Publications

Sun, B; Qin, PY; Yue, W; Guo, Y; Gao, ZH; Wang, Y; Li, YF; Yan, ZY (2024). 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. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 224, 109134.

Abstract
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.

DOI:
10.1016/j.compag.2024.109134

ISSN:
1872-7107