The GF land surface albedo product based on the high-spatial-and-temporal-resolution BRDF priori-knowledge and its preliminary validation

YOU Dongqin; WEN Jianguang; TANG Yong; LIU Qiang; ZHONG Shouyi; HAN Yuan; GONG Baochang; ZHONG Bo; WU Shanlong; LIU Qinhuo

doi:10.11834/jrs.20231717

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The GF land surface albedo product based on the high-spatial-and-temporal-resolution BRDF priori-knowledge and its preliminary validation
Vol. 27, Issue 3, Pages: 738-747(2023)
Received：08 November 2021，

Published：07 March 2023
DOI： 10.11834/jrs.20231717
稿件说明：

移动端阅览

游冬琴，闻建光，唐勇，刘强，钟守熠，韩源，宫宝昌，仲波，吴善龙，柳钦火.2023.基于高时空分辨率BRDF先验知识的高分卫星地表反照率产品及其初步验证.遥感学报，27（3）： 738-747 DOI： 10.11834/jrs.20231717.

You D Q， Wen J G，Tang Y， Liu Q， Zhong S Y， Han Y， Gong B C， Zhong B， Wu S L and Liu Q H. 2023. The GF land surface albedo product based on the high-spatial-and-temporal-resolution BRDF priori-knowledge and its preliminary validation. National Remote Sensing Bulletin， 27（3）：738-747 DOI： 10.11834/jrs.20231717.

摘要

地表反照率是地表辐射能量平衡中的关键因子。利用高分卫星数据生产地表反照率产品，将有效地支撑局地尺度的遥感监测应用。但高分卫星数据反演反照率面临着角度信息不足难以获取BRDF特性的难题。本文利用高分一号卫星，基于低分辨率多角度信息获取时空分布的BRDF先验知识来反演高分卫星数据BRDF，进而估算反照率，生成了高分卫星全国地表反照率产品，并分析了产品的时空特征和基于地面测量数据验证了产品精度。主要利用黑河、怀来、东北遥感试验站多个观测点长时间序列地面观测进行直接验证，地表覆盖类型包括玉米作物，海棠、水杉、油松等林地以及草地，而在冬季其覆盖则转变为裸土或冰雪等。产品的时空动态变化基本捕捉了地表的变化特征，与地面站点数据时间序列特征吻合度较高。产品在植被覆盖的结果普遍较好，而在裸土和冰雪条件下精度则有所降低，总体均方根误差为0.05，相对精度80.24%，基本满足应用需求。验证结果表明，本文利用更为精细的具有时空分布特征（高时空分辨率）的BRDF先验知识有效地反演了高分数据反照率产品。反照率产品精度直接受到辐射定标、几何定位、大气校正、云识别、算法等精度影响，高分卫星反照率产品算法的适用性和产品的精度还需要更充分的验证和分析。

Abstract

Land surface albedo is a critical parameter in radiation and energy budget. Using GF satellite data to produce the land surface albedo is beneficial for local-scale environmental monitoring.

The challenge beneath the albedo estimation from the GF satellite data is the inadequate angular information

which complicates the BRDF inversion and the albedo derivation based on BRDF. We use the high spatial-and-temporal-resolution priori-knowledge BRDF database obtained from coarse spatial resolution multiangular information to help describe the GF BRDF features. Then

the GF albedo is estimated from the derived GF BRDF. The algorithm is applied in GF-1 data to generate the land surface albedo product in China. First

this algorithm and the production are introduced briefly. Then

the spatial-temporal features are evaluated by qualitative analysis and quantitative validation. In the validation

a long time series of field-measured albedo from the sites of different land covers is used. It includes the cropland (maize) in the Daman site from the Heihe remote sensing test site located in the northwest of China

the forest (Chaenomeles

metasequoia

Chinese pine) in the Huailai test remote sensing site located in the north of China

and the grass in the Dongbei remote sensing test site located in the northeast of China. These sites would be covered by bare soil or snow in winter. In this study

we preliminarily evaluate the algorithm feasibility in albedo production and product precision.

The time series comparisons of the field measurements and the GF product present good agreement for different sites over 1 year to 2 years. For over 1-or 2-year time-continuous comparisons

the land surface status is changed driven by the phenology (vegetation growing cycle). Thus

this finding reveals the feasibility of the algorithm based on the spatial-temporal distributed BRDF a priori knowledge. The total root mean square error is 0.05

with a relative accuracy of 80.24%

which meets the application requirement.

Therefore

this algorithm is feasible for the albedo estimation from the GF data. The validation results show a good agreement between the field measurements and the product. However

the remote sensing common products from GF satellite data have not been initiated long enough. Thus

evaluating the GF satellite data is still needed. Besides the algorithm itself

the albedo accuracy is directly affected by the land surface reflectance product’s precision

which may introduce uncertainties from the geometric and radiometric calibration

atmospheric correction

and even cloud production. Therefore

much work should be done to evaluate this product and clarify the effects of those factors. In this way

the algorithm and albedo production can be improved.

关键词

Keywords

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