavelengths of 707 to 709 nm. Minimal s values for chlorophyll a + b ranged from 32 to 62 mumol/m2 across species when band ratios having numerator wavelengths of 693 to 720 nm were used with the application of a power function. Optimal denominator wavelengths for the band ratios were 850 nm for reflectance and transmittance and 400 nm for absorptance. This information can be applied in designing field portable chlorophyll meters and in the landscape-scale remote sensing of plant responses to the environment.     

SIMULATED RELATIONSHIPS BETWEEN SPECTRAL REFLECTANCE,THERMAL EMISSIONS,AND EVAPOTRANSPIRATION OF A SOYBEAN CANOPY1

ABSTRACT: A canopy reflectance model is incorporated into a routine for simulating water and energy flows in the soil-plant-atmosphere system. The reflectance model is structured tocalculate canopy albedo throughout each simulation period and to determine spectral reflectances at a specified time during the day. Spectral vegetation indices are then calculated from the reflectances and related to the evapotranspiration and thermal response of the canopy. The canopy reflectance model is also used to establish the photo-sytheticaily active radiation load at various depths in the canopy. Stomatal resistances are calculated using these radiation values and integrated to give the minimum canopy resistance. Actual canopy resistance is obtained by adjusting minimum canopy resistance for environmental stresses such as leaf water potential and leaf temperature. Using data for a soybean canopy, canopy evapotranspiration and temperatures are simulated for a range of leaf area index values and compared with the corresponding spectral vegetation indices. The resuits indicate that the normalized difference spectral index has an inverse linear relationship with canopy temperature, concurring with results obtained from satellite observations. The possibility of using a spectral vegetation index and thermal observations together to parameterize surface moisture availability for evapotranspiration is considered.     

Mixed-Grass Prairie Canopy Structure and Spectral Reflectance Vary with Topographic Position

Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of canopy height in autumn to ensure conservation of grassland structure for wildlife and forage for livestock. However, more comprehensive assessment of vegetation structure could be achieved for mixed-grass prairie by integrating field survey, topographic position (summit, mid and toeslope) and spectral reflectance data. Thus, we examined the variation of mixed-grass prairie structural attributes (canopy leaf area, standing crop mass, canopy height, nitrogen, and water content) and spectral vegetation indices (VIs) with variation in topographic position at the Grand River National Grassland (GRNG), South Dakota. We conducted the study on a 36,000-ha herbaceous area within the GRNG, where randomly selected plots (1?km² in size) were geolocated and included summit, mid and toeslope positions. We tested for effects of topographic position on measured vegetation attributes and VIs calculated from Landsat TM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data collected in July 2010. Leaf area, standing crop mass, canopy height, nitrogen, and water content were lower at summits than at toeslopes. The simple ratio of Landsat Band 7/Band 1 (SR71) was the VI most highly correlated with canopy standing crop and height at plot and landscape scales. Results suggest field and remote sensing-based grassland assessment techniques could more comprehensively target low structure areas at minimal expense by layering modeled imagery over a landscape stratified into topographic position groups.     

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.     

Dams,Floodplain Land Use,and Riparian Forest Conservation in the Semiarid Upper Colorado River Basin,USA

Land and water resource development can independently eliminate riparian plant communities, including Fremont cottonwood forest (CF), a major contributor to ecosystem structure and functioning in semiarid portions of the American Southwest. We tested whether floodplain development was linked to river regulation in the Upper Colorado River Basin (UCRB) by relating the extent of five developed land-cover categories as well as CF and other natural vegetation to catchment reservoir capacity, changes in total annual and annual peak discharge, and overall level of mainstem hydrologic alteration (small, moderate, or large) in 26 fourth-order subbasins. We also asked whether CF appeared to be in jeopardy at a regional level. We classified 51% of the 57,000 ha of alluvial floodplain examined along >2600 km of mainstem rivers as CF and 36% as developed. The proportion developed was unrelated to the level of mainstem hydrologic alteration. The proportion classified as CF was also independent of the level of hydrologic alteration, a result we attribute to confounding effects from development, the presence of time lags, and contrasting effects from flow alteration in different subbasins. Most CF (68% by area) had a sparse canopy (50% canopy cover occupied <1% of the floodplain in 15 subbasins. We suggest that CF extent in the UCRB will decline markedly in the future, when the old trees on floodplains now disconnected from the river die and large areas change from CF to non-CF categories. Attention at a basinwide scale to the multiple factors affecting cottonwood patch dynamics is needed to assure conservation of these riparian forests.     

