.png){kind=icon}
返回
LST retrieval algorithm adapted to the Amazon evergreen forests using MODIS data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.rse.2017.10.015.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Considering the importance of this biome and climate change projections, the monitoring of vegetation status of these rainforests becomes of significant importance. In this context vegetation temperature is presented as a key variable linked with plant physiology. In particular some studies showed the relationship between this variable and the CO2 absorption capacity and biomass loss of these tropical forests proving the potential use of vegetation temperature in the monitoring of the vegetation status. Nevertheless, the use of thermal remote sensing data over tropical forests still has some limitations being of special importance the atmospheric correction under very humid conditions and the possible high occurrence of cloudy pixels. In order to mitigate these limitations over the Amazon region, we present in this paper a new processing methodology to derive a LST product from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The LST product was generated using a tuned split window equation and cloud information derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. This LST product was validated using simulated and in situ data, and inter compared to the MODIS LST standard product (MOD11). Validation analysis shows that the new LST product reduces the RMSE by 0.6 to 1 K when compared to the MODIS standard LST product, mainly because of a reduction of the bias. We also show a preliminary intercomparison between MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) LST spatial patterns to illustrate the feasibility of VIIRS to extend forward the MODIS LST temporal series.
Keyword:
LAND-SURFACE TEMPERATURES
CLOUD CONTAMINATION
CLIMATE-CHANGE
DEFORESTATION
PRODUCTS
VEGETATION
SVALBARD
MASK
BIAS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.4
论文数:
1.1W
被引数:
9.4W
机构
引用论文
Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Nino 2015-2016在厄尔尼诺2015-2016期间，亚马逊雨林破纪录的变暖和极端干旱.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Role of Presenilin in Mitochondrial Oxidative Stress and Neurodegeneration in Caenorhabditis elegansAntioxidantsOAAI
AntioxidantsIF0
OAAI分享收藏Systematic land cover bias in Collection 5 MODIS cloud mask and derived products - A global overview收集5 MODIS云掩模和衍生产品中的系统土地覆盖偏差-全球概述
PREAI
分享收藏
查看更多内容