Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of –0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.
<p>The sustainability of crop production regarding different climate change scenarios will compromise actors and activities involved in agri-food systems. Furthermore, sustainable development was defined by the World Commission on Environment and Development as the ability to meet present demands without compromising the needs of future generations. In parallel, according to the Food and Agriculture Organization (FAO), land evaluation is the process of projecting land use potential based on its characteristics, and it has been the principal approach used worldwide to manage land use planning. Its use today is required due to changing needs and pressures from decision-making policies or agricultural market tendencies among others, so a rational use of natural land is a crucial goal for economic development. However, future climate change scenarios will modify the actual crop development conditions and must be tackled.</p><p>This paper presents two case studies at the river basin scale to determine the Land Use Suitability (LUS) analysis that is performed according to the FAO framework, thus, areas that are the most suitable for crops using GIS and multicriteria methodology that involves actual and future climatic conditions under different climate change scenarios, crop management practices and edaphological conditions for different crops. The tool developed generates a product that classifies areas suitable for a particular crop from a collection of maps and their corresponding thresholds. The approach involves standardizing the suitability maps, assigning relative importance weights to the suitability maps, and then combining the weights and the standardized suitability maps to obtain a suitability score.</p><p>In this paper, the wheat crop LUS at the J&#250;car River Basin (42,735 Km2, located in Spain) and the cotton LUS at the Pinios River Basin 11,000 km2, located in Greece) are evaluated. Once the LUS is estimated, a collection of yearly thematic maps over both river basins is ready for use by local stakeholders, regarding different climate change scenarios (RCP 4.5 and RCP 8.5).</p><p>These results are part of the EU Horizon 2020 project REXUS (<em>Managing Resilient Nexus Systems Through Participatory Systems Dynamics Modelling</em>), in which local stakeholders, from farmers to land use managers, are collecting and evaluating the information. Our final goal is to provide spatial information for future climate change scenarios that increase land-use knowledge and enhance decision-making policies.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.