sen2r is a scalable and flexible R package to enable downloading and preprocessing of Sentinel-2 satellite imagery via an accessible and easy to install interface. It allows the execution of several ...preprocessing steps which are commonly performed by Sentinel-2 users: searching the Sentinel-2 archive for datasets available over a spatial area of interest and in a defined time window, downloading them, applying the Sen2Cor atmospheric correction algorithm to compute surface reflectances, merging adjacent tiles, performing geometric transformations, applying a cloud mask, computing spectral indices and colour images. The package is designed to be accessible to a range of users, from beginners to skilled R users. It comes with a Graphical User Interface, which can be used to set the processing parameters and launch processing operations: this feature makes sen2r accessible also for novices with limited programming experience. High-level R functions, which enable customised image processing workflows and control over intermediate steps, can be useful to experienced remote sensing researchers. Thanks to those functions it is possible to easily schedule automatic processing chains, so to manage massive processing operations. This paper describes the main characteristics, functionalities and performance of the package and highlights its usefulness as the operational back-end of service-oriented architectures, as illustrated by the Saturno project.
•We present sen2r, an R package devoted to download and preprocess Sentinel-2 data.•A Graphical User Interface facilitates settings the processing parameters.•sen2r can be used to automatically keep a Sentinel-2 archive updated.•A case study for precision farming applications is described.
The rice growing district in northwestern Italy, where paddies were traditionally flooded throughout spring, was interested by a general decrease of standing water presence caused by the adoption of ...dry seeding crop practices, with consequences for water management and for the ecology of breeding waterbirds. This communication analyses changes in flooding dynamics in the last four years, estimating them from MODIS data and comparing results with previous knowledge of the same study area. Results highlighted an intensification of the phenomenon in the north-western regions (-3.3 ± 0.6% per year in the period 2013-2021) and the almost complete loss of flooded surfaces east to the Ticino river (reaching in 2021 5% of the flooded extension estimated in 2000). Such findings highlight the importance of monitoring this phenomenon - considered by other authors as the biggest anthropogenic change in surface water of all Europe since 2000 - in near real time from remotely sensed data to monitor dynamics and support sustainable management of water usage at the district level.
Non-photosynthetic vegetation (NPV) plays a key role in soil conservation, which in turn is important in sustainable agriculture and carbon
farming. For mapping NPV image spectroscopy proved to ...outperform multispectral
sensors. PRISMA (PRecursore IperSpettrale della Missione Applicativa) is the
forerunner of a new era of hyperspectral satellite missions, providing the
proper spectral resolution for NPV mapping. This study takes advantage from
both spectroscopy and machine-learning techniques. Exponential Gaussian
Optimization was used for modelling known absorption bands (cellulose-lignin,
pigments, water content and clays), resulting in a reduced feature space, which
is split by a decision tree (DT) for mapping different field conditions (emerging,
green and standing dead vegetation, crop residue and bare soil). DT training
and validation exploited reference data, collected during PRISMA overpasses on a large farmland. Mapping results are accurate both at pixel and parcel level (O.A.
> 90%; K > 0.9). Field status and crop rotation trajectories through time
are derived by processing 12 images over 2020 and 2021. Results proved that
PRISMA data are suitable for mapping field conditions at parcel scale with high
confidence level. This is important in the perspective of other hyperspectral
missions and is a premise toward quantitative estimates of NPV biophysical
variable.
The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many ...scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R
2
= 0.82 and R
2
= 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.
Due to the low efficiency of nitrogen fertilizers in flooded rice paddies, there is a rising demand for tools able to detect crop nitrogen status in space and time to allow farmers to use the ...technical novelties of precision agriculture to improve fertilizer management in extensive fields. This work sets up an operational approach to increase nitrogen use efficiency of top-dressing fertilization by supporting variable rate fertilization in rice cropping systems. The procedure exploits (i) crop modelling to identify best periods for fertilization (When), (ii) Sentinel-2 imagery to draw management zones (MZ) and lead field scouting (Where), and (iii) smartphone app to measure nitrogen nutritional index (NNI) (How much). Automatically generated MZ from Sentinel-2 data were able to identify within field patches with different nutritional status and NNI data well described the crop temporal dynamic in relation to crop development and nutritional needs. The workflow was implemented to provide farmers with timely information on plant nutritional status during the 2018 growing season to define site-specific fertilization strategies implemented with variable rate technology (VRT). Tests conducted on 6 fields over 30 ha in 3 farms showed the feasibility of the proposed workflow in real farming conditions allowing a reduction of applied fertilizer up to 25% in the areas with sufficient nutritional status. Demonstration revealed that VRT based on geospatial information from integrated in-field and satellite data can provide agronomic and environmental benefits compared with standard fertilization resulting in promising outcomes both in terms of yield (increase in the range 0.2–0.5 t ha
−1
) and nitrogen use efficiency (increase up to 7.8%).
