Satellite soil moisture for yield prediction in water limited regions&#160;
Authors: Vreugdenhil Mariette , Pfeil Isabella , Brocca Luca , Camici Stefania , Enenkel Markus , Wagner Wolfgang 
Published: March 2021

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&lt;p&gt;Accurate and reliable&amp;#160;early warning systems can&amp;#160;support&amp;#160;anticipatory&amp;#160;disaster risk financing&amp;#160;which&amp;#160;can be more cost effective than post-disaster&amp;#160;emergency response.&amp;#160;One of the challenges in&amp;#160;anticipatory&amp;#160;disaster risk financing is basis risk, as a result of&amp;#160;data and&amp;#160;model uncertainty.&amp;#160;The increasing availability of Earth Observation&amp;#160;(EO)&amp;#160;data provides the opportunity to&amp;#160;develop shadow models or include different variables in early warning systems&amp;#160;and weather index insurance. Especially of interest is the early indication of&amp;#160;climate impacts on agricultural production.&amp;#160;Traditionally, crop and yield prediction models&amp;#160;use meteorological data such as precipitation and temperature, or&amp;#160;optical based indicators&amp;#160;such as&amp;#160;Normalized&amp;#160;Difference&amp;#160;Vegetation&amp;#160;Index (NDVI), for yield prediction.&amp;#160;&amp;#160;In recent years, soil moisture has gained popularity for yield prediction as it controls the water availability for plants.&amp;#160;&amp;#160;&lt;/p&gt;
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&lt;p&gt;Here, we will present the use of different satellite-based rainfall and soil moisture products, in combination with NDVI, to develop a yield deficiency indicator over two water limited regions. An analysis for Senegal and Morocco is performed at the national level using yield data of four major crops from the Food and Agriculture Organization of the United Nations. Freely available EO datasets for rainfall, soil moisture, root zone soil moisture and NDVI were used. All datasets were spatially resampled to a 0.1&amp;#176; grid, temporally aggregated to monthly anomalies and finally detrended and standardized. First, regression analysis with yearly yield was performed per EO dataset for single months. For this, EO datasets where aggregated over areas where the specific crop was grown. Secondly, based on these results multiple linear regression was performed using the months and variables with the highest explanatory power. The multiple linear regression was used to provide spatially varying yield predictions by trading time for space. The spatial predictions were validated using sub-national yield data from Senegal.&amp;#160;&amp;#160;&lt;/p&gt;
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&lt;p&gt;The analysis&amp;#160;demonstrates the added-value of&amp;#160;satellite&amp;#160;soil&amp;#160;moisture for&amp;#160;early yield prediction.&amp;#160;Both in Senegal and Morocco&amp;#160;rainfall and&amp;#160;soil moisture&amp;#160;showed&amp;#160;a high predictive&amp;#160;skill&amp;#160;early in the growing season: negative early season soil moisture anomalies often lead to low yield. NDVI&amp;#160;showed&amp;#160;more predictive power later in the growing season.&amp;#160;For example, in Morocco soil moisture at the start of the season can already explain 56% of the variability in yield. NDVI&amp;#160;can explain 80% of the yield, however this is at the&amp;#160;end of the growing season.&amp;#160;Combining&amp;#160;anomalies of the&amp;#160;optimal months&amp;#160;based on&amp;#160;the&amp;#160;different variables in multiple linear regression&amp;#160;improved yield prediction. Again,&amp;#160;including NDVI&amp;#160;led&amp;#160;to higher predictive power, at the cost of early warning.&amp;#160;&amp;#160;This analysis shows very clearly that soil moisture&amp;#160;can be&amp;#160;a valuable tool&amp;#160;for&amp;#160;anticipatory&amp;#160;drought risk financing and early warning systems.&amp;#160;&lt;/p&gt;
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