Green LAI Research Articles

A Greening Strategy of Mitigation of the Thermal Environment for Coastal Sloping Urban Space

Greening can usually have a cooling effect on urban space; but is this law also applicable to coastal sloping urban space? The coastal urban space of Qingdao Haizhifeng Square, with a sloping topography, was the area we selected to study. The study area contained two parts: a coastal green space and a residential area. ENVI-met was used to create six scenarios. Different lawns, black pine and ash were planted in the two areas to study the cooling effect. The results showed that the closer the area was to the sea, the better the thermal comfort. In both the coastal green area and the residential area, trees increased the PET of the site, and the higher the LAI of the trees, the more obvious the thermal effect. At 15:00, the hottest time during the summer, the highest PET at pedestrian height was lowest in the scenario without trees, reaching 28.3 °C, and the highest was with full ash, reaching 34.3 °C. At the same time, the average difference in PET between the two scenarios was 1.4 °C. The highest PET at pedestrian height was generated in the area of the building away from the sea breeze, especially in the case of the sloping topography behind it or dense street trees on the urban road. Finally, it was concluded that, in urban spaces with a coastal slope topography, lawns should be planted in the coastal green part and low LAI trees in residential areas, and shade trees should not be planted on the coastal walkway. This afforestation strategy can provide a basis to formulate a strategy for promoting the design of regions with similar geographical and climatic conditions in the future.

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.

Multiple drivers of seasonal change in PRI: Implications for photosynthesis 2. Stand level

The goal of this study was to explore the relationships between stand-level photochemical reflectance index (PRI) and canopy structure/pigment pools, as well as light use efficiency (LUE) of photosynthetically active vegetation focusing on seasonal or ontogenetic time frames. PRI was originally designed as a means of assessing the xanthophyll cycle and LUE over short (e.g. diurnal) time frames, and few studies have explored the drivers of PRI over longer, seasonal time frames, particularly in crops having different photosynthetic pathways or canopy structures. Consequently, our purpose was to understand and quantify the drivers of PRI responses over seasonal time scales for two crops, maize (C4) and soybean (C3), contrasting in photosynthetic pathway, leaf structure and canopy architecture. In both crops, PRI was very closely related to green LAI (R2>0.90) and stand chlorophyll (Chl) content (R2>0.93). The slopes of the relationships in different phenological stages, vegetative and reproductive, were substantially different (3-fold smaller in the vegetative stage). The main cause of this disparity was the high PRI value of soil/residue background. While PRI was a sensitive indicator of the changes in stand green LAI and stand Chl content over the full growing season, it was not sensitive to LUE; LUE explained below 12% of PRI variation in maize and 19% in soybean. Unlike leaf-level PRI, stand-level PRI was not clearly related to the Car/Chl ratio, presumably because the large changes in canopy structure (affecting stand Chl and green LAI) had a dominant influence on PRI over this time frame. The strong relationship between PRI and stand Chl content as well as between PRI and Chl-related vegetation index over a growing cycle allowed us to subtract the stand Chl content effect from measured PRI to reveal the component of PRI most likely related to periods of stress. However, for accurate subtraction of the Chl effect from long-term PRI records, thoughtful study of uncertainties related to “natural” variation of PRI-stand Chl relationships, and stand Chl content estimation for different varieties of the same species and for different species is required. The findings of a strong link between stand-level PRI and stand green LAI and Chl content and the lack of a clear relationship between PRI and LUE over seasonal and ontogenetic time spans suggest the need for a more careful evaluation of the relationship between PRI and either LUE or photosynthetic activity. In particular, studies that contrast short-term (e.g. diurnal) vs. long-term (e.g. seasonal) pigment, PRI, and photosynthetic responses in contrasting vegetation types are needed to clarify the different mechanisms involved at different temporal and spatial scales. These findings have important implications for attempts to monitor photosynthetic phenology from remote sensing, many of which have relied on PRI as an indicator of photosynthetic activity.