Photosynthesis, chlorophyll integrity, and spectral reflectance in lichens exposed to air pollution

The major objective of the present study was to identify the relationship of physiologial parameters of the photosynthetic system with the elemental content of the lichen Ramalina lacera (With.) J.R. Laund. Thalli of R. lacera were collected in an unpolluted site and transplanted in a national park and an industrial region in Israel for 8 mo. Analyses of photosynthetic activity, chlorophyll integrity, spectral reflectance, and amount of 11 metals were performed after this period of exposure. The normalized difference vegetation index (NDVI), indicative of the spectral reflectance response of the thallus, correlated with photosynthetic rate and chlorophyll and K content and correlated inversely with amounts of Ba, Cr, Cu, and Ni. The NDVI appears to enable the detection of early signs of pollutant-induced stress before changes in other physiological parameters become apparent. Elevated amounts of Cr, Cu, Fe, Mn, Ni, and Zn in lichens transplanted to an industrial area and the correlation of Mn and Ni, Mn and V, Ni and V, Fe and Mn, Fe and V, and Fe and Zn point for the greater part to metal processing in a steel smelter. Correlations of Cr and Ni, Cu and Ni, Zn and Cu, Cu and Mn, and Zn and Ni could be related to metal processing in the industrial area but indicate also vehicular activity as a possible originator.     

Quantitative assessment of tidal wetlands using remote sensing

Effective management of tidal wetlands requires periodic data on the boundaries, extent, and condition of the wetlands. In many states, wetlands are defined wholly, or in combination with other criteria, by the presence of particular emergent halophytic plants. Many important characteristics of the wetlands ecosystem are related directly to the production of emergent plant material or may be inferred from knowledge of the distribution of emergent plant species. Remote-sensing techniques have been applied to mapping of the distribution of wetland vegetation but not to quantitative evaluation of the condition of that vegetation.Recent research in the tidal wetlands of Delaware and elsewhere has shown that spectral canopy reflectance properties can be quantitatively related to the emergent green biomass ofSpartina alterniflora (salt marsh cord grass) throughout the peak growing season (April through September, in Delaware). Periodic measurements of this parameter could be applied to calculations of net aerial primary productivity for large areas ofS. alterniflora marsh in which conventional harvest techniques may be prohibitively time consuming. The method is species specific and, therefore, requires accurate discrimination ofS. alterniflora from other vegetation types. Observed seasonal changes in species spectral signatures are shown to have potential for improving multispectral categorization of tidal wetland vegetation types.     

Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images

Spatial distributions of the leaf area index (LAI) needed for carbon cycle modeling in Xingguo County, China were estimated based on correlations between the field-measurements and vegetation indices (VIs). After making geometric and atmospheric corrections to two Landsat ETM+ images, one in January 2000 and the other in May 2003, three VIs (SR, NDVI, and RSR) were derived, and their separate correlations with ground LAI measurements were established. The correlation with RSR was the highest among the three VIs. The retrieved LAI values for January 2000 were lower than those for May 2003 because of a small seasonal variation in the coniferous forests (predominantly masson pine) and the decrease in the understorey vegetation during winter.     

Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).     

Earthworm density,casting activities and its impact on canopy soil nutrient profile under different aboveground vegetations

Efforts were made to assess the earthworm’s density and their casting activity under different surface plant stands (Citrus reticulata, Rosa indica, Citrus + Rosa, Tabernaemontana divaricata, Dalbergia sissoo, and Cynodon dactylon) at few natural and planted sites of a semiarid part of Rajasthan, India. The role of earthworm community in nutrient dynamics of different layers of canopy soils was also measured during this study. Results suggested a direct impact of aboveground vegetation cover on soil microclimatic conditions and earthworm density. The maximum earthworm population density and casts production was under mixed plantation, i.e., Citrus + Rosa (119.2 ind. m⁻² and 2,127.27 gm m², respectively), while T. divaricata supported the minimum earthworm density and casting activities. The population density and total casts production was in the order: C. reticulata + R. indica > R. indica > D. sissoo > C. dactylon > C. reticulata > T. divaricata. Earthworm casts and canopy soils (0–10 and 10–20 cm depth) under different plant stand were analyzed for organic C, total N, available P, and exchangeable cations (K⁺, Ca²⁺ and Mg²⁺). Earthworm casts collected under Citrus + Rosa showed the maximum level of organic C, total N, available P, and exchangeable cations (K⁺ and Mg²⁺). It is concluded that resource input by standing plant community directly affects the canopy soil quality and, thereby nutrient level in earthworm casts. This study suggested that aboveground vegetation pattern plays an important role to improve the nutrient level of canopy soils and belowground earthworm activities.     

LAI inversion algorithm based on directional reflectance kernels

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.     

