1. GEO and LEO satellite sensors can complement each other in monitoring terrestrial vegetation, but there is a mismatch between their observation data due to differences in viewing geometries and spectral response functions.
2. An algorithm has been developed to obtain NDVI-based indices that are less influenced by these factors, allowing for more accurate inter-sensor translations of vegetation indices.
3. The algorithm was evaluated using off-nadir GEO observation data from the Himawari 8 AHI and near-nadir LEO observation data from the Aqua MODIS as a reference over land surfaces in Japan at middle latitudes, showing good agreement between the NDVI-based indices of the sensors.
该文章旨在探讨如何开发一种算法,以获得与植被覆盖度等价的参数(基于归一化植被指数(NDVI)和端元光谱计算的FVC),以便将地球同步轨道卫星(GEO)和低地球轨道(LEO)卫星的观测数据相互补充。然而,该文章存在以下问题:
1. 偏见来源:该文章没有提及其他可能影响GEO和LEO传感器之间一致性的因素,例如大气校正、地表反射率变化等。
2. 片面报道:该文章只关注了日本中纬度地区的情况,并未考虑其他地区可能存在的不同情况。
3. 无根据主张:该文章声称开发的算法可以消除传感器之间的偏差,但并未提供足够证据支持这一主张。
4. 缺失考虑点:该文章没有考虑到NDVI本身存在的局限性,例如对土壤湿度、土壤类型等因素敏感。
5. 所提出主张缺失证据:该文章没有提供足够证据支持使用NDVI-based index作为参数进行传感器之间转换时的有效性。
6. 未探索反驳:该文章没有探讨其他学者对该算法的反驳和质疑。
7. 宣传内容:该文章过于强调GEO卫星的优势,而未充分探讨GEO和LEO卫星之间的差异和限制。
综上所述,该文章存在一些偏见和不足之处,需要更全面地考虑各种因素,并提供更充分的证据支持其主张。