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

Abstract

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.

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

“…Detailed and accurate information on the spatiotemporal distribution of soil moisture is important for numerous applications, such as monitoring of drought [1][2][3] and crop irrigation demands [4][5][6] ; mapping antecedent conditions that trigger wildfires 7,8 , landslides 9,10 , and flooding 11,12 ; and quantifying water, energy, and carbon fluxes between the land and atmosphere [13][14][15] . Depending on the landscape heterogeneity, such physical processes can occur at the 1-100 m spatial scale, at which in-situ sensors could provide detailed information.…”

“…Given its spatial detail, the SMAP-HB dataset will be useful for solving many physical processes and application at spatial scales that so far have been unresolved. These applications include mapping and understanding crop irrigation demands 4,6 , farmer decision making and planting dates 65 , drought impacts [1][2][3] ; and mapping of antecedent soil moisture conditions can help estimate the susceptibility to wildfires 7,8 , landslides 9,10 , flooding, and waterlogging conditions 11,12 . Detailed soil moisture information can aid and improve the quantification of biogeochemical cycles in wetlands and riparian zones 66 , as well as better inform the environmental conditions that facilitate epidemic outbreaks of, for example, West Nile virus 67 , malaria 68 , and locust 69 .…”

Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau

“…It not only shows great significance in the hydrological, bioecological, and biogeochemical cycles [1][2][3], but also plays an active role in hydrological processes such as precipitation, runoff, infiltration, and evapotranspiration [4,5]. Operational SM products have been applied to many research fields, including global climate change [6], agricultural applications [7], drought and flood disaster monitoring [8,9], and weather forecasting [10,11].…”

Downscaling ESA CCI Soil Moisture Based on Soil and Vegetation Component Temperatures Derived From MODIS Data

“…G. Hu is with the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China (e-mail:hugc@aircas.ac.cn) cycle [1,2]. Moreover, soil moisture regulates soil water flux by influencing infiltration and surface runoff [3].…”