Evaluating Satellite Soil Moisture Datasets for Drought Monitoring in Australia and the South-West Pacific

Bhardwaj, Jessica; Kuleshov, Yuriy; Chua, Zhi‐Weng; Watkins, Andrew; Choy, Suelynn; Sun, Qizhen

doi:10.3390/rs14163971

Cited by 10 publications

(5 citation statements)

References 52 publications

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“…A comprehensive analysis of the SWCEM satellite precipitation data performed over Australia showed that blended satellite-gauge products had higher correlations and smaller errors than gauge analysis [48][49][50][51][52][53]. Similarly, earlier studies demonstrated the usefulness of satellite precipitation data not only for Australia but also for countries in the Pacific region [54,55], which means that the SWCEM data as well as the developed in this study flood risk assessment methodology could potentially be used in other countries in the Asia-Pacific region.…”

Section: Flood Hazard Indicators and Data Collectionsupporting

confidence: 59%

Flood Risk Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment

et al. 2023

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Floods are the most common and costliest natural disaster in Australia. Australian flood risk assessments (FRAs) are mostly conducted on relatively small scales using modelling outputs. The aim of this study was to develop a novel approach of index-based analysis using a multi-criteria decision-making (MCDM) method for FRA on a large spatial domain. The selected case study area was the Hawkesbury-Nepean Catchment (HNC) in New South Wales, which is historically one of the most flood-prone regions of Australia. The HNC’s high flood risk was made distinctly clear during recent significant flood events in 2021 and 2022. Using a MCDM method, an overall Flood Risk Index (FRI) for the HNC was calculated based on flood hazard, flood exposure, and flood vulnerability indices. Inputs for the indices were selected to ensure that they are scalable and replicable, allowing them to be applied elsewhere for future flood management plans. The results of this study demonstrate that the HNC displays high flood risk, especially on its urbanised floodplain. For the examined March 2021 flood event, the HNC was found to have over 73% (or over 15,900 km2) of its area at ‘Severe’ or ‘Extreme’ flood risk. Validating the developed FRI for correspondence to actual flooding observations during the March 2021 flood event using the Receiver Operating Characteristic (ROC) statistical test, a value of 0.803 was obtained (i.e., very good). The developed proof-of-concept methodology for flood risk assessment on a large spatial scale has the potential to be used as a framework for further index-based FRA approaches.

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Section: Flood Hazard Indicators and Data Collectionsupporting

confidence: 59%

Flood Risk Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment

et al. 2023

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“…This dataset provides SM with a temporal resolution of 1 h and a horizontal resolution of 0.1 • , measured in volumetric water content (m 3 /m 3 ). The SM data are divided into four layers based on depth: 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm [45,46]. For this study, SM data from the 0-7 cm soil layer at 12:00 UTC were selected for analysis.…”

Section: Era5-landmentioning

confidence: 99%

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Zhang,

Yu,

Gao

et al. 2023

Sustainability

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Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

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“…Research based on the vegetation index cannot fully desc damage caused by high temperatures, and there is a relatively obvious lag. R based on hyperspectral remote sensing has many inconveniences in data acquisit complex physical model construction [41][42][43][44][45]. Therefore, this study uses remote data with high temporal resolutions, including MODIS surface reflection data and land surface temperature products to extract vegetation temperature-sensitive ban combines LST to construct a remote sensing index, LSHDI (land surface heat dam dex), which can more comprehensively reflect the growth environment of vegetati study also analyzes the trend in the LSHDI to obtain the temporal and spatial cha the study area.…”

Section: Study Areamentioning

confidence: 99%

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

Yang

Song

et al. 2023

Remote Sensing

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As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

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Evaluating Satellite Soil Moisture Datasets for Drought Monitoring in Australia and the South-West Pacific

Cited by 10 publications

References 52 publications

Flood Risk Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment

Flood Risk Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

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