scholarly journals Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1265 ◽  
Author(s):  
Nazmus Sazib ◽  
Iliana Mladenova ◽  
John Bolten
Keyword(s):  
Soil Moisture ◽  
Google Earth ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Drought Duration ◽  
Web Based ◽  
Soil Moisture Data ◽  
Global Soil ◽  
Google Earth Engine ◽  
The Web

Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them.

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>

Research on the Variation in Soil Moisture in Arid Regions in Northern China Based on AMSR-E

2013 ◽  
Vol 781-784 ◽  
pp. 2353-2356
Author(s):  
Jing Wen Xu ◽  
Shuang Liu ◽  
Jun Fang Zhao ◽  
Jin Lun Han ◽  
Peng Wang
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Arid Regions ◽  
Microwave Remote Sensing ◽  
Northern China ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Study Region ◽  
Near Surface ◽  
Soil Moisture Data

Soil moisture plays an important role in agricultural drought monitoring. However, the traditional observed soil moisture data from meteorological stations has not been able to meet the demands for large-scale drought monitoring temporally and spatially. The microwave remote sensing is a new effective way for obtaining near surface soil moisture. The temporal and spatial variation in soil moisture in arid regions in northern China is examined based on the soil moisture data retrieved from AMSR-E (Advanced Microwave Scanning Radiometer-EOS), with which the observed soil moisture data at 10 cm depth from meteorological stations are compared. The results show that there is a small change in soil moisture with seasons for the central and west areas, while a large change for the east areas of the study region and the soil moisture in summer and autumn is greater than that in spring and winter. And that it decreases from southeast to northwest spatially, as is agree with the spatial distribution of precipitation in the study area. Moreover, there is a great difference in spatial distribution between the soil moisture retrieved from AMSR-E and the observed soil moisture data from meteorological stations for the central and west areas, while a small difference for the east areas of the study region.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

&lt;p&gt;The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.&lt;/p&gt;&lt;p&gt;Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25&amp;#176;) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.&lt;/p&gt;&lt;p&gt;This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).&lt;/p&gt;

scholarly journals Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

2020 ◽  
pp. 100458
Author(s):  
Michelle Li Ern Ang ◽  
Dirk Arts ◽  
Danielle Crawford ◽  
Bonifacio V. Labatos ◽  
Khanh Duc Ngo ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Google Earth ◽  
Cover Time ◽  
Web Based ◽  
Series Analysis ◽  
Google Earth Engine
Sign in / Sign up

Export Citation Format

Share Document