scholarly journals A pixel-based indicator of upland crop waterlogging using remote sensing soil moisture data

2019 ◽  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Moisture Data ◽  
Upland Crop

The Assimilation of Remote Sensing-Derived Soil Moisture Data into a Hydrological Model for the Mahanadi Basin, India

2019 ◽  
Vol 47 (8) ◽  
pp. 1357-1374 ◽  
Author(s):  
Soumya S. Behera ◽  
Bhaskar Ramchandra Nikam ◽  
Mukund S. Babel ◽  
Vaibhav Garg ◽  
Shiv Prasad Aggarwal
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Soil Moisture Data ◽  
Mahanadi Basin

scholarly journals Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 666 ◽  
Author(s):  
Lihua Xiong ◽  
Ling Zeng
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Objective Function ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Soil Moisture Data ◽  
Streamflow Data ◽  
The Impact ◽  
Hydrological Variables ◽  
Streamflow Simulation

With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration.

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2777
Author(s):  
Tao Cheng ◽  
Siyang Hong ◽  
Bensheng Huang ◽  
Jing Qiu ◽  
Bikui Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Pearson Correlation ◽  
Microwave Remote Sensing ◽  
Northeastern China ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Passive Microwave Remote Sensing ◽  
Soil Moisture Data

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

Drought monitoring in the Ebro basin: comparison of Soil Moisture and Vegetation anomalies

2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Ebro Basin ◽  
Soil Moisture Data

<p>Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.</p><p>Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 
</p><p>A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.</p><p>Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.</p><p>A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.</p><p>The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).</p><p>In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.</p>

On the calibration of an evaporation-based disaggregation method of SMOS soil moisture data

2020 ◽  
Author(s):  
Vivien Georgiana Stefan ◽  
Olivier Merlin ◽  
Beatriz Molero ◽  
Maria-Jose Escorihuela ◽  
Salah Er-Raki
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Linear Model ◽  
Land Surface ◽  
Vegetation Index ◽  
Correlation Coefficients ◽  
Nonlinear Behaviour ◽  
Soil Moisture Data ◽  
Nonlinear Mode

<h3>High resolution (HR) soil moisture estimates are needed by a range of agricultural and hydrological applications, considering it’s one of the drivers of evaporation, infiltration and runoff. Since the resolution of current remote sensing estimates (tens of kilometres) is insufficient for the majority of these applications, different downscaling techniques are used to improve the resolution. Amongst the existing methodologies, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) has been proven to accurately improve the resolution of SMOS (Soil Moisture Ocean Salinity) soil moisture data, by using a soil evaporative efficiency (SEE) model. SEE can be derived from remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI) data. DISPATCH uses two different SEE models: a temperature-based LST-driven model, and a soil moisture-based SMOS-driven model. This study aims at improving the robustness of the soil moisture-based SEE model, by testing different calibrations and models. Two SM-based SEE models, one linear and one nonlinear, are tested, each being calibrated from remote sensing data on a daily and on a multi-date basis. The approaches were implemented over two mixed dry and irrigated areas in Catalonia, Spain, and over a dry area in Morocco.  When looking at the two models in the daily calibration mode, the linear model performs better. Over the two areas in Spain, the correlation coefficients obtained with the linear model are 0.63 and 0.18 as opposed to 0.13 and -0.08, respectively. In Morocco, the correlation coefficients are roughly similar, 0.32 (linear mode) and 0.31 (nonlinear mode). The slopes of linear regression are also improved in the linear case, 0.44 and 0.88, as opposed to -0.14 and 0.11, for the Spanish sites. However, the best results were obtained in the case of the nonlinear model with an annual calibration. When comparing the linear and nonlinear models in the annual calibration mode, correlation coefficients are improved when using the nonlinear mode, from 0.13 and -0.08 to 0.78 and to 0.47 (Spanish site), and from 0.25 to 0.33 (Moroccan site). The slopes of linear regression are also improved, from 0.11 to 0.88, -0.14 to 1.15 (Spain) and from 0.53 to 0.74 (Morocco). The root mean square difference is generally low, ranging from 0.03 to 0.17 m3/m3. Considering several studies that report a strong nonlinear behaviour of the SEE with respect to SM, the nonlinear SM-based model in DISPATCH, combined with a multi-date calibration, is proven to give a significantly better performance, enhancing the robustness of the derived HR SM products.</h3>

scholarly journals The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

2018 ◽  
Vol 22 (11) ◽  
pp. 5889-5900 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernàndez-Garcia ◽  
Francesc Ferrer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Sprinkler Irrigation ◽  
Irrigation Scheduling ◽  
Time Domain Reflectometry ◽  
Rainfall Events ◽  
Soil Moisture Data

Abstract. Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1 km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.

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