Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data

Bei, Xiangyi; Yao, Yunjun; Zhang, Lilin; Lin, Yi; Liu, Shaomin; Jia, Kun; Zhang, Xiaolin; Shang, Ke; Yang, Junming; Chen, Xiaowei; Guo, Xihong

doi:10.3390/s20102811

Cited by 12 publications

(6 citation statements)

References 62 publications

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“…Therefore, the inclusion of Sentinel-2B in future studies will reduce the revisit cycles (Baillarin et al, 2012;Ma et al, 2021) and improve the temporal resolution of Sentinel-2 to reduce the effect of cloud rain on data fusion accuracy. However, the low spatial resolution of MODIS leads to a large number of mixed pixels within the data (Leckie, 1990), which makes it difficult to clearly express the spatial texture detail information among different features (Bei et al, 2020;X. Zhu et al, 2016); hence, this results in a delay in NDVI growing curves.…”

Section: Discussionmentioning

confidence: 99%

Yield estimation of summer maize based on multi‐source remote‐sensing data

Wang

Liu

et al. 2022

Agronomy Journal

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Accurately estimating regional‐scale crop yields is substantial in determining current agricultural production performance and effective agricultural land management. The Yuncheng Basin is an important grain‐producing area in the Shanxi Province. This paper used Sentinel 2A with a spatial resolution of 10 m and MODIS with a temporal resolution of 1 d in 2020. The spatial and temporal nonlocal filter‐based fusion model (STNLFFM) was used to obtain fused data with a spatial resolution of 10 m and a temporal resolution of 1 d, combined with the Carnegie–Ames–Stanford Approach (CASA) and light‐use efficiency model to achieve summer maize (Zea mays L.) yield estimation. The results showed that the fused normalized difference vegetation index (NDVI) could inherit the spatial Sentinel‐2A NDVI details and express the spatial differences between smaller features more effectively. The STNLFFM NDVI curve was consistent with the actual summer maize growth condition, which accurately reflects the NDVI trend and local abrupt change information during the summer maize growth period. Moreover, the fused NDVI was influenced by topographic differences and artificial irrigation factors, whereas the summer maize yield in mountainous and plateau areas of the Yuncheng Basin was <5,000 kg ha−1 and those in the alluvial plain of the Sushui River reached 8,000 kg ha−1. The accuracy of the yield estimation model constructed based on STNLFFM NDVI (mean absolute percentage error [MAPE] = 5.47%, −13.74% ≤ relative error [RE] ≤0.12%) was significantly higher than that of the model based on MODIS NDVI (MAPE = 15.65%, −19.67% ≤ RE ≤ 20.88%), indicating that the use of spatio‐temporal fusion technology can effectively improve the summer maize yield estimation accuracy.

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Section: Discussionmentioning

confidence: 99%

Yield estimation of summer maize based on multi‐source remote‐sensing data

Wang

Liu

et al. 2022

Agronomy Journal

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“…They are classified into two categories: enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) [218] and unmixing method. ESTARFM was tested by Bei et al [219], Tao et al [220] and Zeng et al [221], who all reported the enhancement of the fusion method for complex heterogeneous regions. Bei et al [219] tested the fusion of spatial and temporal adaptive reflectance fusion models (STARFM) with the ESTARFM with different spatiotemporal features.…”

Section: Laimentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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“…where f wet is the relative surface wetness, f sm is soil moisture constraint, f T represents plant temperature constraint exp(−(T a − T opt /T opt ) 2 ), DT max describes the maximum diurnal air temperature range (40 • C), T opt is an optimum temperature (25 • C), R ns is the surface net radiation to the soil (R ns = R n (1 − f c )), G is soil heat flux (µR n (1 − f c ), µ = 0.18), R nv represents the surface net radiation to the vegetation (R nv = R n f c ), f v is the vegetation cover fraction, and NDV I min and NDV I max are the minimum and maximum NDV I, respectively. Compared with the MODIS LE product (MOD16), the MS-PT algorithm reduced the uncertainties by approximately 5 W/m 2 in the LE estimation and provided more reliable LE estimations at multiple biomes [14]. Therefore, the MS-PT was chosen to generate a spatiotemporal continuous LE product with 16 m daily resolution.…”

Section: Le Computationmentioning

confidence: 99%

“…However, due to the lack of detailed land surface information, its relatively coarse spatial resolution is not sufficient to characterize the variations of LE in heterogeneous areas [12]. With the emergence of new satellite sensors, such as the Chinese GaoFen-1 Wide Field View (GF-1 WFV) (16 m spatial resolution and frequent revisit cycle) and Sentinel-2 (a five-day cycle and 10 m spatial resolution), it has been possible to provide highly valuable data sources at fine spatial resolution [13,14]. Nevertheless, cloud contamination seriously interferes with image acquisition, contributing to a generally sparse temporal frequency.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data

Cited by 12 publications

References 62 publications

Yield estimation of summer maize based on multi‐source remote‐sensing data

Yield estimation of summer maize based on multi‐source remote‐sensing data

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

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

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