Canopy Leaf Area Index in Non-Forested Marshes of the California Delta

Leaf area index (LAI, one-sided leaf area per unit ground area) is an important parameter in models of canopy-atmosphere carbon exchange and greenhouse gas fluxes that has been disproportionately under-studied in wetland ecosystems. This study assessed variation in LAI and its sensitivity to canopy height, structure and remotely sensed normalized difference vegetation index (NDVI) in the California Delta, USA in July–August 2014. Plant area index (PAI), an optically measured LAI proxy, significantly varied among wetland sites and vegetation types (p < 0.001). PAI significantly correlated with leaf inclination angle and above-water heights of canopy and standing litter, being on average higher in taller freshwater reeds with steeper leaf angles. Presence of litter contributed to within-site heterogeneity and overestimation of PAI at direct green LAI < 3. Although satellite-based NDVI strongly correlated with PAI (R2 = 0.63, p < 0.001), its application was constrained by presence of water and other non-vegetated backgrounds in wetland pixels. Overall, high within-site variation in LAI resulting from structural heterogeneity and/or vegetation diversity limits representativeness of mean LAI for ecosystem models. This constraint calls for more in-depth future analyses of the effects of vertical and horizontal wetland complexity on LAI measurements and performance as an indicator of light interception and canopy-based ecosystem function.

Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm

Vegetation indices (VIs) have been used previously for estimating green leaf area index (green LAI). However, it has not been verified how characteristics of the relationships between these indices and green LAI (i.e., slope, intercept, standard error) vary for different crops and whether one universal algorithm may be applied for accurate estimation of green LAI. By analyzing the data from four different crops (maize, soybean, wheat, and potato) this study aimed at: (1) determining if the previously used VIs for estimating green LAI in maize and soybean may be applicable for potato and wheat and vice versa; and (2) finding a robust algorithm for green LAI estimation that does not require re-parameterization for each crop. Spectral measurements of wheat and potato were obtained in Israel from 2004 to 2007 and of maize and soybean in the USA from 2001 to 2008, and various VIs calculated using measured reflectance were compared with green LAI measured in the field. For all four crops, ten different VIs were examined. Similarities in relationships between VIs and green LAI were found. Among the examined VIs, two variants of the chlorophyll index and wide dynamic range vegetation index with the green and red edge bands were the most accurate in estimating green LAI in all four crops. Hyperspectral reflectance data were used to determine optimal diagnostic bands for estimating green LAI in four crops using a universal algorithm. The green (530–570nm) and red edge (700–725nm) regions were identified for both the wide dynamic range vegetation index and chlorophyll index as having the lowest errors estimating green LAI. Since the Landsat 8 – OLI has a green spectral band and the forthcoming Sentinel-2, Sentinel-3 and VENμS have both green and red edge bands, it is expected that these VIs can be used to monitor green LAI in multiple crops using a single algorithm by means of near future satellite missions.

Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250 m resolution data

The fraction of incident photosynthetically active radiation absorbed by the photosynthesizing tissue in a canopy (fAPAR) is a key variable in the assessment of vegetation productivity. It also plays tremendous role in accurate retrieval of light use efficiency, which is essential for assessing vegetation health status. The main goal of this work was to study in detail relationships of fAPAR absorbed by photosynthetically active vegetation (fAPARgreen) and Normalized Difference Vegetation Index (NDVI) for two crops with contrasting leaf structures (C3 vs. C4) and canopy architectures, using close range (6m above the canopy) radiometric data and daily MODIS data taken during eight growing seasons over three irrigated and rainfed maize and soybean sites. Our specific goal was to understand differences in fAPARgreen/NDVI relationship when crop canopy was almost vertically homogeneous (with respect to leaf area and leaf chlorophyll content), as in vegetative stage, and vertically heterogeneous as in reproduction stage. Firstly, we established fAPARgreen/NDVI relationships for NDVI, taken at close range, and assessed noise equivalent of fAPARgreen estimation by NDVI, and then we established relationships for NDVI retrieved from daily MODIS 250m data. Daily MODIS data illuminated fine details of this relationship and detected effects of canopy heterogeneity on fAPARgreen/NDVI relationship. In vegetative stage, the fAPAR/NDVI relationships for contrasting in leaf structures and canopy architectures crops were almost linear allowing accurate estimation of fAPARgreen as it is below 0.7. However, very different fAPARgreen/NDVI relationships in reproductive stages for both crops were observed, showing that canopy architecture and leaf structure greatly affect the relationship as leaf chlorophyll content changes and vertical distribution of chlorophyll content and green LAI inside the canopy becomes heterogeneous. We have found fine details of the fAPARgreen/NDVI relationships with two types of hysteresis that prevent the use of a single relationship for fAPARgreen estimation by NDVI over the whole growing season and suggested mechanisms for each type of hysteresis that should be further studied using radiative transfer models.

Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production

Life on Earth depends on photosynthesis. Photosynthetic systems evolved early in Earth history, providing evidence for the significance of pigments in plant functions. Photosynthetic pigments fill multiple roles from increasing the range of energy captured for photosynthesis to protective functions. Given the importance of pigments to plant functioning, greater effort is needed to determine and quantify the relationship between gross primary production (GPP) and canopy chlorophyll (Chl) content, the main photosynthetic pigment, as well as its proxy, green leaf area index (GLAI), both used as quantitative measures of plant greenness. The objective of this study is to establish relationships for GPP vs. canopy Chl content and GPP vs. GLAI in maize. The main focus of the paper is to reveal fine details of the relationships and understand their features in different stages of maize development. Data on GPP, leaf Chl content and GLAI were collected across ten years (2001–2010) at three AmeriFlux sites in Nebraska over irrigated and rainfed maize. Relationships of GPP vs. total canopy Chl content and GPP vs. GLAI were established for vegetative, tasseling and reproductive stages. In each stage, relationships were close with determination coefficients above 0.9; however, the shapes and slopes of the relationships in vegetative stages were different from reproductive stages. This difference was more pronounced for the GPP vs. GLAI relationship. In part, this difference is due to different leaf Chl contents in vegetative and reproductive stages. Smaller but detectable differences in shape and slope were also found for the GPP vs. canopy Chl relationship. Despite the differences in relationships for vegetative and reproductive stages, for the entire growing season, green LAI (GLAI) explained 90% of GPP variation with a coefficient of variation (CV)=17%, while total canopy Chl content explained more than 92% of GPP variation with CV=15%. Quantitative characterization of relationships between GPP and such biophysical characteristics as GLAI and canopy Chl content underlines the role of chlorophyll in photosynthesis and has significant implications on remote sensing of primary production.

Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

• Validation of a dynamically coupled and process-based ecohydrological model. • Autocorrelation and scaling analysis of spatial surface soil moisture patterns. • Patterns depend on soil properties, precipitation and land use heterogeneity. • Evapotranspiration causes small scale patterns during growing season. • Scaling factor depends upon LAI and precipitation. Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km 2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m −2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale patterns that are induced by soil properties are superimposed by the small scale land use pattern and the resulting small scale variability of evapotranspiration. However, this influence decreases at larger spatial scales. Most precipitation events cause temporarily higher surface soil moisture autocorrelation lengths at all spatial scales for a short time even beyond the autocorrelation lengths induced by soil properties. The relation of daily spatial variance to the spatial scale of the analysis fits a power law scaling function, with negative values of the scaling exponent, indicating a decrease in spatial variability with increasing spatial resolution. High evapotranspiration rates cause an increase in the small scale soil moisture variability, thus leading to large negative values of the scaling exponent. Utilizing a multiple regression analysis, we found that 53% of the variance of the scaling exponent can be explained by a combination of an independent LAI parameter and the antecedent precipitation.

Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons

There is an increasing need to monitor the dynamics of green LAI of field crops through the growing season. A simple approach is to use a regression model to estimate crop LAI from a vegetation index derived from optical remote sensing data. However, variations of interference factors in the signal path could induce variations in spectral reflectance, leading to uncertainty in LAI estimation. A semi-empirical equation was implemented to estimate green LAI of field crops from Landsat-5/7 data using a few vegetation indices, including the normalized difference vegetation index (NDVI), the optimized soil adjusted vegetation index (OSAVI), the two band enhanced vegetation index (EVI2) and the modified triangular vegetation index (MTVI2). Data were collected during several growing seasons, from 1999 to 2006, over corn, soybean, and spring wheat fields in an experimental farm in Ottawa (ON, Canada). LAI estimated for corn, soybean and wheat from Landsat data using the vegetation indices was compared to ground LAI. Except for NDVI, comparable results were obtained from the other three vegetation indices, with a coefficient of determination above 0.83 and a root mean square error (RMSE) not more than 0.60. The performance of NDVI was less satisfactory (RMSE>0.66). The uncertainties in LAI estimation induced by variations in soil reflectance, leaf optical properties, canopy structure, and atmospheric conditions were assessed through a global sensitivity analyses using the PROSPECT leaf model coupled to the SAIL canopy model along with the 6S atmospheric transmission model. The sensitivity analyses show that different indices are affected differently by the various interference factors. Comparatively, NDVI is the most influenced by leaf chlorophyll but the least affected by leaf inclination, OSAVI and the narrow band MTVI2 are more efficient in reducing soil effects, and EVI2 has a better performance in reducing aerosol perturbation. At high LAI, the uncertainty of NDVI is the smallest, but the uncertainty propagated to LAI estimation is the largest due to saturation. In this case, vegetation indices that are less prone to saturation should be considered, such as EVI2 and MTVI2. When MTVI2 is used on multispectral data, its ability to reduce soil and leaf chlorophyll perturbation is similar to EVI2 but weaker than when it is used on hyperspectral data. These results show that vegetation indices can be used in a simple regression model to generate baseline green LAI product for seasonal crop growth monitoring, however it is important to be aware of the sources of uncertainty and their relative amplitudes when using the product.

Comparison of different vegetation indices for the remote assessment of green leaf area index of crops

Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI Green, the CI Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m 2/m 2). However, the CI Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m 2/m 2). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).

Two Kinds of Modified Spectral Indices for Retrieval of Crop Canopy Chlorophyll Content

Based on the thorough analysis of the spectral response mechanism of vegetation indices,which are used widely to retrieve chlorophyll content at present,taking advantage of sample reflectance data of crop canopy,simulated by models PROSPECT+SAIL,the sensitivity differences of these vegetation indices for chlorophyll content are compared and discussed,including Pigment-Specific Simple Ratio a(PSSRa),Pigment-Specific Simple Ratio b(PSSRb),Pigment-Specific Normalized Difference a(PSNDa),Pigment-Specific Normalized Difference b(PSNDb),Normalized Pigments Chlorophyll ratio Index(NPCI),Pigment Ratio Index(PRI),Modified Chlorophyll Absorption Ratio Index(MCARI) and Triangular Vegetation Index(TVI).The analysis results indicated that the above vegetation indices were either sensitive to changes of soil background or affected by saturation at high LAI levels.Therefore,it is not an ideal way to retrieve chlorophyll content by these vegetation indices.Then,four new kinds of modified spectral indices based on TVI and MCARI are proposed,including Modified Triangular Vegetation Index 1(MTVI1),Modified Triangular Vegetation Index 2(MTVI2),Modified Chlorophyll Absorption Ratio Index 1(MCARI1) and Modified Chlorophyll Absorption Ratio Index 2(MCARI2).The spectral response mechanism of four modified spectral indices was disclosed that they are both less sensitive to soil background and green LAI and more sensitive to the variation of chlorophyll content,and their performances of chlorophyll content retrieval were validated with the experimental data.The experiment demonstrated that two modified spectral indices, MTVI2 and MCARI2,are proved to be the better predictors for chlorophyll content,and the retrieval model of chlorophyll content of crop canopy can be established based on them.

