Improving Time Series Reconstruction by Fixing Invalid Values and its Fidelity Evaluation

Li, Xinhui; Shen, Runping; Chen, Renxi

doi:10.1109/access.2019.2962757

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…33,34 The HANTS method was superior to the SG filter in dealing with noise, which is in line with several other studies that found similar results for NDVI reconstruction. 35 However, the HANTS method shows some instability at some points in the two profiles depicted in Figure 5. This is the case in points where the HANTS showed large deviations from the raw data, in which the denoised data were larger than the original data.…”

Section: Resultsmentioning

confidence: 96%

Towards optimising the derivation of phenological phases of different crop types over Germany using optical high resolution image time series

Htitiou,

Möller,

Gerighausen

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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Crop phenological phases have traditionally been observed from the ground, which is a labor-intensive and timeconsuming activity that also lacks spatial variability due to the sparse and limited network of ground data, if any are available. In view of this, remote sensing can provide a low-cost avenue to systematically monitor and detect phenological phases from space. The most common approach for retrieving vegetation phenology from remotely sensed time series is the dynamic threshold method. However, only a few number of studies have attempted to calibrate and optimize these derived phenological metrics to relate them to actual crop growth stages. Accordingly, this study attempted to develop a framework to optimize the derivation of phenological phases for three major crops (winter wheat, corn and sugar beet) in Germany by investigating the optimal thresholds and comparing the performances with ground-truth observation data. To this end, the Normalized Difference Vegetation Index (NDVI) time series covering Germany and for two cropping seasons 2019 and 2020 were obtained and derived from a 10 x 10 km tiling grid of Sentinel-2 analysis ready data using a specific decentralized cloud platform that combines both a set of satellite imagery (petabytes of data) with huge analysis capabilities on a very large scale. Since cloud contamination is typically the major drawback for estimating phenology with optical satellite data, the study suggests first a new smoothing and gap filling method (UE-Whittaker) that is based on both envelope detection and the Whittaker filter and that, in the end, constructs high-quality NDVI time series that are suitable for phenological analysis. Based on these generated time series, the estimation of various phenological phases of crops as well as threshold optimisation and calibration were carried out as a second step. In which we traverse the thresholds from 0 to 1 with an increment of 0.01 for each specific phase and finds the optimal threshold when the lowest error value is obtained between the satellitederived DOY and the observed DOY in ground data from the year 2019. Later on, these optimum thresholds were used to derive phenological phases from the next year, and the results of the calculation of the root-mean-square error (RMSE) and the mean absolute error (MAE) between the ground reported in-situ phenology observations and those derived from satellite data reveal that they ranged typically between 3 days and 2 weeks for nearly all the phenological phases. The findings demonstrate how calibrating and optimising the derivation of different phenological phases of crops using only optical data could produce a timely and accurate information on crop growth and its condition for a large area which can be used in agricultural management, crop yield estimation, and several other related applications.

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Section: Resultsmentioning

confidence: 96%

Towards optimising the derivation of phenological phases of different crop types over Germany using optical high resolution image time series

Htitiou,

Möller,

Gerighausen

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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“…These five variables had different temporal resolutions, and they were processed into an 8-day composite to facilitate the analyses of drought. Because the 16-day vegetation products had many missing pixels due to the clouds in summer [71][72][73], we used monthly EVI products provided through a gap-filling process. Since the vegetation vitality changed gradually over time like a cosine curve, the monthly EVI was interpolated at an 8-day interval by applying a cubic spline method [71].…”

Section: Methodsmentioning

confidence: 99%

Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

Lee

Kim²,

Lee

2021

Remote Sensing

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Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

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“…The reflectivity of the cloud pollution area (area affected by clouds and cloud shadows) was abnormal, and the NDVI was lower than that in the unpolluted area [ 25 , 26 ]. To effectively remove the abnormal effect on reflectivity, it was essential to remove the cloud (include cirrus) [ 27 ] pollution pixels from the remote-sensing satellite images.…”

Section: Study Area and Dataset Preprocessingmentioning

confidence: 99%

High-Spatial-Resolution NDVI Reconstruction with GA-ANN

Zhao

Peng

Jiang

et al. 2023

Sensors

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The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm–artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space–time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction’s NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.

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Improving Time Series Reconstruction by Fixing Invalid Values and its Fidelity Evaluation

Cited by 9 publications

References 45 publications

Towards optimising the derivation of phenological phases of different crop types over Germany using optical high resolution image time series

Towards optimising the derivation of phenological phases of different crop types over Germany using optical high resolution image time series

Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

High-Spatial-Resolution NDVI Reconstruction with GA-ANN

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