scholarly journals A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

2020 ◽  
Vol 13 (1) ◽  
pp. 122
Author(s):  
Liangliang Tao ◽  
Dongryeol Ryu ◽  
Andrew Western ◽  
Dale Boyd
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Resource Assessment ◽  
Remotely Sensed Data ◽  
Australian Water ◽  
Spatial And Seasonal Variation ◽  
Dryness Index ◽  
Maximum Water ◽  
Soil Moisture Monitoring

The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

scholarly journals Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

2020 ◽  
Vol 21 (11) ◽  
pp. 2537-2549
Author(s):  
Trent W. Ford ◽  
Steven M. Quiring ◽  
Chen Zhao ◽  
Zachary T. Leasor ◽  
Christian Landry
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Model ◽  
National Scale ◽  
Model Soil ◽  
Soil Moisture Data ◽  
Spatial Coverage ◽  
Soil Moisture Monitoring

AbstractSoil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

scholarly journals DROUGHT MONITORING IN SHANDONG PROVINCE BASED ON MODIFIED TEMPERATURE-VEGETATION-SOIL MOISTURE DRYNESS INDEX (TVMDI)

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 911-916
Author(s):  
H. Wan ◽  
Z. D. Wang ◽  
P. Guo ◽  
B. Wang ◽  
X. C. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Shandong Province ◽  
Severe Drought ◽  
Average Correlation ◽  
Shandong Peninsula ◽  
Wide Range ◽  
Dryness Index

Abstract. Drought is one of the frequent natural disasters in Shandong province, which is characterized by high frequency and wide range. In response to frequent droughts that are not monitored in time, monitoring the changes of drought is of great significance to agricultural production and social development. This study used the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) model, combined with the optical MODIS land surface temperature, vegetation index, surface albedo data and microwave FY-3B soil moisture data, to monitor the drought of Shandong province in 2016. The precipitation and temperature data of weather station were used to validate the monitoring results. The results show that, in 2016, the drought in Shandong province mainly occurred in winter and spring, and the drought in summer was alleviated. From the perspective of space, the northern Shandong and the Shandong peninsula areas are relatively humid with less drought time, while the local areas in the central and southern Shandong province suffer from severe drought with longer drought time. From the perspective of correlation with meteorological factors, the average correlation coefficient between TVMDI and precipitation can reach 0.45, and the average correlation coefficient between TVMDI and temperature can reach 0.63.

scholarly journals Developing a drought-monitoring index for the contiguous US using SMAP

2018 ◽  
Vol 22 (12) ◽  
pp. 6611-6626 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Near Surface ◽  
Index Maps

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

scholarly journals Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation

2018 ◽  
Vol 19 (4) ◽  
pp. 727-741 ◽  
Author(s):  
Randal D. Koster ◽  
Qing Liu ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Model Calibration ◽  
Remotely Sensed ◽  
Surface Model ◽  
Remotely Sensed Data ◽  
Satellite Mission ◽  
Hydrological Simulation ◽  
Near Surface

Abstract The assimilation of remotely sensed soil moisture information into a land surface model has been shown in past studies to contribute accuracy to the simulated hydrological variables. Remotely sensed data, however, can also be used to improve the model itself through the calibration of the model’s parameters, and this can also increase the accuracy of model products. Here, data provided by the Soil Moisture Active Passive (SMAP) satellite mission are applied to the land surface component of the NASA GEOS Earth system model using both data assimilation and model calibration in order to quantify the relative degrees to which each strategy improves the estimation of near-surface soil moisture and streamflow. The two approaches show significant complementarity in their ability to extract useful information from the SMAP data record. Data assimilation reduces the ubRMSE (the RMSE after removing the long-term bias) of soil moisture estimates and improves the timing of streamflow variations, whereas model calibration reduces the model biases in both soil moisture and streamflow. While both approaches lead to an improved timing of simulated soil moisture, these contributions are largely independent; joint use of both approaches provides the highest soil moisture simulation accuracy.

scholarly journals Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques

2021 ◽  
Vol 13 (9) ◽  
pp. 1667
Author(s):  
Mai Son Le ◽  
Yuei-An Liou
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Meteorological Data ◽  
Growing Season ◽  
Heat Index ◽  
Landsat 8 ◽  
Surface Moisture ◽  
Dryness Index ◽  
Spatio Temporal ◽  
Remote Sensing Techniques

The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting.

scholarly journals Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

2021 ◽  
Vol 13 (9) ◽  
pp. 1778
Author(s):  
Soo-Jin Lee ◽  
Nari Kim ◽  
Yangwon Lee
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Crop Yield ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The State ◽  
Agricultural Drought ◽  
Drought Monitoring

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

scholarly journals A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

2007 ◽  
Vol 4 (1) ◽  
pp. 1-33 ◽  
Author(s):  
B. P. Weissling ◽  
H. Xie ◽  
K. E. Murray
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Streamflow Prediction ◽  
Remotely Sensed Data ◽  
Moisture Condition ◽  
Soil Moisture Condition

Abstract. Soil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite). This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI), on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS) curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available.

Hyper-resolution land surface modeling enables 30-m SMAP-based soil moisture at continental scales

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Nathaniel W. Chaney ◽  
Hylke E. Beck ◽  
Ming Pan ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Field Scale ◽  
Remotely Sensed Data

<p>Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Microwave-based satellite remote sensing offers unique opportunities for the large-scale monitoring of soil moisture at frequent temporal intervals. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. Several downscaling techniques based on high-resolution remotely sensed data proxies have been proposed (1 km to 100 m). Although these techniques yield aesthetically pleasing maps, by neglecting how the water and energy fluxes physically interact with the landscape, these approaches often fail to provide soil moisture estimates that are hydrologically consistent.</p><p>This work introduces a state-of-the-art framework that combines a process-based hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution brightness temperature to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). We demonstrate this framework by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission and subsequently merging the HydroBlocks-RTM and the SMAP L3-enhanced brightness temperature at the HRU scale. This allows for hydrologically consistent SMAP-based soil moisture retrievals at an unprecedented 30-m spatial resolution over continental domains. </p><p>We applied this framework to obtain 30-m SMAP-based soil moisture retrievals over the contiguous United States (2015-2018). When evaluated against sparse and dense in-situ soil moisture networks, the 30-m soil moisture retrievals showed substantial improvements in performance at field and watershed scales, outperforming both the SMAP L3-enhanced and the SMAP L4 soil moisture products. This work leads the way towards hydrologically consistent field-scale soil moisture retrievals and highlights the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. </p>

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