Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS

Champagne, Catherine; Davidson, Andrew; Cherneski, Patrick; L’Heureux, Jessika; Hadwen, Trevor

doi:10.1175/jhm-d-14-0039.1

Cited by 39 publications

(21 citation statements)

References 25 publications

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“…Agriculture and Agri-Food Canada (AAFC) has developed a system to integrate multi-sensor soil moisture data (through data intercalibration), therefore allowing the seamless use of AMSR, SMOS and SMAP for agroclimate risk monitoring and reporting [75]. The feasibility of detecting vegetation drought with ASCAT-derived soil moisture was reported in a recent study [76].…”

Section: Operational Retrievals Of Ssm From Eomentioning

confidence: 99%

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

et al. 2018

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Abstract:Global information on the spatio-temporal variation of parameters driving the Earth's terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen.

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Section: Operational Retrievals Of Ssm From Eomentioning

confidence: 99%

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

et al. 2018

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“…Two general approaches have been used to estimate drought severity using satellite soil moisture data sets. The first set of approaches use a baseline of historical conditions to define statistical characteristics of soil moisture for each location to develop a relative indicator of drought severity [10][11][12]. Another approach to involves using the field capacity, wilting point or the available water holding capacity of soils to determine water storage in a particular soil [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Another approach to involves using the field capacity, wilting point or the available water holding capacity of soils to determine water storage in a particular soil [13,14]. A key limitation to the first approach is the lack of historical satellite soil moisture data to define these baseline statistical conditions [10]. A limitation of the second approach is the need to define soil characteristics which are not often available at a suitable spatial scale and which can be statistically incompatible with the satellite data sets.…”

Section: Introductionmentioning

confidence: 99%

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

et al. 2019

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Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)'s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set's sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X 2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X 2 = 89 on Day of Year 129) and then the RDPS (peak X 2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18-20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10-15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

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“…Drought, crop monitoring, and yield forecasting are a particularly important application set for the new SMAP data (Kumar et al 2014;Champagne et al 2015;Das et al 2014, manuscript submitted to J. Hydrometeor.…”

Section: Introductionmentioning

confidence: 99%

Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture

McNally

Husak

Brown

et al. 2015

Journal of Hydrometeorology

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The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.18-resolution, ;10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

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Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS

Cited by 39 publications

References 25 publications

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture

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