Net ecosystem CO 2 exchange in a temperate cattail marsh in relation to biophysical properties

Net ecosystem exchange (NEE) of carbon dioxide (CO 2) was measured at a temperate cattail marsh using the eddy covariance technique in order to examine the relationships between NEE, weather, and vegetation properties. Analyses of CO 2 fluxes for a complete year (May 9, 2005 to May 30, 2006) showed that the marsh wetland was a net CO 2 sink for each month from June to September (monthly 24-h averages of −0.1, −5.1, −4.8, and −2.2 g C m −2 day −1, respectively) and a source of CO 2 to the atmosphere for the remaining fall and winter months. Wintertime (November to April) average NEE was 0.5 g C m −2 day −1. The annual cumulative CO 2 balance shows a net uptake of 264 g C m −2 year −1 by the marsh. Ecosystem respiration (ER) and gross ecosystem production (GEP) were estimated to be 567 and 831 g C m −2 year −1, respectively. Respiration rates were associated with a Q 10 value of 2.8. Peak aboveground biomass (average of 1156 g m −2) and peak green LAI (average of 3.63) were reached in mid-August. Variations in growing season NEE were well correlated with variations in live biomass ( r = 0.92) and green LAI ( r = 0.94).

Parameter Genetik Jagung Populasi Bisma pada Pemupukan yang Berbeda. II. Ragam dan Korelasi Genetik Karakter Sekunder

&lt;p&gt;Genetic Parameter of Secondary Traits of Corn in Bisma&lt;br /&gt;Population under Different Fertilizer Application. II. Genetic&lt;br /&gt;Variance and Correlation of Secondary Traits.&lt;br /&gt;Sutoro, Abdul Bari, Subandi, and Sudirman Yahya. The&lt;br /&gt;magnitute of secondary traits of corn could affect the corn&lt;br /&gt;yield. Genetic parameter value of secondary traits are&lt;br /&gt;important in plant breeding, especially in selection program.&lt;br /&gt;Genetic parameter could be used for estimation of correlated&lt;br /&gt;response by involving value of genetic correlation and&lt;br /&gt;genetic variance. Value of genetic parameter is different&lt;br /&gt;among population and environment. Genetic parameter of&lt;br /&gt;secondary traits on Bisma population under 3 different level&lt;br /&gt;of fertilizer application were studied in Bogor. Result of the&lt;br /&gt;study showed that addtive genetic variance of ASI, chlorofil,&lt;br /&gt;seed number, amount of green leaves and LAI at flowering&lt;br /&gt;stage greater than dominant variance. Conversely, additive&lt;br /&gt;genetic variance of green leaf number and LAI at maturing&lt;br /&gt;stage, and leaf senescene are lower than dominant variance.&lt;br /&gt;ASI has greatest heritability among secondary traits&lt;br /&gt;under three level of fertilizer application. Positive genetic&lt;br /&gt;correlation was found between grain yield under low fertileizer&lt;br /&gt;application and ASI under high level fertilizer application&lt;br /&gt;or between grain yield under high level of fertilizer application&lt;br /&gt;and ASI under low level fertilizer application.&lt;/p&gt;

Year‐round observations of the net ecosystem exchange of carbon dioxide in a native tallgrass prairie

SummaryAn Ameriflux site was established in mid 1996 to study the exchange of CO2 in a native tallgrass prairie of north‐central Oklahoma, USA. Approximately the first 20 months of measurements (using eddy covariance) are described here. This prairie, dominated by warm season C4 grasses, is typical of the central Kansas/northern Oklahoma region. During the first three weeks of the measurement period (mid‐July–early August 1996), moisture‐stress conditions prevailed. For the remainder of the period (until March 1998), however, soil moisture was nonlimiting. Mid‐day net ecosystem CO2 exchange (NEE), under well‐watered conditions, reached a maximum magnitude of 1.4 mg CO2 m−2 s−1 (flux toward the surface is positive) during peak growth (mid‐July 1997), with green leaf area index of 2.8. In contrast, under moisture‐stress conditions in the same growth stage in 1996, mid‐day NEE was reduced to near‐zero. Average night NEE ranged from near‐zero, during winter dormancy, to − 0.50 mg CO2 m−2 s−1, during peak growth. Most of the variance in average night NEE was explained by changes in soil temperature (0.1 m depth) and green leaf area. The daytime NEE measurements were examined in terms of a rectangular hyperbolic relationship with incident photosynthetically active radiation. The analysis showed that the quantum yield during peak growth was similar to those measured in other prairies and the y‐intercept, so obtained, can be potentially used as an estimate of night‐time CO2 emissions when eddy covariance data are unavailable. Daily integrated NEE reached its peak magnitude of 30.8 g CO2 m−2 d−1 (8.4 g C m−2 d−1) in mid‐July when the green LAI was the largest (about 2.8). In general, the seasonal trend of daily NEE (on relatively clear days) followed that of green LAI. Annually integrated carbon exchange, between prescribed burns in 1997 and 1998, was 268 g C m−2 y−1. After incorporating carbon loss during the prescribed burn , the net annual carbon exchange in this prairie was near‐zero in 1998.

