Two-Dimensional Off-Grid DOA and Polarization Parameter Estimation for Parallel Coprime Polarization Sensitive Array

For the two-dimensional (2-D) spatial angle and polarization parameter estimation problem of spatial electromagnetic signals, a 2-D off-grid DOA and polarization parameter estimation algorithm based on dual parallel coprime polarization sensitive array (PCPSA) is proposed. The algorithm applies a PCPSA to receive signals, constructs a novel cross-covariance matrix according to the characteristics of the received data, and then transforms the four-dimensional parameter estimation to four one-dimensional parameter estimation by extending the virtual domain of this cross-covariance matrix, which effectively reduces the computational complexity while achieving underdetermined parameter estimation. In addition, in order to further improve the parameter estimation accuracy, the proposed algorithm introduces a off-grid model and estimates the grid bias by applying the orthogonality of the signal subspace and the noise subspace, which reduces the inherent bias brought by the preset grids and achieves the improvement of parameter estimation performance. The simulation experiments show the effectiveness of the algorithm and the better parameter estimation performance compared with the on-grid algorithm

Investigating plant’s stomatal and non-stomatal responses to water stress via STEMMUS-SCOPE model

Water stress factor is utilized to describe drought effects on plant growth in land surface models (LSMs). Accurately representing water stress is critical to understand the impact of climate change on plant and ecosystem. Models use various approaches to describe the responses of vegetation to water stress. Some models assumed water stress causes stomata closure to attenuate gas exchange process, while others assumed water stress reduces the maximum rate of carboxylation (Vcmax) to slow photosynthesis. Only a few models considered both constraints. However, which parameterization can better capture the dry condition is still controversial. A reliable detection and attribution of the impact of water stress on plant is necessary for understanding the consequence of climate change on the ecosystem from a mechanism aspect. In this study, an empirical stomatal conductance scheme (proposed by Ball et al. in1987, called “BB_gs”) and a unified stomatal conductance model (proposed by Medlyn et al. 2011, called “ME_gs”) were coupled into STEMMUS-SCOPE model to explore the discrepancy between empirical and optimal approaches. Three scenarios were designed to represent the effect of water stress on gas exchange (gs_w), photosynthesis (Vcmax_w) and both processes (gs & Vcmax_w). The coupled model was implemented for three sites with different plant function types, including C3 grassland, C3 shrub, and C4 cropland. Results showed that the optimal stomatal conductance scheme has better performance than the empirical approach because the optimal method considers the realistic stomata regulation. The Vcmax_w scheme captured the drought effects better than other schemes. The results improved our understanding on regional ecosystem functioning under the context of climate change

Determination of Hydrolysable Nitrogen in Soil Samples by Alkaline Hydrolysis Diffusion Separation Acid-base Titration Based on a Polypropylene Diffusion Dish

BACKGROUND The traditional alkaline hydrolysis diffusion separation acid-base titration method is used to determine the hydrolysable nitrogen in soil samples. Usually, a glass diffusion dish is used for alkaline hydrolysis diffusion separation. However, during sample pretreatment and alkaline hydrolysis diffusion separation, the operator often faces the following three problems. First, the solution in the inner chamber of the glass diffusion dish is very easily polluted by sodium hydroxide solution and alkaline glue solution. Second, ammonia leakage occurs easily between the glass diffusion dish and the cover, and it is often not possible to remedy when it is found. Third, the glass diffusion dish is bulky and fragile, and the experimental operation is inconvenient, all of which lead to the instability of measurement results due to inexperience of the operator. OBJECTIVES To establish a new method for the determination of hydrolysable nitrogen in soil samples by alkaline hydrolysis diffusion separation acid-base titration based on polypropylene diffusion dish. METHODS A polypropylene diffusion dish was used instead of a glass diffusion dish in the alkaline hydrolysis of hydrolysable nitrogen. The cleaning method and the sealing of the diffusion dish were improved. The addition amount of reducing agent and the concentration of sodium hydroxide solution (1.8mol/L) were unified. The addition volume of sodium hydroxide solution was appropriately increased, and the concentration of hydrochloric acid standard solution was reduced. RESULTS The absolute deviation of reference materials GBW07416a with the measured value of hydrolysable nitrogen < 50mg/kg was 0.2-1.8mg/kg. The absolute deviation of reference materials GBW07415a, NSA-1, NSA-4, NSA-5 and NSA-6 with the measured value of hydrolysable nitrogen of 50-200mg/kg was 0-4.0mg/kg. The recovery rate of nitrate nitrogen converted to ammonium nitrogen by reducing agent was 89.6%-96.4%. The measured value of soil available reference materials was consistent with the standard value. CONCLUSIONS The stability and accuracy of hydrolysable nitrogen determination are improved. The pollution in this method is significantly reduced, ammonia leakage is avoided, and the operation is convenient. The method meets the requirements for determining the content of hydrolysable nitrogen in soil samples

A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China's grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R-2 = 0.79, RMSE = 0.77 g C m(-2) day(-1)) and temperate grasslands (R-2 = 0.75, RMSE = 0.60 g C m(-2) day(-1)). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China's grasslands. The four models were trained with two strategies: training for all of northern China's grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China's grasslands fairly well, while the SAE model performed best (R-2 = 0.858, RMSE = 0.472 gC m(-2) d(-1), MAE = 0.304 gC m(-2) d(-1)). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy