The application of FengYun-3 Microwave Radiation Imager soil moisture product in drought monitoring

2020 ◽  
Author(s):  
Ruijing Sun ◽  
Yeping Zhang ◽  
Shengli Wu
Keyword(s):  
Soil Moisture ◽  
Microwave Radiation ◽  
Snow Water Equivalent ◽  
Surface Soil ◽  
Microwave Radiometer ◽  
Empirical Relationship ◽  
Drought Monitoring ◽  
Retrieval Algorithm ◽  
Severe Drought ◽  
Drought Event

<p>FY-3(Feng Yun 3) satellites series are the China’s second-generation polar-orbiting meteorological satellites. FY-3B is the second satellite of FY3 series which was launched on November 5, 2010. One of the eleven instruments on board the FY-3B satellite is the Microwave Radiation Imager (MWRI) which is a highly sensitive microwave radiometer. It is China’s first space-borne microwave radiometer. It has 5 different frequencies from 10.65GHz to 89GHz with dual polarization. The MWRI instrument provides measurements of terrestrial, oceanic, and atmospheric parameters, including precipitation rate, sea ice concentration, snow water equivalent, soil moisture, atmospheric cloud water, and water vapor. Soil moisture, as a key parameter in the drought monitoring, becomes especially concerned. The FY-3B/MWRI soil moisture product provides global observations of land surface soil moisture. The current soil moisture retrieval algorithm of FY-3B/MWRI uses the brightness temperature with both v and h polarizations of 10.65GHz to eliminate the effects of surface roughness and vegetation simultaneously. For the bare surface soil estimation part, the algorithm is based on a parameterized surface emission model (the Qp model) which uses a physically based soil moisture inversion technique for application with passive microwave measurements. For the vegetation correction part, the algorithm uses the empirical relationship between the NDVI and the vegetation water content to estimate the vegetation optical depth. The spatial resolution of FY-3B/MWRI soil moisture product is 0.25°×0.25°. In recent years, drought occurs frequently worldwide. As the only microwave sensor which operationally provides global soil moisture products currently in china, the FY-3B/MWRI soil moisture product plays an important part in drought monitoring during the meteorological service. In the summer of 2014, Henan Province which is located in the middle area of China suffered severe drought. The soil moisture of this area remained a very low level all along until significant precipitation finally came in last September. In the year of 2018, there was a severe drought occurred in Afghan, we used a long-time data series to analyze this drought event. The result showed that the FY-3B/MWRI soil moisture can objectively reflect the spatial distribution and development process of drought. This paper will give an introduction of the applications of FY-3B/MWRI soil moisture product during these drought event.</p>

scholarly journals Research on Occurrence and Development of Pasture Drought Events in Alpine Grassland using the Drought Threshold

2018 ◽  
Author(s):  
Tiaofeng Zhang ◽  
Lin Li ◽  
Hongbin Xiao ◽  
Hongmei Li
Keyword(s):  
Soil Moisture ◽  
Alpine Grassland ◽  
Soil Drought ◽  
Severe Drought ◽  
Drought Event ◽  
Drought Intensity ◽  
Quantitative Expression ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
The Relationship

Abstract. Pasture is vital to livestock husbandry development in Qinghai and even in North China. Drought is the primary meteorological disaster that affects pasture, but insufficient soil moisture is the most prominent cause of pasture drought. Timely and accurate determination of the soil moisture threshold of pasture is important for objective recognition and monitoring of the occurrence and development of pasture drought. This study aims at investigating pasture responses to soil drought as well as quantitative expression of soil drought degree and drought threshold. Test plots were selected from the pasture test station. Five testing groups were set according to coverage rate (0–100 %) at the initiation the pasture growth period. The impacts of profile moisture characteristics, drought threshold, and precipitation on duration of pasture drought were studied. Research results have demonstrated that moisture in the soil profile below 20 cm decreases slightly throughout drought events in alpine grassland. Changes of soil moisture in the 0–20 cm layer can generally reflect drought stress of the pasture. In the process of a drought event, the relationship between soil water storage and cumulative relative water loss can be expressed via a logarithmic linear equation. Quantitative expression of drought degree in grasslands can be realized by transforming the slope of this equation into the index D with an interval of [0, 1]. The occurrence rates of mild drought,moderate drought, and severe drought were 0.36, 0.45, and 0.70, respectively. The duration of severe drought was closely related with initial soil moisture. The relationship between duration of drought and the necessary minimum precipitation can be expressed by an exponential equation. Values of the D index can express soil drought intensity and pasture drought intensity. The durations for different grades of drought events were correlated with both initial soil moisture and previous precipitation. The conclusions of this study can provide scientific references for the objective understanding onoccurrence, development, monitoring, and early warning of pasture drought.

scholarly journals Assimilation of Disaggregated Microwave Soil Moisture into a Hydrologic Model Using Coarse-Scale Meteorological Data

2006 ◽  
Vol 7 (6) ◽  
pp. 1308-1322 ◽  
Author(s):  
O. Merlin ◽  
A. Chehbouni ◽  
G. Boulet ◽  
Y. Kerr
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Hydrologic Model ◽  
Observation Time ◽  
Coupling Scheme ◽  
Surface Soil Moisture ◽  
Coarse Scale ◽  
Near Surface

Abstract Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days.