Use of Landsat Thematic Mapper Data to Assess Seasonal Rangeland Changes in the Southeast Kalahari, Botswana

/ Management problems arise in semiarid rangeland that are characterized by marked wet and dry seasons because of forage deficiencies in the dry season. These natural vegetation rangelands can sustain livestock all year long when forage and senesced grass are available into the dry season. Seasonal range condition data are required to provide a basis for pasture management to help locate dry season cover and thereby minimize overstocking and degradation. The generation of seasonal data using Thematic Mapper (TM) imagery was undertaken to assess changes in natural vegetation cover in the southern Botswana Kalahari. Visual analysis of spectral reflectance curves, the development of spectral separability indexes, and conventional classification analysis techniques were used to identify and differentiate rangeland features. Results from reflectance curves indicated that most rangeland cover types could be preferentially distinguished using mainly wet season data, especially on the longer TM wavebands, and that range feature differentiation was more problematic on darker soils than on lighter soils. Spectral separability indexes (SSIs) confirmed that range feature separation varied considerably as a function of waveband and was more effective in the wet than the dry season. The SSIs also showed that range feature differentiation in both seasons was most effective using a combination of the chlorophyll absorpance band (TM3) and two mid-infrared bands (TM5 and TM7). Wet season data were more effectively classified in terms of range features than dry season data although some class similarity was inferred across the two classified data sets. The work shows that overall trends may be generated by comparing seasonal data sets, thereby providing an overall basis for dry season decision making. However, particular problems arise within the dry season data sets probably because of spectral similarities between shadow and darkened vegetation cover, thereby implying that further work is needed. KEY WORDS: Semiarid rangelands; Botswana; Kalahari; Spectral differentiation; Seasonal change; Darkened vegetation cover     

SNOWPACK CHANGES RESULTING FROM TIMBER HARVEST INTERCEPTION,REDISTRIBUTION, AND EVAPORATION1

ABSTRACT: Three processes were examined as causing snowpack changes in forest clearings. Two of the three contribute to increases and one counteracts by reducing snowpack. The two that increase snowpack are redistribution and decreased loss to interception. Snow evaporation from a clearing counteracts snowpack increases. Research has indicated that as vegetation density increases, so too does the loss to interception. As snow in the canopy reaches the limit that the canopy can hold (the threshold amount) evaporation increases. Aerodynamics of the forest canopy were studied as well. As timber is cut, wind patterns are disturbed, creating disruptions in the wind velocity gradient depositing snow in openings. This redistribution leads to an increased snow water equivalent and augments runoff. Snow evaporation was shown to increase proportionally with opening size. Evaporation offsets the water yield gains derived from forest cut. It was found that this offset is inclusive to the measurements of water yield changes in experimental forests. An optimal size of harvest block may be five tree heights in width as suggested by numerous studies.     

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.     

Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (Z(SD)) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and Z(SD) observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6-25 FNU; absorption coefficient of CDOM at 400 nm, 1.0-12.2 m(-1); Z(SD), 0.5-5.5 m; and chlorophyll a concentration, 2.4-80 mug L(-1). The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for Z(SD). If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of Z(SD). Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km(2)), which are often not included in routine monitoring programs.     

Measurement of trace gas fluxes over an unfertilized agricultural field using the flux-gradient technique

Trace gas fluxes exhibit extensive spatial and temporal variability that is dependent on a number of factors, including meteorology, ambient concentration, and emission source size. Previous studies have found that agricultural fertilization contributes to higher fluxes of certain gases. The magnitude of trace gas fluxes over unfertilized crops is still uncertain. In the present study, deposition of ammonia (NH), nitric acid (HNO), and sulfur dioxide (SO) was measured over unfertilized soybean using the flux-gradient technique. The eddy diffusivity was estimated from eddy covariance measurements of temperature fluxes, resulting in K of 0.64 ± 0.30 m s. Flux means and standard deviations were -0.14 ± 0.13, -0.22 ± 0.19, and -0.38 ± 0.54 μg m s for NH, HNO, and SO, respectively. Low concentrations of NH and HNO increased the relative uncertainties in the deposition velocities estimated from measured fluxes. This contributed to dissimilarities between deposition velocities estimated from the resistance analogy and deposition velocities estimated from fluxes. However, wet canopy conditions during the study may have led to an underestimation of deposition by the resistance analogy because the resistance method does not accurately describe the enhanced deposition rates that occur after dew formation. Quantification of vegetation characteristics, such as leaf wetness and apoplast chemistry, would be beneficial in future studies to more accurately determine stomatal resistance and its influence on fluxes.     

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630–690 nm), band 5 (1550–1750 nm) and the ratio between bands 1 (450–520 nm) and 4 (760–900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520–600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.