Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

Ojha, Nitu; Merlin, Olivier; Suere, Christophe; Escorihuela, Maria-José

doi:10.3389/fenvs.2021.555216

Cited by 24 publications

(13 citation statements)

References 64 publications

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“…Our findings showed that EVI (R=0.27) correlated higher than NDVI (R=0.21). Compared to NDVI, EVI is more sensitive in high biomass regions and can reduce the effect of variability of soil colour and atmospheric noise (Matsushita et al 2007;Ojha et al 2021). Jurečka et al (2018) found that the EVI has a slightly greater correlation value (R=0.8) than the NDVI (R=0.1).…”

Section: Comparing the Correlation Of Vis In Oil Palm Yield Predictionmentioning

confidence: 99%

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learnings (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) model to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection techniques were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R2=0.91; RMSE= 2.92 tonnes per ha; MAE= 2.56 tonnes per ha and MAPE= 0.09) and full predictors (R2=0.76; RMSE of 3.03 tonnes per ha; MAE of 2.88 tonnes per ha; MAPE of 0.10 tonnes per ha). In addition, advanced ensemble ML techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5% - 26% for R2, and that decreased by 3% - 31% for RMSE, 7% - 34% for MAE, and 1% - 15% for MAPE, respectively. DNN generates the most accurate statistical metrics, with an increase of around 15% for R2, 11% for RMSE, 32% for MAE and 1% for MAPE. Our study successfully developed efficient, effective and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These can be potentially handled by plantation management to estimate oil palm yields to speed up the decision-making process for sustainable production.

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Section: Comparing the Correlation Of Vis In Oil Palm Yield Predictionmentioning

confidence: 99%

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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“…LST and enhanced vegetation index (EVI) data extracted from MOD11A1/MYD11A1 and MOD13A2 products, respectively, were used as input to the DISPATCH disaggregation method. In practice, the C4DIS processor is applied to SMAP level-3 data to disaggregate SM data at 1 km resolution [54,63].…”

Section: Modismentioning

confidence: 99%

“…Recent improvements in the DISPATCH algorithm (named DISPATCH veg−ext ) allowed increasing its applicability domain by including densely vegetated areas and using the MODIS EVI instead of MODIS NDVI to increase its robustness over vegetated areas [54]. Herein, the DISPATCH veg−ext algorithm is sequentially applied to 36 km resolution SMAP SM and to the 1 km resolution disaggregated SMAP SM using MODIS and Landsat LST/NDVI data, respectively.…”

Section: Dispatchmentioning

confidence: 99%

“…where T s,HR is the soil temperature at HR, and T s,max and T s,max the soil temperature for fully dry (SEE = 0) and wet conditions (SEE = 1). More details are given in Ojha et al [45] and Ojha et al [54]. Note that the 100 m resolution DISPATCH SM data are finally resampled at the field crop scale for comparison with Sentinel-1-based estimates.…”

Section: Dispatchmentioning

confidence: 99%

“…At the same time, recent research progresses present good direction for downscaling SMAP-like data using ancillary optical/thermal data [50][51][52]. However, such disaggregation approaches are still generally implemented at the 1 km resolution using MODIS or Sentinel-3 data [53][54][55][56]. Our study fundamentally differs from those previous approaches in that DISPATCH is implemented at 100 m resolution by using Landsat data, so that the spatial variability of SM is represented at a much finer scale, which is now consistent with the typical size of crop fields.…”

Section: Introductionmentioning

confidence: 99%

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A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Ojha

Merlin

Amazirh³

et al. 2021

Sensors

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Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

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Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning

Ang

Shafri

Lee³

et al. 2022

Earth Sci Inform

View full text Add to dashboard Cite

Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

Cited by 24 publications

References 64 publications

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning

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