scholarly journals Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1726 ◽  
Author(s):  
Yizhi Han ◽  
Xiaojing Bai ◽  
Wei Shao ◽  
Jie Wang
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Vegetation Index ◽  
Coupled Model ◽  
Agricultural Drought ◽  
Agricultural Fields ◽  
Backscattering Coefficient ◽  
Modis Data ◽  
Vegetation Parameters ◽  
Scattering Contribution

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

scholarly journals Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

2013 ◽  
Vol 10 (6) ◽  
pp. 7963-7997 ◽  
Author(s):  
A. McNally ◽  
C. Funk ◽  
G. J. Husak ◽  
J. Michaelsen ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Drought ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
The Relationship ◽  
African Sahel

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

scholarly journals A pan-African high-resolution drought index dataset

2019 ◽  
Author(s):  
Jian Peng ◽  
Simon Dadson ◽  
Feyera Hirpa ◽  
Ellen Dyer ◽  
Thomas Lees ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Root Zone ◽  
Environmental Data ◽  
Climatic Research Unit ◽  
Drought Vulnerability ◽  
Coarse Resolution ◽  
Mass Migration

Abstract. Droughts in Africa cause severe problems such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security over Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectorial perspective that includes crops, hydrological systems, rangeland, and environmental systems. Such assessments are essential for policy makers, their advisors, and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time-Series (TS) datasets, and Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project, as well as with root zone soil moisture modelled by GLEAM. Agreement found between coarse resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information to study drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) with link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)

scholarly journals The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

2020 ◽  
Vol 12 (9) ◽  
pp. 1358 ◽  
Author(s):  
Shuai Huang ◽  
Jianli Ding ◽  
Bohua Liu ◽  
Xiangyu Ge ◽  
Jinjie Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Ratio Model ◽  
Retrieval Model ◽  
Backscattering Coefficient ◽  
Strong Control

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

scholarly journals High-resolution simulation and validation of soil moisture in the arid region of Northwest China

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xianyong Meng ◽  
Hao Wang ◽  
Ji Chen ◽  
Mingxiang Yang ◽  
Zhihua Pan
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Resolution ◽  
Arid Region ◽  
Arid Regions ◽  
Northwest China ◽  
Agricultural Drought ◽  
Data Set ◽  
Study Region ◽  
Community Land Model

AbstractSoil moisture plays an important role in land-atmosphere interactions, agricultural drought monitoring, and water resource management, particularly across arid regions. However, it is challenging to simulate soil moisture of high spatial resolution and to evaluate soil moisture at fine spatial resolution in arid regions in Northwest China due to considerable uncertainties in forcing data and limited in situ measurements. Then, the data set was used to produce the 1 km high-resolution atmospheric forcing datasets and to drive the Community Land Model version 3.5 (CLM3.5) for simulating spatiotemporally continuous surface soil moisture. The capabilities of soil moisture simulation using CLM3.5 forced by the XJLDAS-driven field were validated against data obtained at three soil layers (0–10, 0–20, and 0–50 cm) from 54 soil moisture stations in Xinjiang. Results show that the simulated soil moisture agreed well with the observations [CORR > 0.952], and the intra-annual soil moisture in Xinjiang gradually increased during May through August. The main factors that affect changes in soil moisture across the study region were precipitation and snowmelt. The overall finding of this study is that an XJLDAS, high-resolution forcing data driven CLM3.5 can be used to generate accurate and continuous soil moisture of high resolution (1km) in Xinjiang. This study can help understand the spatiotemporal features of the soil moisture, and provide important input for hydrological studies and agricultural water resources management over the arid region.

scholarly journals The Performance of Multiple Model-Simulated Soil Moisture Datasets Relative to ECV Satellite Data in China

