Precipitation-use efficiency along a 4500-km grassland transect

National Natural Science Foundation of China [30590381, 40971027]; State Key Technologies RD Program [2006BAC08]; Chinese Academy of Sciences ; National Key Research and Development Program [2010CB833501]

Spatial and temporal flows of China's forest resources: Development of a framework for evaluating resource efficiency

Science & Technology Basic Work Program of China: Scientific Survey of the Middle-lower Reaches of Lantsang River and the Great Shangri-La Region [2008FY110300]; National Basic Research Program of China (973 Program): Ecosystem Services and Ecological Safety of the Major Terrestrial Ecosystems of China [2009CB421106]; National Natural Science Foundation of China [30670374]; EU ; European Commission, DG Research [003874]

Evaluation for use efficiency of agricultural resources in grain production: A case study of Changshu, Taihe and Ansai in China

National Natural Science Foundation of China [70673097]

Radiation-use efficiency and the harvest index of winter wheat at different nitrogen levels and their relationships to canopy spectral reflectance

Radiation-use efficiency (RUE, g/MJ) and the harvest index (HI, unitless) are two helpful characteristics in interpreting crop response to environmental and climatic changes. They are also increasingly important for accurate crop yield simulation, but they are affected by various environmental factors. In this study, the RUE and HI of winter wheat and their relationships to canopy spectral reflectance were investigated based on the massive field measurements of five nitrogen (N) treatments. Crop production can be separated into light interception and RUE. The results indicated that during a long period of slow growth from emergence to regreening, the effect of N on crop production mainly showed up in an increased light interception by the canopy. During the period of rapid growth from regreening to maturity, it was present in both light interception and RUE. The temporal variations of RUEAPAR (aboveground biomass produced per unit of photosynthetically active radiation absorbed by the canopy) during the period from regreening to maturity had different patterns corresponding to the N deficiency, N adequacy and N-excess conditions. Moreover, significant relationships were found between the RUEAPAR and the accumulative normalised difference vegetation index (NDVI) in the integrated season (R-2 = 0.68), between the HI and the accumulative NDVI after anthesis (R-2 = 0.89), and between the RUEgrain (ratio of grain yield to the total amount of photosynthetically active radiation absorbed by the canopy) and the accumulative NDVI of the whole season (R-2 = 0.89) and that after anthesis (R-2 = 0.94). It suggested that canopy spectral reflectance has the potential to reveal the spatial information of the RUEAPAR, HI and RUEgrain. It is hoped that this information will be useful in improving the accuracy of crop yield simulation in large areas.

Study of remote sensing based parameter uncertainty in Production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.