Combined use of Sentinel SAR and optical data for soil moisture estimation

2020 ◽  
Author(s):  
Giulia Graldi ◽  
Simone Bignotti ◽  
Marco Bezzi ◽  
Alfonso Vitti
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Case Studies ◽  
Agricultural Land ◽  
Physiological State ◽  
European Space Agency ◽  
Irrigation Scheduling ◽  
Optical Data ◽  
Agricultural Land Use ◽  
Mountainous Area

<p>This work investigates the performance of two soil moisture retrieval methods using optical and radar satellite data. The study was conducted in areas with predominant agricultural land use since soil moisture is one of the parameters of interest in a wider study for water resource optimization in agricultural practices such as irrigation scheduling.<br>The two methods considered are based on the identification of changes in the investigated parameter between two acquisition dates. The implemented methods have been applied to study areas characterized by different orographic complexity and land use heterogeneity. Data from the European Space Agency (ESA) Sentinel 1 and Sentinel 2 missions were used, and results were validated with field measurements from the International Soil Moisture Network (ISMN).<br>At first, the methods were applied in a mountainous area of an irrigation consortium in Trentino (Italy), where the results pointed out the complexity of the study and the limitations of the current models in these contexts. Factors such as orographic complexity, type and physiological state of crops make the reduction of SAR data particularly complex to model.<br>The methods were then tested in a simpler orographic context such as that of the Po Valley in Bologna (Italy), also characterized by agricultural land use.<br>Finally, the methods were applied in a lowland with agricultural vocation located in Spain, for which an extended archive of soil moisture measurements distributed by the ISMN is available. In this context, the models were analyzed and were evaluated both functional and parametric adjustments of the models on the basis of the previous case studies.<br>Some of the results obtained are of high quality, while others highlight the complexity of the problem faced and the need for further investigation: increasing the number of case studies and using optical or SAR vegetation index different from the mainly used NDVI, could enhanced the models used for soil moisture retrieval.</p>

scholarly journals Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

2020 ◽  
Vol 12 (18) ◽  
pp. 2919
Author(s):  
Ann-Kathrin Holtgrave ◽  
Norbert Röder ◽  
Andrea Ackermann ◽  
Stefan Erasmi ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Optical Data ◽  
Agricultural Land Use ◽  
Land Use Type ◽  
Auxiliary Data ◽  
Land Use Types ◽  
Sentinel 2

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

Stock change of soil moisture under crop cultivation: A relevant and low cost indicator to assess hydrological performance of agricultural land use systems in Tigray, northern Ethiopia.

2020 ◽  
Author(s):  
Richard Kraaijvanger
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Agricultural Land ◽  
Root Zone ◽  
Low Cost ◽  
Crop Productivity ◽  
Agricultural Land Use ◽  
Stock Change

<p>In the highlands of Tigray both crop yield and soil erosion are important concerns. At the same time the impact of climate change is felt in the form of delayed and more erratic rains. Different adaptation strategies are proposed to increase resilience. The successful implementation of most of these strategies, like for example, agroforestry, conservation tillage and water harvesting, heavily relies on improved infiltration and the amount of water stored in the root zone. In this presentation the water storage in the root zone is discussed in relation to crop productivity and hydrological performance of the local (agricultural) land use system. For this purpose measurements of (gravimetric) soil moisture content, taken at different depths in the root zone and at regular time intervals during four growing seasons in the period 2010-2013, were considered. In total 43 sites were involved, which were measured for one up to three years. In addition to soil moisture content, at selected sites also bulk density, saturation, field capacity and wilting point were determined. On the basis of the data collected, site-specific changes in soil moisture budgets were analyzed and trends observed were related to crop productivity and hydrological parameters (like rainfall and evapotranspiration). First outcomes pointed to a relatively rapid increase of soil moisture stock at the start of the growing season, followed by a more or less stable level, and ending at crop maturation with a very rapid decrease. Typical figures for gravimetric moisture content at the stable level were between 25 and 30 %. Soil depth was in most cases shallow (around 40 cm) and likely limiting moisture storage capacity. Assuming that at the start of the stable phase rainfall still is exceeding evapotranspiration, this then will point to a relatively high risk for run off at this stage. Stock change of soil moisture as such appears a relevant and low cost indicator to assess hydrological performance of land use systems in terms of infiltration capacity and soil moisture availability. In line with that, analysis of stock change of soil moisture might provide relevant clues for designing and optimizing effective land management strategies that successfully deal with erosion hazard and result in a more resilient and sustainable production of food crops.</p>

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