Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed.

The surface energy balance algorithm for land (SEBAL) has been demonstrated to provide accurate estimates of crop evapotranspiration (ET) and yield at different spatial scales even under highly heterogeneous conditions. However, validation of the SEBAL using in-field direct and indirect measurements of plant water status is a necessary step before deploying the algorithm as an irrigation scheduling tool. To this end, a study was conducted in a maize field located near the Venice Lagoon area in Italy. The experimental area was irrigated using a 274 m long variable rate irrigation (VRI) system with 25-m sections. Three irrigation management zones (IMZs; high, medium and low irrigation requirement zones) were defined combining soil texture and normalized difference vegetation index (NDVI) data. Soil moisture sensors were installed in the different IMZs and used to schedule irrigation. In addition, SEBAL-based actual evapotranspiration (ET_r) and biomass estimates were calculated throughout the season. VRI management allowed crop water demand to be matched, saving up to 42 mm (−16%) of water when compared to uniform irrigation rates. The high irrigation amounts applied during the growing season to avoid water stress resulted in no significant differences among the IMZs. SEBAL-based biomass estimates agreed with in-season measurements at 72, 105 and 112 days after planting (DAP; r² = 0.87). Seasonal ET matched the spatial variability observed in the measured yield map at harvest. Moreover, the SEBAL-derived yield map largely agreed with the measured yield map with relative errors of 0.3% among the IMZs and of 1% (0.21 t ha⁻¹) for the whole field. While the FAO method-based stress coefficient (K_s) never dropped below the optimum condition (K_s = 1) for all the IMZs and the uniform zone, SEBAL K_s was sensitive to changes in water status and remained below 1 during most of the growing season. Using SEBAL to capture the daily spatial variation in crop water needs and growth would enable the definition of transient, dynamic IMZs. This allows farmers to apply proper irrigation amounts increasing water use efficiency.

Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers and the insurance market ask for a reliable, objective, and less labor-intensive method to determine crop hail losses. Integrating remote sensing and crop modeling provides a unique opportunity for the crop insurance market for a reliable, objective, and less labor-intensive method to estimate hail damage. To this end, a study was conducted on eight distinct maize fields for a total of 90 hectares. Five fields were damaged by the hailstorm that occurred on 13 July 2019 and three were not damaged. Soil and plant samples were collected to characterize the experimental areas. The Surface Energy Balance Algorithm for Land (SEBAL) was deployed to determine the total aboveground biomass and obtainable yield at harvest, using Landsat 7 and 8 satellite images. Modeled hail damages (HDDSSAT1, coupling SEBAL estimates of obtainable yield and DSSAT-based potential yield; HDDSSAT2, coupling yield map at harvest and the Decision Support System for Agrotechnology Transfer (DSSAT)-based potential yield) were calculated and compared to the estimates of the insurance company (HDinsurance). SEBAL-based biomass and yield estimates agreed with in-season measurements (−4% and +0.5%, respectively). While some under and overestimations were observed, HDinsurance and HDDSSAT1 averaged similar values (−4.9% and +3.4%) compared to the reference approach (HDDSSAT2).

Several remote sensing-based methods have been developed to apply site-specific nitrogen (N) fertilization in crops. They consider spatial and temporal variability in the soil-plant-atmosphere continuum to modulate N applications to the actual crop nutrient status and requirements. However, deriving fertilizer N recommendations exclusively from remote proximal and remote sensing data can lead to substantial inaccuracies and new, more complex approaches are needed.Therefore, this study presents an improved approach that integrates crop modelling, proximal sensing and forecasts weather data to manage site-specific N fertilization in winter wheat. This improved approach is based on four successive steps: (1) optimal N supply is estimated through the DSSAT crop model informed with a combination of observed and forecast weather data; (2) actual crop N uptake is estimated using proximal sensing; (3) N prescription maps are created merging crop model and proximal sensing information, considering also the contribution of the soil N mineralisation; (4) N-Variable Rate Application (N-VRA) is implemented in the field. A VRA method based on DSSAT fed with historical weather data and a business-as- usual uniform fertilization were also compared.The methods were implemented in a 23.4 ha field in Northern Italy, cropped to wheat and characterized by large soil variability in texture and organic matter content. Results indicated that the model-based approaches consistently led to higher yields, agronomic efficiencies and gross margins than the uniform N application rate. Furthermore, the proximal sensing-based approach allowed capturing of the spatial variability in crop N uptake and led to a substantial reduction of the spatial variability in yield and protein content. This study grounds the development of web-based software as a friendly tool to optimize the N variable rate application in winter cereals.

Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R² of 0.62, and for LAI using MSR and NDVI an R² of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions.