Harmonization of GEOV2 fAPAR time series through MODIS data for global drought monitoring

Cammalleri, Carmelo; Verger, Aleixandre; Lacaze, Roselyne; Vogt, J.

doi:10.1016/j.jag.2019.03.017

Cited by 24 publications

(15 citation statements)

References 21 publications

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“…2.1 Data for the production of global long-term surface soil moisture data 2.1.1 Satellite-based surface soil moisture data products SMAP currently has the highest quality of all remotesensing-based soil moisture products (Al-Yaari et al, 2019) and is thus chosen as the primary training target. The SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V002 (SPL3SMP_E_002; hereinafter SMAP_E for short), which was developed by improving the spatial interpolation of the original 36 km resolution SMAP soil moisture data (Chan et al, 2018), was adopted in this study. SMAP_E was reprojected from the EASE-Grid 2.0 projection with 9 km resolution to the WGS 1984 geographic coordinate system with 0.1 • resolution.…”

Section: Methodsmentioning

confidence: 99%

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Chen

Feng

2021

Earth Syst. Sci. Data

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Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

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Section: Methodsmentioning

confidence: 99%

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Chen

Feng

2021

Earth Syst. Sci. Data

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“…Jiang (2017) also found the large temporal inconsistency between existing global LAI products at a longer time scale [85]. Cammalleri (2019) found GEOV2 fAPAR showed a systematic overestimation of the fAPAR anomalies compared with the MODIS fAPAR and proposed a two-step harmonization procedure to remove this discrepancy [86]. However, the homogenization may alter the magnitude of the original fAPAR time series in an undesirable way.…”

Section: Discussionmentioning

confidence: 99%

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

et al. 2020

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Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value.

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“…In order to delineate the drought impact and define the suboptimal conditions for crop growth, a specific threshold was selected for each of the calculated anomalies' time series [63]. In drought analysis in general a value of ±1 is often used to discriminate possible wet (positive anomalies) or dry (negative anomalies) conditions from the normal [11,35]. This value corresponded to suboptimal conditions and was used for the NDVI and ESI-based anomalies, while in the case of LST, the reverse was applied (possible wet (negative anomalies) or dry (positive anomalies).…”

Section: Methodsmentioning

confidence: 99%

Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

et al. 2020

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Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks.

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Harmonization of GEOV2 fAPAR time series through MODIS data for global drought monitoring

Abstract: HighlightsA harmonization procedure of GEOV2 and MODIS fAPAR data is introduced.Systematic overestimations of GEOV2 anomalies compared to MODIS are removed.Temporal consistent standardized anomalies can be used in drought studies.

Cited by 24 publications

References 21 publications

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

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