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: A case study of Zhenglan Banner, Inner Mongolia, China

作者：Sun, Bin[1,2] Qin, Pengyao[1,2] Yue, Wei[1,2] Guo, Ye[3] Gao, Zhihai[1,2] Wang, Yan[4] Li, Yifu[1,2] Yan, Ziyu[1,2]

第一作者：孙斌;Sun, Bin

通信作者：Gao, ZH[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]NFGA, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Dev Res Ctr NFGA, Beijing 100013, Peoples R China;[4]Shandong Geog Inst Land Spatial Data & Remote Sens, Jinan 250002, Peoples R China

年份：2024

卷号：224

外文期刊名：COMPUTERS AND ELECTRONICS IN AGRICULTURE

收录：;EI(收录号：20242416233899);Scopus(收录号：2-s2.0-85195430066);WOS:【SCI-EXPANDED(收录号:WOS:001256625100001)】；

基金：This work was supported by the National Natural Science Foundation of China [grant number 42271407; 42001386] and the special fund for Science and Technology Innovation Teams of Shanxi Province (No. 202204051001010) .

语种：英文

外文关键词：Grass yield; Net primary productivity conversion method; Carnegie-Ames-Stanford approach; Optimal temperature; Spatiotemporal fusion

摘要：Grass yield (GY) is a critical component of the comprehensive analysis of the grass - livestock balance in grassland. Net primary productivity (NPP) conversion methods, such as the Carnegie - Ames - Stanford approach (CASA) model, are an important tool for remote -sensing -based estimations of GY. However, the application of such approaches is limited by the simplification of key vegetation growth processes. In this study, we integrated high spatial and temporal resolution normalized difference vegetation index (NDVI) data collected from Gaofen6 (GF-6) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively, in 2020 with the climatic characteristics of grassland vegetation to derive a reasonable expression of the optimum temperature. We then improved the CASA model for the accurate estimation of GY for six different grassland types in Zhenglan Banner (sandy sparse forest grassland, sandy shrub grassland, sandy meadow, low hill steppe, gently sloping steppe, and lowland meadow) at high spatial and temporal resolution. The model estimations were evaluated using field data. The results reveal that adopting the optimum temperature to incorporate vegetation growth characteristics achieves a better theoretical basis and minimizes the influence of anomalous NDVI maxima compared with the original CASA model. 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 reached 0.75. Moreover, the overall estimation accuracy was improved by nearly 15%. The spatial distribution of GY in Zhenglan Banner was found to be similar to the spatial distribution of grassland types with obvious seasonal differences, and summer was the critical period for GY, accounting for more than 80% of growth. The proposed model aims to provide scientific and technical guidance for the regulation of grassland resources and reasonable grazing utilization in Northern China.