This study investigates the efficacy of NDVI (Normalized Difference Vegetation Index), EOMI (Exogenous Organic Matter Index), and MNDWI (Modified Normalized Difference Water Index) in identifying application of manure in sandy soil farmlands. By leveraging Sentinel-2 multispectral imagery acquired over Turkish farmland in the Adana region, this study aims to assess the joint utilization of these indices to enhance detection methodologies within the specified geographic context.

Independent, large-scale assessment of agricultural practices can be useful for inventorying and certification purposes. Satellite Earth observation technology may represent a powerful tool to implement this application. In this paper we analyze the problem of identifying from NDVI sequences the type of inter-row management in vineyards, i.e. whether vegetation is allowed to develop in the inter-row space or this latter is tilled and soil remains exposed. Typical sequences are analyzed from Sentinel-2 acquisitions over a test site in northern Italy, and both supervised and unsupervised classification is applied to features extracted from time sequences. The accuracy levels reach high values only in some cases, but joint analysis of features and resulting accuracy levels is providing significant clues laying the basis for future improvement.

Spaceborne temporal sequences of synthetic aperture radar (SAR) data have a definite advantage over multispectral data sequences in terms of continuity and regularity. Still, deep-learning (DL) applications in remote sensing have primarily focused on multispectral data. This work is focused instead on a novel 3-D DL architecture for SAR data sequences. The proposed approach utilizes a trained-from-scratch 3-D fully convolutional network (FCN) with a 3-D ResNet-50 as a backbone to classify ten land cover types using multitemporal Sentinel-1 SAR data. Experimental results show that this architecture provides a trained model that outperforms existing DL methods applied to the same SAR sequence in terms of overall accuracy (OA). In addition, the results using only SAR data provide very similar and consistent performances to those achievable using multispectral data. Accordingly, the proposed approach demonstrates the potential of SAR temporal sequences in land cover mapping using DL techniques.

Food traceability in organic agriculture requires a comprehensive "crop history" that includes information from the moment seedlings begin to sprout. Radar remote sensing could contribute in this framework by providing satellite-observable variables and time sequences that help build a more complete crop history. One possible application of this concept is monitoring tillage techniques, which have different impacts on soil properties. The so-called "minimum tillage" reduces erosion and surface runoff, and translates into different backscattering mechanisms in spaceborne radar observation, compared to those of conventional ploughing. In this work, preliminary experiments were conducted to assess the type of tillage based on sequences of spaceborne radar data, specifically plowing-and-harrowing versus minimum tillage. By using radar remote sensing, customers can achieve a more comprehensive understanding of the history of their food from "farm-to-fork", which can be particularly important for high-tier organic food.

This paper investigates the feasibility of using multitemporal Sentinel-2 data to help monitor compliance with "closed periods" regulations for manure applications in agriculture, and to detect potential infringements. Previous work used Exogenous Organic Matter Indices (EOMIs), derived from Sentinel-2 bands, to identify fields with and without manure spreading. In this study, time series of the EOMI index are analyzed, and a method for satellite-based manure detection is proposed. Results show distinctive features on the occasion of known manuring events, but further validation is needed due to the lack of an extensive set of samples with related ground truth. The proposed method shows potential for operational use in monitoring manuring compliance.

In this work, we investigate how time series of the Normal-ized Differential Vegetation Index (NDVI) can provide use-ful clues to enhance the traceability of organic food, and dis-cuss the possibility to use machine learning in this context. Crop rotation, non-chemical weed control operations such as “green mulching”, fertilization, water supply management, all reflect into variables that are observable from space and may help reassuring the consumer that the traceable food they are purchasing matches the declared standards of sustainability and organic compliance. In this study we address detection of green mulching and weeding, based on experiments on a set of rice fields in North-ern Italy. Our findings suggest that the cover crops associated with green mulching can be confirmed and weeding can be detected using data from the Sentinel-2 satellite constellation, whereas fertilization is far more difficult to detect correctly. The cost associated with procuring training data seems to dis-courage the use of machine learning at this stage.

The use of multitemporal SAR sequences is becoming more intensive in mapping and change detection applications, be-cause of the availability of data sets with enough time length and sampling frequency. However, this procedure often requires to consider sequences of heterogeneous SAR data sets acquired by different sensors with different spatial resolution, frequency and polarimetric features. Their full exploitation is still an open and interesting research task. This work introduces first a pre-processing sequence ap-plied by the team of the project MultiBiGSARData to obtain calibrated and co-registered heterogeneous SAR sequences. Then, a first example of their use for land cover classification is provided. The results are shown for test areas in Italy and Argentina, because the data sets are obtained by combining images acquired by the SIAGSE constellation, composed by the COSMO-SkyMed (CSK), COSMO-SkyMed Second Generation (CSG), and SAOCOM satellites.

Within the European Space Agency (ESA) Climate Change Initiative (CCI) project framework it is fundamental to generate High Resolution (HR) annual surface water maps. In this work we present an innovative approach aimed at this task using multi-temporal Sentinel-1 SAR data. Mapping water bodies with dual-polarized radar data everywhere in the world is challenging, as the dual-pol backscatter intensity signal is strongly affected by many factors such as terrain an acquisition geometry. In this study, an existing Medium Resolution Land Cover (MRLC) map is ecploited to automatically collect sample points and build a k-means unsupervised model. A qualitative analysis is first performed in three test areas. Preliminary quantitative analysis is then presented, showing 97% extraction accuracy.

Probably a consequence of the unbalance between rice production in Asia and in Europe, satellite-based rice identification in Asia is widely discussed in scientific literature whereas SAR-based mapping of European rice paddy field has received less attention so far. In this paper, we propose a simple methodology for identifying European rice paddy fields from time series of SAR data. Standard practices for management of water in conventional European rice paddy fields translates into a distinctive pattern of low backscatter values between April and May, typically preceded and followed by higher backscatter values due to ploughing and emergence. Our proposed method leverages such pattern to discriminate rice against other crops and in a test involving the entire Italian rice-producing region of Lombardy has achieved very good Overall Accuracy (OA) scores. This paper reports the method, our test results and draws some preliminary conclusions.

The European Space Agency (ESA) Climate Change Initiative (CCI) is aiming, in its current phase, at an accurate description and analysis of land cover (LC) and land cover change (LCC) using high spatial resolution Earth Observation (EO) data. A new high resolution LC map could have a key role in the extraction of the so-called Essential Climate Variables (ECV), and be crucial to understand climate change. Indeed, until now these important variables have been derived by the climate modelling community at the global scale using medium resolution EO data (i.e., with a spatial sampling between 100 and 300 m).

The data acquired by the Inference Imaging Spectrometer (IIM) sensor on board of the Chinese Chang'E-1 mission can be used to infer important information on the Moon surface composition. In this work, the multi-path and multi-reflection phenomena occurring on its rugged surface recorded at the IIM rather coarse resolution (200m) are described by means of nonlinear spectral analysis based on the p-linear mixture model (pLMM) and the p-harmonic mixture model (pHMM). The analysis by pLMM and pHMM provides details on the materials and elements on the Moon surface, and their abundance distribution and fractional cover can be properly estimated without any a priori information on its chemical composition. Mineral map extractions using pLMM and pHMM have been considered and compared with those obtained by means of the modified partial least squares regression (PLSR) methodology, assessing the reliability and accuracy of the pLMM- and pHMM-based approach.