Estimating crop net primary production using high spatial resolution remote sensing data

摘要

Net Primary Productivity (NPP) is crucial in modelling global carbon cycle. There are a lot of studies focused on NPP evaluation using remote sensing method, resulting in different evaluation models. Most of the models are based on large spatial scale such as national or global, leading to retrieval errors in heterogeneous pixels and difficulties in field validation. This paper develops a new, remote sensing NPP evaluation method to estimate NPP on high spatial resolution. The model uses a newly improved Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Model which is based on the recollision probability (FAPAR-P Model) to calculate the Absorbed Photosynthetic Active Radiation (APAR), which improves the accuracy of APAR estimation. The study area was the midstream of Heihe River Basin, located mostly in Zhangye, Gansu province, China.