文献类型：期刊文献

英文题名：High Temporal and Spatial Estimation of Grass Yield by Applying an Improved Carnegie-Ames-Stanford Approach (Casa)-Npp Transformation Method

作者：Sun, Bin[1,2] Qin, Pengyao[1,2] Yue, Wei[1,2] Gao, Zhihai[1,2] Wang, Yan[3] Li, Yifu[1,2] Yan, Ziyu[1,2]

第一作者：Sun, Bin;孙斌

机构：[1] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China; [2] Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing, China; [3] Shandong Geographical Institute of Land Spatial Data and Remote Sensing Technology, Jinan, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230141373)

语种：英文

外文关键词：Agriculture - Ecosystems - Photosynthesis - Phytoplankton - Remote sensing - Vegetation

摘要：Grass yield (GY) is the basis for comprehensive analysis of the grass–livestock balance in grassland. As an important tool for estimation of GY using remote sensing data, Net primary productivity (NPP) conversion methods still some uncertainties in the application due to their simplification of some key vegetation growth processes. Based on the Carnegie–Ames–Stanford approach (CASA) model, integrating the advantages of the high spatial resolution of GaoFen-6 wide-field-of-view data and the high temporal resolution of MODIS NDVI data, this paper proposes a reasonable expression method for the optimal temperature of the model. The applicability of the NPP conversion method to estimation of GY in different grassland types is then analyzed. The results show that, compared with the original method, which uses the optimum temperature defined at the maximum NDVI, using the average temperature from the beginning of the vegetation growing season to the period when the NDVI reaches its maximum as the optimum temperature for vegetation growth has a better theoretical basis and minimizes the influence of anomalous NDVI maxima. This largely avoids the influence of the lagged response of grassland vegetation growth to temperature. The developed GY model has strong applicability, and the correlation between the measured and estimated GY before and after optimization all reached 0.75. The overall estimation accuracy of the optimized model was improved by nearly 15%, especially in sandy shrub grassland and lowland meadow. The spatial distribution of GY in Zhenglan Banner in Inner Mongolia was found to be similar to the spatial distribution of grass types with obvious seasonal differences, and summer was the important period of grassland production, accounting for more than 80% of growth. The model proposed in this paper aims to provide scientific and technical means for the regulation of grassland resources and reasonable grazing utilization in Northern China. ? 2023, The Authors. All rights reserved.