The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies ...acting as a surrogate for the lost natural wetlands. The extent of submerged paddies strictly depends on crop management practices: in this framework, the recent diffusion of rice seeding in dry conditions has led to a reduction of flooded surfaces during spring and could have contributed to the observed decline of the populations of some waterbird species that exploit rice fields as foraging habitat. In order to test the existence and magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period, MODIS remotely-sensed data were used to estimate the extent of the average flooded surface and the proportion of flooded rice fields in the years 2000–2016 during the nesting period of waterbirds. A general reduction of flooded rice fields during the rice-sowing season was observed, averaging − 0.86 ± 0.20 % per year (p-value < 0.01). Overall, the loss in submerged surface area during the sowing season reached 44 % of the original extent in 2016, with a peak of 78 % in the sub-districts to the east of the Ticino River. Results highlight the usefulness of remote sensing data and techniques to map and monitor water dynamics within rice cropping systems. These techniques could be of key importance to analyze the effects at the regional scale of the recent increase of dry-seeded rice cultivations on watershed recharge and water runoff and to interpret the decline of breeding waterbirds via a loss of foraging habitat.
To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model ...and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project.
•We present a high-resolution rice forecasting system integrating WARM model and RS•The system extends the MARS one and was tested in Italy, Greece and Spain•Variance explained ranged from 66% to 89% in 6 out of 8 combinations ecotype×district•The assimilation of RS LAI increased the forecasting capability in 7 out of 8 cases
Extrinsic and intrinsic factors may influence the activity budget of wild animals, resulting in a variation in the time spent in different activities among populations or individuals of the same ...species. In this study, we examined how extrinsic and intrinsic factors affect the behaviour of the alpine marmot (
Marmota marmota
), a hibernating social rodent inhabiting high-elevation prairies in the European Alps. We collected behavioural observations during scan sampling sessions on marked individuals at two study sites with different environmental characteristics. We used Bayesian hierarchical multinomial regression models to analyse the influence of both intrinsic (sex and age-dominance status) and extrinsic (environmental and climatic variables) factors on the above-ground activity budget. Marmots spent most of their time above ground foraging, and were more likely to forage when it was cloudy. Extrinsic factors such as the site, period of the season (June, July–August, and August–September), and time of the day were all related to the probability of engaging in vigilance behaviour, which reaches its peak in early morning and late afternoon and during July, the second period included in the study. Social behaviours, such as affiliative and agonistic behaviours, were associated mostly with sex and age-dominance status, and yearlings were the more affiliative individuals compared to other status. Overall, our results suggest that in alpine marmots, intrinsic factors mostly regulate agonistic and affiliative behaviours, while extrinsic factors, with the unexpected exception of temperature, affect the probabilities of engaging in all types of behavioural categories.
The breeding populations of colonial herons, egrets and allied waterbirds in Northwestern Italy increased since 1972, when a long-term monitoring was initiated, up to the end of the 20° century. ...Populations of continental importance for some heron species were concentrated mostly in the district of intensive rice cultivation, where the paddies offered wide foraging opportunities. After 2000, new cultivation techniques caused a progressive reduction in paddy flooding. We found a significant relationship between the post-2000 decrease in the number of nests of the three most abundant species of waterbirds, Grey Heron, Little Egret and Black-crowned Night-heron, and the diminishing extent of paddy flooding estimated on satellite-derived images. On the other hand, outside the paddies district the same three species continued to increase or remained mostly stable. The recent agronomic changes in the paddies of Northwestern Italy compromise their role as surrogates of natural wetlands and their importance for environmental conservation. The spread of dry rice fields, and the consequent loss of their value for conservation of aquatic biodiversity, call for a revision of the regulations and incentives to farmers under the Common Agriculture Policy by the European Union and by local authorities. Return to early-submerged rice fields and adoption of environmental-friendly practices, such as the creation of compensatory wetlands, should become mandatory for rice to maintain its status as “green” crop.
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