Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI), are commonly used to estimate the LAI. However, these indices commonly saturate at moderate-to-dense canopies (e.g., NDVI saturates when LAI exceeds three). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in the red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR), and the green chlorophyll index (CI_green) formula. These improved indices are termed NDVI_{red& RE} (red and red-edge NDVI), MS_{Rred& RE} (red and red-edge MSR index), and CI_{red& RE} (red and red-edge CI). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured. We investigated the predictive power of nine VIs for crop LAI estimation, including NDVI, MSR, and CIgreen; the red-edge modified indices: NDVI_Red-edge, MSR_Red-edge, and CI_Red-edge (generally represented by VIRed-edge); and the newly improved indices: NDVI_{red& RE}, MSR_{red& RE}, and CI_{red& RE} (generally represented by VI_{red& RE}). The results show that VI_{red& RE} improves the coefficient of determination (R²) for LAI estimation by 10% in comparison to VI_Red-edge. The newly improved indices prove to be the powerful alternatives for the LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-edge channels (such as Sentinel-2) when applied to precision agriculture.

We present validation studies of MLS version 2.2 upper tropospheric and stratospheric ozone profiles using ozonesonde and lidar data as well as climatological data. Ozone measurements from over 60 ozonesonde stations worldwide and three lidar stations are compared with coincident MLS data. The MLS ozone stratospheric data between 150 and 3 hPa agree well with ozonesonde measurements, within 8% for the global average. MLS values at 215 hPa are biased high compared to ozonesondes by ∼20% at middle to high latitude, although there is a lot of variability in this altitude region. Comparisons between MLS and ground‐based lidar measurements from Mauna Loa, Hawaii, from the Table Mountain Facility, California, and from the Observatoire de Haute‐Provence, France, give very good agreement, within ∼5%, for the stratospheric values. The comparisons between MLS and the Table Mountain Facility tropospheric ozone lidar show that MLS data are biased high by ∼30% at 215 hPa, consistent with that indicated by the ozonesonde data. We obtain better global average agreement between MLS and ozonesonde partial column values down to 215 hPa, although the average MLS values at low to middle latitudes are higher than the ozonesonde values by up to a few percent. MLS v2.2 ozone data agree better than the MLS v1.5 data with ozonesonde and lidar measurements. MLS tropical data show the wave one longitudinal pattern in the upper troposphere, with similarities to the average distribution from ozonesondes. High upper tropospheric ozone values are also observed by MLS in the tropical Pacific from June to November.

We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure < ∼ 50 hPa in the high latitudes. It demonstrates clear improvement over the a priori down to the lower troposphere in the tropics and down to an average of ∼ 550 (300) hPa at middle (high) latitudes. In the tropics and midlatitudes, the profile mean biases (MBs) are less than 6 %, and the standard deviations (SDs) range from 5 to 10 % for pressure < ∼ 50 hPa to less than 18 % (27 %) in the tropics (midlatitudes) for pressure > ∼ 50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2 % with SDs of < 5 % and the MBs of the tropospheric ozone column (TOC) are within 6 % with SDs of 15 %. In the high latitudes, the profile MBs are within 10 % with SDs of 5–15 % for pressure < ∼ 50 hPa but increase to 30 % with SDs as great as 40 % for pressure > ∼ 50 hPa. The SOC MBs increase up to 3 % with SDs as great as 6 % and the TOC SDs increase up to 30 %. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure > ∼ 100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2 % in the stratosphere and 8 % in the troposphere and up to 6 % in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14–0.7 % year⁻¹ for pressure < ∼ 80 hPa, 0.7 DU year⁻¹ in SOC, and −0. 33 DU year⁻¹ in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality.

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and inter-comparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R ², i.e., ~0.6 to ~0.7 between SNAP-derived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m² m^–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i.e., R ² of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m² m^–2 and ~0.6 m² m^–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions.

Air pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO₂, SO₂, and O₃ in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO₂, SO₂, and O₃ were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO₂, SO₂, and O₃ for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO₂, SO₂, and O₃, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO₂, SO₂, and O₃, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate.

This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km², which is 48% higher than the previous ten years, with an average of 6760 km². The steady increase of deforestation in the Brazilian Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon. We estimated 295 million tons of net CO₂, which is equivalent to 16.4% of the combined emissions of CO₂ and CH₄ emitted by Brazil in 2019. The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas. Hotspots’ location was deemed to be an important tool to verify forest degradation. The incidence of hotspots in forest area is from 5% to 20% of newly slashed-and-burned areas, which confirms the strong impact of deforestation on ecosystem degradation due to fire occurrences over the Brazilian Amazon.