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Leaf angle distribution (LAD) is an important vegetation parameter quantifying the structure of the canopy. Together with leaf area index, LAD is often assumed to fully describe the structure of herbaceous plant canopies such as field crops. To this date, only a few papers have addressed the direct estimation of LAD from near-nadir imaging spectroscopy data. Using simulations with the PROSAIL vegetation reflectance model and measurements of six field crops, we propose and evaluate two methods of determining leaf mean tilt angle (MTA), the central moment of LAD, from reflectance data in blue, red and near infrared wavebands. First, we noted that when canopy reflectance in blue (479 nm) is plotted against that in red (663 nm), the PROSAIL-simulated canopies with different MTAs occupied different areas in the plot. Similarly, the six crop species in the measured dataset were separated in the red–blue reflectance plane. To retrieve MTA from spectral reflectance measurements, we created rotated coordinate axes in the red–blue plane and aligned one of the axes with the largest change in MTA. The new coordinate, which was highly and near-linearly correlated with MTA ( R 2 = 0.97), was subsequently used to estimate MTA from airborne imaging spectroscopy data. The second MTA retrieval algorithm was based on earlier findings of a high correlation ( R 2 = 0.78) between MTA and reflectance at 748 nm. Using a lookup table created with PROSAIL, we inverted MTA from measured crop reflectance at this far red edge wavelength. We evaluated the two MTA retrieval methods using photographically measured species-specific MTA. For the red–blue method, MTA was estimated with a root mean square difference (RMSD) of 18.7° between field measured and spectroscopically estimated values. Retrieval from crop reflectance at 748 nm provided a RMSD of 11.4°. To demonstrate the generality of our methods, analyze their limitations, and explain the differences between the two retrieval algorithms, we give an analytical interpretation of the observed correlations using the spectral invariants theory. [ABSTRACT FROM AUTHOR]

Abstract: To deal with water resources crisis caused by climate change and intensified human activities, the Hetao Irrigation District of Inner Mongolia in North China has been experiencing rehabilitation for water-saving since 1999. This has significantly changed its regional hydrological cycle and thus water use patterns over different land use types. In this paper, Surface Energy Balance Algorithm for Land (SEBAL) model fed with MODIS data was applied in Hetao area from 2000 to 2010 to examine the spatial and temporal patterns of evapotranspiration (ET). The SEBAL estimated ET agreed well with that from other methods in the study area. The results indicate that inter-annual variability in ET over agricultural land, water body, woodland and irrigated grassland are primarily explained by the variation of reference ET. So it is with the seasonal variability in agricultural land ET on monthly basis. However, the inter-annual variations of ET over sandy land, Gobi desert and mountain areas are mainly controlled by precipitation. Over the study period, a reduction in river water diversion for irrigation has not reduced the agricultural land ET, indicating no significant impact on agricultural production. But ET over the non-irrigated grassland tends to decrease, which was likely caused by declining groundwater table in recent years. [Copyright &y& Elsevier]

Abstract: This paper introduces a new procedure to investigate the influence of weather types on spatio-temporal tree-ring growth patterns. Within the framework of the procedure a new proxy-based climate index termed as the atmospheric circulation tree-ring index (ACTI) is calculated that combines weather-type classification data with climate proxies. The new approach is tested using a Central European multi-species tree-ring width network consisting of 115 sites. The procedure used to calculate the ACTI time series includes: 1. Monte Carlo simulations, 2. multiple testing correction, and 3. selection and grouping algorithms. The tree-ring width series are grouped according to their weather-type responses, and the ACTI time series were computed for each group. The new procedure explains the climate signal preserved in tree-ring chronologies more comprehensively because the complex interaction between the different climate elements is taken into account by using weather-type classification data. Strong correlations between the ACTI and tree-ring width series (r =0.69) and between the ACTI time series and climate gridded datasets (r >0.6) were observed. Further a correlation between ACTI and northern hemisphere mean temperatures (r =0.5) were found. These results indicate that the ACTI enables the investigation of weather-type growth relationships. [Copyright &y& Elsevier]

Abstract: The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux (H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET=0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil+vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50Wm−2 for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30Wm−2) and highest r 2 values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6mmd−1. The reflectance-based crop coefficient model yielded RMSD values of 0.4mmd−1, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry. [Copyright &y& Elsevier]

Abstract: The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant radiance values from high-resolution HRVIR2/SPOT4 imagery. The band combination composed of green (G), red (R) and near infrared (NIR) proved to be appropriate for LAI prediction, with a cross-validation error lower than 0.7. The convex hull technique has been proposed to assess the extrapolation error of the up-scaling model. Although the model showed to be sensitive to pixels outside the convex hull, the error associated with extrapolated pixels was moderate (i.e. root mean square error (RMSE)≈0.3). A close agreement was found between LAI maps derived from different field based datasets despite the notable differences in the sampling constraints imposed by the use of different instruments. It confirms the validity of a stratified random sampling strategy and suggests the suitability of using around 50 elementary sampling units (ESUs) to characterize the study area. A sensitivity analysis has confirmed that this method is suited to capture the variability across the site extent and, at the same time, minimize field efforts. Knowledge gained from this work provides guidance for an efficient sampling strategy of LAI in a cropland area. [Copyright &y& Elsevier]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Abstract: The moderate resolution imaging spectroradiometer (MODIS) is being used to monitor gross primary production (GPP), both spatially and temporally, routinely from space. However, estimates of GPP at various flux stations indicate that the MODIS algorithm may (i) over-predict GPP at sites where limitation to growth by low-soil water content is not adequately captured by the reduction in stomatal conductance by vapor pressure and (ii) under-predict GPP in highly productive, evergreen, needle leaf forests, due to a reduced radiation-use-efficiency term. The objective of this paper is to determine if any systematic bias exists in the MODIS algorithm relative to eddy covariance (EC) estimates of GPP made over an evergreen, needle leaf temperate rainforest on Vancouver Island, Canada, which is routinely water-stressed in summer months. Results indicate that 8-day GPP as predicted by the standard MODIS algorithm, with appropriate parameters for evergreen needle leaf forest, was highly correlated to EC-measured GPP (r 2 =0.89, p <0.001, S.E.=0.9gCm−2 day−1), however with significant bias, under predicting GPP by as much as 30%. Increasing the radiation-use-efficiency term ɛ max (gCMJ−1) from the MODIS lookup value to the maximum observed at the site resulted in a reduced bias in the predicted GPP, however estimates were 8% higher than EC measurements. To account for soil water stress on plant growth, we implemented a soil water modifier initially proposed by Leuning et al. [Leuning, R., Cleugh, H., Zegelin, S., Hughes, D., 2005. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates. Agric. For. Meteorol. 129, 151–173] that accounts for rainfall and potential evaporation in the antecedent 3 months, a surrogate for soil water availability. Results confirm that field observations of relative available soil water content in the 0–60cm layer matched the proposed soil water modifier closely with the relationship between the modified MODIS algorithm GPP and the EC-measured GPP remaining highly significant (r 2 =0.91, p <0.001, S.E.=1.1gCm−2 day−1) with no significant bias. Whilst broad scale implementation of such a soil water modifier into the MODIS algorithm is still limited due to lack of rainfall data, at least in the short-term, the modifier does provide an alternative for researchers and land mangers, interested in applying the MODIS GPP products over regional areas, but who may have, or are observing, over-estimated production estimates due to the lack of inclusion of soil water modification to growth. [Copyright &y& Elsevier]