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1384 ◽  
Author(s):  
Wenkui Bai ◽  
Xiling Gu ◽  
Shenlin Li ◽  
Yihan Tang ◽  
Yanhu He ◽  
...  
Keyword(s):  
Soil Moisture ◽  
South China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coupled Model ◽  
Temporal Correlation ◽  
Multiple Model ◽  
Model Intercomparison ◽  
Reanalysis Dataset ◽  
Intercomparison Project

Reliability and accuracy of soil moisture datasets are essential for understanding changes in regional climate such as precipitation and temperature. Soil moisture datasets from the Essential Climate Variable (ECV), the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), the Global Land Data Assimilation System (GLDAS), and reanalysis products are widely used. These datasets generated by different techniques are compared in a common framework over China in this study. The comparison focuses on four aspects: spatial pattern, temporal correlation, long-term trend, and the relationships with precipitation and the Normalized Difference Vegetation Index (NDVI). The results indicate that all soil moisture datasets reach a good agreement on the spatial patterns of wet and dry soil. These patterns are also consistent with that of precipitation. However, there are considerable discrepancies in the absolute values of soil moisture among these datasets. In terms of unbiased Root-Mean-Square Difference (unRMSE, i.e., removing the differences in absolute values), all modeled datasets obtain performances comparable with ECV observations. Our results also suggest that a multi-model ensemble of soil moisture datasets can improve the representation of soil moisture conditions. The optimal dataset from which the wetting/drying trends in soil moisture have the highest consistency in terms of changes in precipitation and NDVI varies by season. Specifically, in spring, CMIP5 in northwest China shows that the trends in soil moisture are consistent with the changes in precipitation and NDVI. In summer, ECV presents the most identical performance compared to the changes in precipitation and NDVI. In autumn, GLDAS and Reanalysis have better performance in south China and parts of north China. In winter, GLDAS performs the best in the east of south China, followed by the Reanalysis dataset. These discrepancies among the datasets present various changes in different regions, which should be well noted and discussed before use.

scholarly journals High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type

2021 ◽  
Vol 13 (6) ◽  
pp. 1112
Author(s):  
Vivien-Georgiana Stefan ◽  
Gianfranco Indrio ◽  
Maria-José Escorihuela ◽  
Pere Quintana-Seguí ◽  
Josep Maria Villar
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Cover ◽  
Root Zone ◽  
Agricultural Drought ◽  
Intermediate Step ◽  
Land Cover Type ◽  
Simple Method ◽  
Cover Type ◽  
Spatial Coverage

Root-zone soil moisture (RZSM) plays a key role for most water and energy budgets, as it is particularly relevant in controlling plant transpiration and hydraulic redistribution. RZSM data is needed for a variety of different applications, such as forecasting crop yields, improving flood predictions and monitoring agricultural drought, among others. Remote sensing provides surface soil moisture (SSM) retrievals, whose key advantage is the large spatial coverage on a systematic basis. This study tests a simple method to retrieve RZSM estimates from high-resolution SSM derived from SMAP (Soil Moisture Active Passive). A recursive exponential filter using a time constant τ is calibrated per land cover type, which uses as an intermediate step a long-term ISBA-DIF (Interaction Soil Biosphere Atmosphere—Diffusion scheme) dataset over an area located in Catalonia, NE of Spain. The τ values thus obtained are then used as an input to the same recursive exponential filter, to derive 1 km resolution RZSM estimates from 1 km SMAP SSM, which are obtained from the original data by downscaling to a 1 km resolution, through the DISPATCH (DISaggregation based on a Physical and Theoretical scale CHange) methodology. The results are then validated with scaled in situ observations at different depths, over two different areas, one representative of rainfed crops, and the other of irrigated crops. In general, the estimates agree well with the observations over the rainfed crops, especially at a 10 cm and 25 cm depth. Nash–Sutcliffe (NS) scores ranging between 0.33 and 0.58, and between 0.37 and 0.56 have been found, respectively. Correlation coefficients for these depths are high, between 0.76 and 0.91 (10 cm), and between 0.71 and 0.90 (25 cm). For the irrigated sites, results are poorer (partly due to the extremely high heterogeneity present), with NS scores ranging between −2.57 and 0.16, and correlations ranging between −0.56 and 0.48 at 25 cm. Given the strong correlations and NS scores found in the surface, the sensitivity of the filter to different τ values was investigated. For the rainfed site, it was found, as expected, with increasing τ, increasing NS and correlations with the deeper layers, suggesting a better coupling. Nevertheless, a strong correlation with the surface (5 cm) or shallower depths (10 cm) observed over certain sites indicates a certain lack of skill of the filter to represent processes which occur at lower levels in the SM column. All in all, a calibration accounting for the vegetation was shown to be an adequate methodology in applying the recursive exponential filter to derive the RZSM estimates over large areas. Nevertheless, the relative shallow surface at which the estimates correlate in some cases seem to indicate that an effect of evapotranspiration in the profile is not well captured by the filter.

