Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

Cai, Zhanzhang; Jönsson, P.; Jin, Hongxiao; Eklundh, Lars

doi:10.3390/rs9121271

Cited by 184 publications

(125 citation statements)

References 73 publications

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“…Additionally, for each NDVI 16-day composite raster, the raster that represents the day of others. However, spline smoothing provides rather good results and its parameters can be well tuned through cross-validation [45]. Therefore, temporal gap filling is done using NDVI and NDVI_DOY information pixel-wise, by fitting a cubic smoothing spline.…”

Section: Filling Spatial and Temporal Data Gapsmentioning

confidence: 99%

“…Even with both satellites used synergically, the NDVI observations are ideally available every 9 days, which is too sparse for modeling daily SSM. The smoothing methods provide relatively simple, yet effective way for reconstructing NDVI time-series [45]. No smoothing method can be recommended more than others.…”

Section: Correlation Between Daily Filled Predictors and In Situ Soilmentioning

confidence: 99%

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New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

et al. 2020

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ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R 2 and MAE of 0.0518 m 3 /m 3 , 0.7312 and 0.0374 m 3 /m 3 , respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

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Section: Filling Spatial and Temporal Data Gapsmentioning

confidence: 99%

Section: Correlation Between Daily Filled Predictors and In Situ Soilmentioning

confidence: 99%

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

et al. 2020

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“…In Mianab region of Iran sugarcane yield was estimated via time series evaluation of NDVI, GNDVI and EVI vegetation indices and the results showed that NDVI and GNDVI vegetation indices with R 2 =0.63, RMSE=4.71 ton/ha and R 2 =0.60, RMSE 4.93 ton/ha, respectively, have good relations with sugarcane stem yield efficiency in regional scale (Khosravirad, Omid, Sarmadian, & Hosseinpour, 2019). Many time-series smoothing methods can be used for reducing noise and extracting plant phonological parameters from remotely-sensed data, but there is still no conclusive evidence in favour of one method over the others (Cai, Jönsson, Jin, & Eklundh, 2017). There is a clear interest for managers and decision-makers to have tools capable of monitoring continuously the vegetative vigour of sugarcane and providing timely information regarding potential short-term impacts of weather conditions on yield expectations.…”

Section: Introductionmentioning

confidence: 99%

Predicting Sugarcane Yields in Khuzestan Using a Large Time-Series of Remote Sensing Imagery Region

Khosravirad

Omid

Sarmadian

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ=1.4) vegetation index relative to first period of growth with R 2 =0.66 and RMSE=3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R 2 =0.66 and RMSE=3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R 2 =0.78 and RMSE=3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.

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“…One common methodology to derive the timing of crop calendar events is based on thresholds of the remote signal being previously filtered or smoothed, although this performance is site-specific, and strongly affected by the suitability of the specified threshold [5,6]. Alternatively, more robust methods are based on fitting a local mathematical model to the curve described by the signal.…”

Section: Introductionmentioning

confidence: 99%

“…Detailed data of seasonal vegetation changes over large areas can only be objectively and cost-effectively obtained by using the time-series of satellite images with high temporal resolution. Investigations of this issue have been commonly based on the analysis of diverse data sources and the assumption that cropping areas follow an annual cycle (growth, maturity, and senescence) that can be represented by the change of the remote signal (e.g., seasonal Normalized Difference Vegetation Index (NDVI) values) throughout the time [1-4].One common methodology to derive the timing of crop calendar events is based on thresholds of the remote signal being previously filtered or smoothed, although this performance is site-specific, and strongly affected by the suitability of the specified threshold [5,6]. Alternatively, more robust methods are based on fitting a local mathematical model to the curve described by the signal.…”

mentioning

confidence: 99%

Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California

et al. 2018

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Remote sensing technology allows monitoring the progress of vegetation and crop phenology in large regions. Seasonal vegetation trends are commonly estimated from high temporal resolution but coarse spatial resolution satellite imagery, e.g., from MODIS-NDVI (Moderate Resolution Imaging Spectroradiometer—Normalized Difference Vegetation Index) time-series, which has usually limited their application to scenarios with few land uses or crops covering areas larger than actual parcel sizes. As an alternative, this paper proposes a general and robust procedure to map crop phenology at the level of individual crop parcels, and validates its feasibility in a complex and diverse cropland area located in central California. A first calibration phase consisted of evaluating the three curve-fitting models implemented in the TIMESAT software (i.e., asymmetric Gaussian (AG), double logistic (DL), and adaptive Savitzky–Golay (SG) filtering) and reporting the model and its settings that best adjusted to the MODIS-NDVI profile of each crop studied. Next, based on the selected crop-specific models and with a crop map previously obtained from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-temporal images, the procedure mapped four crop calendar events (i.e., start, end, middle, and length of the season) and five phenology-related metrics (i.e., base, maximum, amplitude, derivatives, and integrals of the NDVI values) of the study region by object-based image analysis (OBIA) of the MODIS-NDVI time-series. To mitigate the impact of mixed pixels, the OBIA procedure was designed to automatically apply a restrictive criterion based on the coverage of MODIS-NDVI pixels in each crop parcel: (1) using only the MODIS-NDVI pixels that were placed 100% within each crop parcel (i.e., “pure” pixels); or (2) if no “pure” pixels exist in any crop parcel, using only pixels with coverage percentages greater than 50%, and in such cases, reporting the mixing percentage in the output file. The calibration phase showed that the performance of the SG filtering was superior in most crops, with the exception of rice, while the AG model was intermediate in all of the cases. Differences between the dates of the start and end of the season that were observed in 120 ground-truth fields and the ones estimated by the crop-specific models were in a range of 11 days (for the corn fields) and 22 days (for the vineyard fields) on average. The OBIA procedure was also validated in 240 independent parcels with “pure” MODIS-NDVI pixels, reporting 89% and 82% of accuracy when mapping the start and end of the season, respectively. Our results revealed different growth patterns of the studied crops, especially of the crop calendar events of herbaceous (i.e., corn, rice, sunflower, and tomato) and woody crops (i.e., almond, walnut, and vineyard), of the NDVI derivatives of rice and the other studied herbaceous crops, and of the NDVI integrals of vineyard and the other studied woody crops. The resulting maps and tables provide valuable geospatial information for every parcel over time with several applications in cropland management, irrigation scheduling, and ecosystem modeling.

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Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

Cited by 184 publications

References 73 publications

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

Predicting Sugarcane Yields in Khuzestan Using a Large Time-Series of Remote Sensing Imagery Region

Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California

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