ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation

Lilin Zhang,Junming Yang,Xiaozheng Guo,Ruiyang Yu,Yunjun Yao,Xiangyi Bei,Zijing Xie,Yufu Li,Ke Shang

doi:10.3390/rs13183703

Abstract

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.

Highlights

Introduction iationsLatent heat flux (LE) refers to the heat flux transferred to the atmosphere in the process of surface soil evaporation, vegetation transpiration, and canopy intercepted evaporation, and is an important component in water balance and the energy cycle [1,2,3].Spatiotemporally continuous latent heat flux (LE) is of considerable significance to a variety of studies including understanding water and energy exchange [4], renewing terrestrial freshwater resources [5] and climate change forecasting [6]
Four GF-like images were produced by the Extremely Randomized Trees Fusion Model (ERTFM) based on the available GF-1 and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products
Compared with the original images, the images fused by the ERTFM can maintain spatial details and closely resemble the actual image

Summary

Introduction

Latent heat flux (LE) refers to the heat flux transferred to the atmosphere in the process of surface soil evaporation, vegetation transpiration, and canopy intercepted evaporation, and is an important component in water balance and the energy cycle [1,2,3]. Continuous LE is of considerable significance to a variety of studies including understanding water and energy exchange [4], renewing terrestrial freshwater resources [5] and climate change forecasting [6]. The accurate estimation of LE with both high frequency and high spatial resolution is urgently desired for regional water resources management and irrigation decision making in agriculture. Satellite-based observations provide an unprecedented opportunity to monitor largearea terrestrial ecosystem dynamics, and have been used extensively in land surface-related estimates and applications from regional to global scales [7,8].

Methods

Results

Discussion

Conclusion