Monitoring wheat yield and production is essential for ensuring global food security. Remote sensing can be used to achieve it due to its ability to provide global, comprehensive, synoptic, and repetitive information in near real-time. This study used the 2006–2016 Normalized Difference Vegetation Index (NVDI) and Enhanced Vegetation Index 2 (EVI2) time series at a 250 m spatial resolution and 2006–2011 MERIS Terrestrial Chlorophyll Index (MTCI) time series at a 300 m spatial resolution. The post-maximum period for pixels containing wheat was selected based on the EU’s Common Agrarian Policy (CAP) and Corine Land Cover (CLC) data. It was correlated with yield and production values from governmental statistics (GS) of the largest Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) wheat producers in Spain and for Spain overall. The selection of wheat masks was crucial for the accuracy of the models, with CAP masks offering greater forecasting capability. Models using CLC produced R² values between 0.45 and 0.7, while those using CAP outperformed the former with R² values of 0.9 throughout Spain. Production models outperformed yield models, and MTCI was the vegetation index (VI) that provided the greatest R² value of 0.94. However, model accuracy was heavily conditioned by the precision of input data, where anomalies were detected in some NUTS-2.

LiDAR (Light Detection and Ranging) systems such as ALS (Airborne Laser Scanning) are increasingly being used in studies that analyse the forest structure and for the characterisation of their ecosystem processes. The main reason is their ability to provide an accurate three-dimensional description of the canopy structure, compared to other existing methods, such as passive sensors or photogrammetry. In addition, the high positional accuracy of ALS and their capacity of penetrating the canopy through small gaps in the forest canopy allow the estimation of parameters such as aboveground biomass, vegetation height, or leaf area index, among others. In forestry applications, the acquisition of these parameters usually requires a pre-processing analysis of the point clouds, which includes ground point filtering, Digital Terrain Model (DTM) and Canopy Height Model (CHM) derivation, tree detection, and segmentation, among other processes.  In the last decades, point cloud processing has benefited by the development of dedicated software packages such as LAStools, FUSION, or Terrascan, focused on obtaining DTM/CHM and LiDAR-derived metrics. However, the recent development of more sophisticated software packages, such as LidR or Pycrown, allow implementing novel and state-of-the-art algorithms as well as specific user-created functions.The wide variety of licensed and open-source software packages for ALS data processing, together with the increasing diversity of existing algorithms and methodologies, has provoked a multitude of comparative analysis of the most widely used algorithms in the scientific literature.  However, given the recent development of the field, a robust and exhaustive review of the current use of these software and the related algorithms is still missing. In this contribution, we present a synthesis review of 613 papers on the use of software packages and algorithms for ALS processing used between 2016 and 2020. The review focuses in forest environments with a complex structure where the difference in elevation, slope, and the existence of multiple vegetation strata usually requires more complex and specialised algorithms. Therefore, three specific steps of LiDAR processing workflow were considered: ground point filtering, DTM interpolation and crown detection and segmentation. The results showed that ground point filtering (84% of the studies) is the most common step in ALS processing, compared to DTM interpolation (71%) and tree segmentation (36%). For the DTM interpolation step, TIN construction was the most used method (13%) compared to other methods such as ordinary kriging (3%). Conventional software packages that employ algorithms based on progressive TIN densification or hieratical robust interpolation approaches were the most commonly used in ground point filtering for DTM generation. Meanwhile, other user developed advanced algorithms were used more frequently in canopy segmentation processing, especially in those articles using datasets with high point densities (165.93 p/m2 on average), compared to datasets processed with more general software solutions as FUSION (13.81 p/m2).

Obtaining specific field calendars for each crop is very useful information for farmers and public administration to understand and manage harvests. This information can be collected manually from each farm, but this approach is highly time and money consuming. It is possible to acquire it more efficiently using phenology estimates obtained through remote sensing. Common Agrarian Policy (CAP) and Geographical Information System of the Common Agrarian Policy (GISCAP) data were used to know the location of principal cereal plots in Andalusia, Spain. It included common wheat, durum wheat, triticale, oat, rye, barley, sorghum, maize and rice. Several phenometrics from Sentinel-2 were obtained: start of the season (SOS), middle of the season (MOS), length of the season (LOS) and end of the season (EOS). This dataset was correlated and compared with sowing and harvesting data collected by the Spanish government. The results showed a high correlation between SOS and sowing and between EOS and harvest for most of the studied crops.Sowing for common wheat, durum wheat, triticale, oat, rye, and barley took place between October and December according to government calendars, while SOS generally started one month later, between November and January. However, in these crops, harvest and EOS occurred simultaneously, mostly in June. In the case of sorghum, maize and rice, which are summer cereals, their phenometrics differed from the others. Sowing and SOS for sorghum mostly occurred in April and March, and harvest and EOS in September and October, in typically at the same time. Maize sowing took place in March, SOS in April, and harvest and EOS in September. Finally, rice sowing occurred in May, SOS in June, harvest in October and EOS in November. This study shows that obtaining accurate crop calendars from Sentinel-2 phenological trajectories is feasible, providing valuable information for farmers and public administrations.

Land surface phenology (LSP), the study of seasonal dynamics on the vegetated land surface from satellite data, has contributed to improve the understanding of the phenological dynamics of vegetation in boreal and temperate ecosystems located in the high and mid-latitude regions of the Northern Hemisphere. Iberian Peninsula is one of the most biologically rich regions of the European continent, where endemic plant species are very common. Hence, the ecosystems of the Iberian Peninsula provide a wide variety of ecosystem services, which may be altered by changes in climatic conditions. LSP has not been studied in detail in the Iberian ecosystems. Despite their ecological importance and vulnerability to climate change, there is a lack of an exhaustive and long-term characterisation of the phenological patterns of the main natural vegetation land cover types of the whole Iberian Peninsula. Therefore, the goal of this study was to monitor the LSP dynamics across major natural vegetation land cover types in the Alpine, Atlantic, and Mediterranean biogeographical regions of the Iberian Peninsula. Two-band Enhanced Vegetation Index (EVI2) time-series were generated from the MOD09Q1 surface reflectance product from January 2001 to December 2021. This MODIS product is based on 8-day image composites at a spatial resolution of 250 meters. The methodology to estimate LSP from raw EVI2 time-series followed two steps, including a) data denoising and b) the extraction of spring and autumn phenometrics. A double logistic function was used to smooth the EVI2 time-series from TIMESAT software. Start of the growing season (SOS) (i.e., proxy of the spring phenology) and the end of the growing season (EOS) (i.e., proxy of the autumn phenology) were extracted using a 10% threshold-based technique.Results of this research showed spring and autumn phenometrics had a similar spatial pattern across major natural vegetation land cover types of the three biogeographic regions of the Iberian Peninsula. Generally, SOS dates were in March-April, while EOS dates were in November-December. Despite this, the spatial patterns of LSP were very different in Mediterranean shrublands, savannahs and grasslands. SOS and EOS dates showed the highest variability in these natural vegetation land cover types. The growing season generally started between October and February (SOS) and ended between June and November (EOS). The spatial variability of spring and autumn phenometrics in these three natural vegetation cover types may be related to several factors: i) the mixture of the phenological cycles of different functional vegetation types (e.g., herbaceous, shrub or tree vegetation); ii) the mixture of phenological trajectories of multiple plant species; iii) the influence of non-vegetation cover, such as bare soil, affecting spectral information. These LSP patterns of natural vegetation cover types in the Iberian Peninsula could contribute to improve the understanding of the response of Iberian ecosystems to climate change.