Evaluation of 2018 drought and effectiveness of adaptation measures in the Netherlands

2020 ◽  
Author(s):  
Marjolein H.J. van Huijgevoort ◽  
Janine A. de Wit ◽  
Ruud P. Bartholomeus
Keyword(s):  
Soil Moisture ◽  
The Netherlands ◽  
Capillary Rise ◽  
Hydrological Drought ◽  
Water Levels ◽  
Severe Drought ◽  
Drought Event ◽  
Groundwater Levels ◽  
The Impact

<p>Extreme dry conditions occurred over the summer of 2018 in the Netherlands. This severe drought event led to very low groundwater  and surface water levels. These impacted several sectors like navigation, agriculture, nature and drinking water supply. Especially in the Pleistocene uplands of the Netherlands, the low groundwater levels had a large impact on crop yields and biodiversity in nature areas. Projections show that droughts with this severity will occur more often in the future due to changes in climate. To mitigate the impact of these drought events, water management needs to be altered.</p><p>In this study, we evaluated the 2018 drought event in the sandy regions of the Netherlands and studied which measures could be most effective to mitigate drought impact. We have included meteorological, soil moisture and hydrological drought and the propagation of the drought through these types. Droughts were determined with standardized indices (e.g. Standardized Precipitation Index) and the variable threshold level method. Investigated measures were, for example, higher water levels in ditches, reduced irrigation from groundwater, and increased water conservation in winter. We also studied the timing of these measures to determine the potential for mitigating effects during a drought versus the effectiveness of long term adaptation. The measures were simulated with the agro-hydrological Soil–Water–Atmosphere–Plant (SWAP) model for several areas across the Netherlands for both agricultural fields and nature sites.</p><p>As expected, decreasing irrigation from groundwater reduced the severity of the hydrological drought in the region. Severity of the soil moisture drought also decreased in fields that were never irrigated due to the effects of capillary rise from the groundwater, but, as expected, increased in currently irrigated fields. Increasing the level of a weir in ditches had a relatively small effect on the hydrological drought, provided water was available to sustain higher water levels. This measure is, therefore, better suited as a long term change than as ad hoc measure during a drought. The effectiveness of the measures depended on the characteristics of the regions; for some regions small changes led to increases in groundwater levels for several months, whereas in other regions effects were lost after a few weeks. This study gives insight into the most effective measures to mitigate drought impacts in low-lying sandy regions like the Netherlands.</p>

scholarly journals Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products

2019 ◽  
Author(s):  
Shraddhanand Shukla ◽  
Kristi R. Arsenault ◽  
Abheera Hazra ◽  
Christa Peters-Lidard ◽  
Randal D. Koster ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Food Insecurity ◽  
Crop Yield ◽  
Southern Africa ◽  
Early Warning ◽  
Crop Yields ◽  
Severe Drought ◽  
Drought Event ◽  
Antecedent Soil Moisture ◽  
Monitoring And Forecasting

Abstract. The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. In 2015–2016, an El Niño-driven drought resulted in major maize production shortfalls, food price increases, and livelihood disruptions that pushed 29 million people into severe food insecurity. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically, starting in April) and lean season (typically, starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available rootzone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of a recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). The NHyFAS system's ability to forecast and monitor the 2015/2016 drought event is evaluated. The system's capacity to explain interannual variations in regional crop yield and identify below-normal crop yield events is also evaluated. Results show that the NHyFAS products would have identified the regional severe drought event, which peaked during December–February of 2015/2016, at least as early as 1 November 2015. Next, it is shown that February RZSM forecasts produced as early as 1 November (4–5 months before the start of harvest and about a year before the start of the next lean season) correlate fairly well with regional crop yields (r = 0.49). The February RZSM monitoring product, available in early March, correlates with the regional crop yield with higher skill (r = 0.79). It is also found that when the February RZSM forecast produced on November 1 is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds (significance level ~ 86 %) of the time. Furthermore, when the February RZSM monitoring product (available in early March) indicates a lowest tercile value, the crop yield is always below normal, at least over the sample years considered. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region.