Effect of crop nitrogen status and temperature on the radiation use efficiency of winter oilseed rape

In temperate environments, the total dry matter (TDM) of a crop is closely related to the amount of photosynthetically active radiation absorbed (ΣPARa), as long as other factors (water, nutrients, . . .) are non-limiting. For oilseed rape crops, many authors have shown that the radiation use efficiency (RUEa) varies within a wide range, from 1 to 4 g MJ −1 of ΣPARa according to developmental stage and environmental conditions. In order to explain this variability, effects of N and temperature on RUEa were investigated during a 1-year field experiment involving three N treatments (N fertilisation rates: 0, 135 and 270 kg ha −1). Leaf, flower, and pod surface areas, as well as the DM and N content of the various plant parts were measured every 2–4 weeks for 17 sampling dates from emergence to harvest. RUEa was calculated from total generated DM (shoot and root DM, plus that of dead leaves fallen to the ground). Daily PARa was calculated using a 3-layer model taking into account leaf and pod absorption and reflection of PAR by flowers and soil. The N nutrition index (NNI) proposed by Lemaire and Gastal (1997) was used to evaluate the N effect on RUEa. NNI was significantly higher than the critical N value (meaning that N was non-limiting) for the N270 treatment from emergence to pod formation, but N deficiency occurred with N0 at the 12-leaf stage and later with N135 at flowering. The maximum possible RUEa (RUEa max) was assumed to be the value obtained with the treatment N270, where N was non-limiting. The N deficiencies which occurred for N0 and N135 significantly reduced the green LAI and PAI, and consequently ΣPARa. To remove any effect other than N on the RUEa, the ratio of actual to maximum RUEa (RUEa/RUEa max) proposed by Bélanger et al. (1992) was calculated for each developmental stage of oilseed rape. A linear regression fit well ( R 2=0.919; 8 d.f.) the response of RUEa/RUEa max versus N deficiency, for values of NNI lower than 1. The resulting equation was the following: RUEa/RUEa max=0.74×NNI+0.23. RUEa max was also significantly affected by developmental stage. Whereas the corresponding changes in RUEa max from the 6-leaf stage to the end of flowering could be related to air temperature, there was evidence of a developmental effect in the other stages. RUEa was lower in the early stages (emergence to 5–6 leaves), and from pod formation until ripening; the latter decrease could be attributed to the high energy cost of lipid biosynthesis.

Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types

In a range of plant species from three Californian vegetation types, we examined the widely used "normalized difference vegetation index" (NDVI) and "simple ratio" (SR) as indicators of canopy structure, light absorption, and photosynthetic activity. These indices, which are derived from canopy reflectance in the red and near‐infrared wavebands, highlighted phenological differences between evergreen and deciduous canopies. They were poor indicators of total canopy biomass due to the varying abundance of non‐green standing biomass in these vegetation types. However, in sparse canopies (leaf area index [LAI]°0‐2), NDVI was a sensitive indicator of canopy structure and chemical content (green biomass, green leaf area index, chlorophyll content, and foliar nitrogen content). At higher canopy green LAI values (&gt;2; typical of dense shrubs and trees), NDVI was relatively insensitive to changes in canopy structure. Compared to SR, NDVI was better correlated with indicators of canopy structure and chemical content, but was equivalent to the logarithm of SR. In agreement with theoretical expectations, both NDVI and SR exhibited near‐linear correlations with fractional PAR intercepted by green leaves over a wide range of canopy densities. Maximum daily photosynthetic rates were positively correlated with NDVI and SR in annual grassland and semideciduous shrubs where canopy development and photosynthetic activity were in synchrony. The indices were also correlated with peak springtime canopy photosynthetic rates in evergreens. However, over most of the year, these indices were poor predictors of photosynthetic performance in evergreen species due to seasonal reductions in photosynthetic radiation‐use efficiency that occurred without substantial declines in canopy greenness. Our results support the use of these vegetation indices as remote indicators of PAR absorption, and thus potential photosynthetic activity, even in heterogeneous landscapes. To provide accurate estimates of vegetation‐atmosphere gas fluxes, remote NDVI and SR measurements need to be coupled with careful estimates of canopy photosynthetic radiation‐use efficiency.

Solar zenith angle effects on vegetation indices in tallgrass prairie

Nadir spectral reflectances for a range of grassland conditions on the Konza prairie in Kansas were acquired in 1987 from June to October during the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment, or FIFE. Measurements in the red (0.668 ± 0.005 μm) and near-infrared (0.821 ± 0.006 μm) were obtained at numerous solar zenith angles (SZAs) during the diurnal cycle. Reflectance factors were utilized for computation of two spectral vegetation indices (VIs) — the simple ratio (SR) and the normalized difference vegetation index (NDVI). The diurnal change in the narrow red and near-infrared bands and their VIs as a function of SZA, and the relationship of these spectral data to biophysical measurements, were examined throughout the growing season. The assumption of independence of VIs from illumination geometry was specifically addressed. Both the SR and the NDVI were significantly affected by SZA for many prairie conditions. SR was significantly related to more canopy variables over a wider SZA range (27°–61°) than was NDVI. For intermediate green LAI (0.5 < LAI < 2.0), both indices were at a minimum value at solar noon, increasing with SZA. Conversely, the higher biomass sites (LAI > 2.0) with especially tall grass exhibited either aa maximum value at solar noon which decreased as SZA increased or a moderate to high constant VI. Low density canopies (LAI < 0.5) exhibited no change in their low VI values over the diurnal course. These qualitatively different responses precluded the use of a general “correction” to remove the observed SZA effects on the measured VI values. The strength of the relationships between VI and LAI also varied, depending on SZA, with lower predictive capability at the traditionally used “high sun” condition than at oblique sun angles (SZA > 40°). The restriction of surface measurements to a standard acquisition SZA of 45° produced a good correspondence of both the SR and NDVI to LAI (r 2 = 0. 69). The best predictive linear model for the SR was obtained at SZA = 45°(r 2 = 0.82) and was based on two canopy variables, the green LAI and the canopy standing green to standing dead dry weight ratio. Therefore, a standard SZA for data acquisitions at 45° is recommended over the traditional “high sun” practice. The results suggest that the VIs measure the instantaneous, projected, and illuminated LAI canopy fraction relative to the illuminated substrate.

Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. Field experiments were conducted on a typic Argiaquoll at the Purdue Agronomy Farm, West Lafayette, IN to determine the effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat ( Triticum aestivum L). The fertilization treatments consisted of 0, 60, and 120 kg N/ha, applied as urea in the spring. Spectral reflectance was measured 11 times during the 1979 growing season and 10 times during the 1980 growing season with a spectroradiometer (Exotech 20C) in the 400–2400 nm wavelength region. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index, plant moisture, and fresh and dry phytomass. Relationships between spectral and agronomic variables were developed using data from 1979 and tested with data from 1980. N fertilization of wheat reduced visible, increased near infrared, and deceased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the nonfertilized plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. This study demonstrated that N-stressed wheat could be distinguished from healthy wheat spectrally and, therefore, that multispectral imagery may be useful for monitoring crop condition.