Abstract
Rice cultivation is one of the largest users of the world’s freshwater resources. The contribution of remote sensing observations for identifying the conditions under which rice is cultivated, particularly throughout the growing season, can be instrumental for water, and crop management. Data from different remote sensing platforms are being used in agriculture, namely to detecting anomalies in crops. This is attempted by calculating vegetation indices (VI) that are based on different vegetation reflectance bands, especially those that rely on the Red, Green, and near-infrared bands, such as the Normalised Difference Vegetation Index (NDVI) or the Green Normalised Difference Vegetation Index (GNDVI). However, particular features of different crops and growing conditions justify that some indices are more adequate than others on a case-to-case basis, according to the different vegetation’s spectral signatures. In recent years, a vegetation index related to the Red Edge reflectance band, the Normalised Difference Red Edge (NDRE) has shown potential to be used as a tool to support agricultural management practices; this edge band, by taking a transition position, is very sensitive to changes in vegetation properties. This work, focusing on the rice crop and the application of different irrigation practices, explores the capability of several VIs calculated from different reflectance bands to detect variability, at the plot scale, in rice cultivation in the Lower Mondego region (Portugal). The remote sensing data were obtained from satellite Sentinel-2A imagery and using a multispectral camera mounted on an Unmanned Aerial System (UAS). By comparing several vegetation indices, we found that NDRE is particularly useful for identifying non-homogeneities in irrigation and crop growth in rice fields. Since few satellite sensors are sensible in the Red Edge band and none has the spatial resolution offered by UAS, this study explores the potential of UAS to be used as a useful support information tool in rice farming and precision agriculture, regarding irrigation, and agronomic management.
Highlights
Rice is an important cereal crop that plays a critical role in global food security and sustainable development, feeding more people in the world than any other crop (e.g., Cantrell and Reeves, 2002; Yu et al, 2002; Nguyen and Ferrero, 2006; Normile, 2008)
Gu et al (2007) found that Normalized Difference Water Index (NDWI) values exhibited a quicker response to drought conditions than Normalised Difference Vegetation Index (NDVI), a study that led to the testing of NDWI as a drought indicator (Gu et al, 2008)
This study explores the role of remote sensing (RS) as a tool to better characterize, e.g., rice crop responses and water requirements, and to support water management and agronomic decisions, namely in precision agriculture
Summary
Rice is an important cereal crop that plays a critical role in global food security and sustainable development, feeding more people in the world than any other crop (e.g., Cantrell and Reeves, 2002; Yu et al, 2002; Nguyen and Ferrero, 2006; Normile, 2008). Since food security problems persist in many areas of the world, robust, and reliable tools that can be used to improve water and crop management and for mapping and the early forecasting of rice yields are very important. Reliable and timely estimates of rice crop production areas and yields are essential for providing information for planners and decision makers to formulate policies in the case of shortfall or surplus (e.g., Mosleh et al, 2015). Whereas RS has the potential to contribute overall to these goals in a variety of forms (we recall all advances in this research area and the applications in agriculture that have boosted in recent years), at present, the understanding of the potential of such contributions to rice cropping lags behind other crops
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