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.

The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture

2020 ◽  
Author(s):  
Jianxiu Qiu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Large Scale ◽  
Vegetation Index ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Biological Variables ◽  
Research Activities

<p>The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.</p>

scholarly journals Forest Cover Assessment in Tuv province of Mongolia Using MODIS and Landsat-7 ETM+

2011 ◽  
pp. 51-60
Author(s):  
R Tsolmon ◽  
K Yanagida ◽  
M Erdenetuya ◽  
L Ochirhuyag
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Forest Cover ◽  
Agricultural Fields ◽  
Forest Cover Change ◽  
Vegetation Monitoring ◽  
Mixed Pixel ◽  
Modis Data ◽  
Landsat 7 ◽  
Academy Of Sciences

The study aimed at determining the relative proportions of forest cover and other components in a mixed pixel. For this purpose a linear mixing model was used for the derivation of a land cover classification map in two study areas of Tuv province, Mongolia. Main types of forest cover change are forests to burn scars and agricultural fields in the study areas. In this paper, two reflective channels 3 and 4 of LANDSAT ETM+ and reflective channels land 2 of MODIS data was used to map five and four land components respectively. Clouds proportion was derived using MODIS data. A synergy between high-resolution MODIS and Landsat ETM+ data may greatly enhance the operational success of satellite based vegetation monitoring, in providing multi-spectral data on parameters of the environment.DOI: http://dx.doi.org/10.5564/pmas.v0i4.40Proceedings of the Mongolian Academy of Sciences 2007 No 4 pp.50-59

scholarly journals Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain

2020 ◽  
Vol 20 (1) ◽  
pp. 21-33 ◽  
Author(s):  
María del Pilar Jiménez-Donaire ◽  
Ana Tarquis ◽  
Juan Vicente Giráldez
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Crop Damage ◽  
Rainfall Regime ◽  
Type I ◽  
Drought Prediction ◽  
Mediterranean Countries ◽  
Four Levels

Abstract. Drought prediction is crucial, especially where the rainfall regime is irregular, such as in Mediterranean countries. A new combined drought indicator (CDI) integrating rainfall, soil moisture and vegetation dynamics is proposed. Standardized precipitation index (SPI) is used for evaluating rainfall trends. A bucket-type soil moisture model is employed for keeping track of soil moisture and calculating anomalies, and, finally, satellite-based normalized difference vegetation index (NDVI) data are used for monitoring vegetation response. The proposed CDI has four levels, at increasing degrees of severity: watch, warning, alert type I and alert type II. This CDI was thus applied over the period 2003–2013 to five study sites, representative of the main grain-growing areas of SW Spain. The performance of the CDI levels was assessed by comparison with observed crop damage data. Observations show a good match between crop damage and the CDI. Important crop drought events in 2004–2005 and 2011–2012, distinguished by crop damage in between 70 % and 95 % of the total insured area, were correctly predicted by the proposed CDI in all five areas.

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