Remote Sensing of Surface Soil Moisture from FengYun MicroWave Radiation Imager (MWRI) Data Using a Machine Learning Technique

2020 ◽  
Author(s):  
Sibo Zhang ◽  
Wei Yao
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Tibetan Plateau ◽  
Microwave Radiation ◽  
Missing Values ◽  
Surface Soil ◽  
Low Frequency ◽  
Global Scale ◽  
Western China ◽  
Surface Soil Moisture

<p>In the past, soil moisture can be retrieved from microwave imager over most of land conditions. However, the algorithm performances over Tibetan Plateau and the Northwest China vary greatly from one to another due to frozen soils and surface volumetric scattering. The majority of western Chinese region is often filled with invalid retrievals. In this study, Soil Moisture Operational Products System (SMOPS) products from NOAA are used as the learning objectives to train a  machine learning (random forest) model for FY-3C microwave radiation imager (MWRI) data with multivariable inputs: brightness temperatures from all 10 MWRI channels from 10 to 89 GHz, brightness temperature polarization ratios at 10.65, 18.7 and 23.8 GHz, height in DEM (digital elevation model) and statistical soil porosity map data. Since the vegetation penetration of MWRI observations is limited, we exclude forest, urban and snow/ice surfaces in this work. It is shown that our new method performs very well and derives the surface soil moisture over Tibetan Plateau without major missing values. Comparing to other soil moisture data, the volumetric soil moisture (VSM) from this study correlates with SMOPS products much better than the MWRI operational L2 VSM products. R<sup>2</sup> score increases from 0.3 to 0.6 and ubRMSE score improves significantly from 0.11 m<sup>3</sup> m<sup>-3</sup> to 0.04 m<sup>3</sup> m<sup>-3</sup> during the time period from 1 August 2017 to 31 May 2019. The spatial distribution of our MWRI VSM estimates is also much improved in western China. Moreover, our MWRI VSM estimates are in good agreement with the top 7 cm soil moisture of ECMWF ERA5 reanalysis data: R<sup>2</sup> = 0.62, ubRMSD = 0.114 m<sup>3</sup> m<sup>-3</sup> and mean bias = -0.014 m<sup>3</sup> m<sup>-3</sup> for a global scale. We note that there is a risk of data gap of AMSR2 from the present to 2025. Obviously, for satellite low frequency microwave observations, MWRI observations from FY-3 series satellites can be a benefit supplement to keep the data integrity and increase the data density, since FY-3B\-3C\-3D satellites launched in November 2010\September 2013\November 2017 are still working today, and FY-3D is designed to work until November 2022.</p>

scholarly journals Using vegetation indices for soil-moisture retrievals from passive microwave radiometry

2001 ◽  
Vol 5 (4) ◽  
pp. 671-678 ◽  
Author(s):  
E.J. Burke ◽  
W.J. Shuttleworth ◽  
A.N. French
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Retrieval Algorithm ◽  
Emission Model ◽  
Normalised Difference Vegetation Index

Abstract. Surface soil moisture and the nature of the overlying vegetation both influence microwave emission from land surfaces significantly. One widely discussed but underused method for allowing for the effect of vegetation on soil-moisture retrievals from microwave observations is to use remotely sensed vegetation indices. This paper explores the potential for using the Normalised Difference Vegetation Index (NDVI) in soil-moisture retrievals from L-band (1.4 GHz) aircraft data gathered during the Southern Great Plains '97 (SGP97) experiment. A simplified version of MICRO-SWEAT, a soil vegetation atmosphere transfer (SVAT) scheme coupled with a microwave emission model, was used as the retrieval algorithm. Estimates of the optical depth of the vegetation, the parameter that describes the effect of the vegetation on microwave emission, were obtained by calibrating this retrieval algorithm against measurements of soil moisture at 15 field sites. A significant relationship was found between the optical depth so obtained and the observed NDVI at these sites, although this relationship changed with the resolution of the microwave brightness temperature observations used. Soil-moisture estimates made with the retrieval algorithm using the empirical relationship between optical depth and NDVI applied at two additional sites not used in the calibration show good agreement with field measurements. Keywords: NDVI, soil moisture, passive microwave, SGP97

scholarly journals Drought Monitoring for Washington State: Indicators and Applications

2011 ◽  
Vol 12 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Shraddhanand Shukla ◽  
Anne C. Steinemann ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Standardized Precipitation Index ◽  
Land Surface Model ◽  
Washington State ◽  
Drought Monitoring ◽  
Surface Model ◽  
Drought Conditions ◽  
The Standardized Precipitation Index

Abstract A drought monitoring system (DMS) can help to detect and characterize drought conditions and reduce adverse drought impacts. The authors evaluate how a DMS for Washington State, based on a land surface model (LSM), would perform. The LSM represents current soil moisture (SM), snow water equivalent (SWE), and runoff over the state. The DMS incorporates the standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture percentile (SMP) taken from the LSM. Four historical drought events (1976–77, 1987–89, 2000–01, and 2004–05) are constructed using DMS indicators of SPI/SRI-3, SPI/SRI-6, SPI/SRI-12, SPI/SRI-24, SPI/SRI-36, and SMP, with monthly updates, in each of the state’s 62 Water Resource Inventory Areas (WRIAs). The authors also compare drought triggers based on DMS indicators with the evolution of drought conditions and management decisions during the four droughts. The results show that the DMS would have detected the onset and recovery of drought conditions, in many cases, up to four months before state declarations.

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