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Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

The digital elevation model based on SRTM is very convenient for a wide range of studies but requires correction due to the influence of forest vegetation. The present study was conducted to analyse the effect of boreal forests on altitudes, aspects and slopes calculated from the SRTM. A DEM based on topographic maps at 1:100 000 scale was used as a reference. The linear regression analysis showed low data correlation in forested areas. The presence of different types of forests and felling in the woods leads to a complex distribution of deviations from the SRTM. A simple correction method was proposed, using a forest mask, built according to Landsat, and forest heights indicated on the topographic maps. After correction, the correlation coefficient between the altitudes increased by 0.05–0.14, the share of matching aspects by 1–4% and the share of matching slopes by 2–8%.

Urban populations are expanding rapidly and so are cities. Remote sensing offers a convenient means of monitoring this expansion as it covers a period of 40 years in the case of the LANDSAT satellite. In some parts of the globe, this is probably the only viable means of monitoring due to the lack of other types of data. In order to monitor expansion, first, urban land has to be separated from other land-cover types. Although this can be done by standard classification processes, it is much more efficient to establish an urban index UI analogous to the widely used normalized difference vegetation index NDVI for vegetation. Existing efforts to establish such a UI are reviewed and compared in a common context. Following this, a novel, more efficient UI is introduced. The calculation of the new index is straightforward, based on combining the NDVI with the normalized difference built-up index. The results are promising as the index can efficiently segment urban areas, even in the presence of excessive bare land. The proposed method is evaluated on two test sites selected in different LANDSAT scenes. The new index is valid only for sensors with the same bands as those of LANDSAT.

Wheat yield estimation in this paper is based on the above ground biomass estimation. The model of above ground biomass estimation requires satellite data, which express the vegetation status. The model was applied in Russia where winter wheat is widely grown. The results showed a high accuracy in estimating winter wheat yield. The range of fractional differences between estimated and observed yields is between -0.40 and 0.50 in 2011, and 83% of the fractional differences are between -0.30 and 0.30. When this method is used in large areas, parameters calibration is crucial. We also summarize that in future study, high resolution images, meteorological factors retrieved by remote sensing data and more field observed data should be used to improve the method.