Synergistic use of Sentinel-1 and Sentinel-2 data for Fall Armyworm infestation detection and mapping in maize croplands

Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple

The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68).

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.

Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali

At present, the works devoted to the creation of digital soil maps using geographic information systems (GIS) and remote sensing (RS) data are relevant. In the work the analysis of vegetation indices (VI) for soil mapping was carried out, the maps of vegetation indices were created: Normalized Difference Vegetation Index (NDVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Soil-Adjusted Vegetation Index (SAVI), Tranformed Soil-Adjusted Vegetation Index (TSAVI), Enhanced Vegetation Index2 (EVI2) for the territory of ZAO Mirny farm of Kochenevsky District using Sentinel-2 A satellite image (May 10, 2021). ). As a result it was revealed that the vegetation indices OSAVI and EVI2 allow to establish spatial boundaries between the main types of soils of automorphic, half-hydromorphic and hydromorphic moisture regimes. Background. Multispectral space and aerial photos for thematic mapping of soil resources are of greater practical application. Sentinel-2 A space image with good spatial and spectrozonal resolution (10 m, 20 m and 60 m) and territorial coverage (290 km) was used in this article. This made it possible to calculate and analyze various vegetation indices for the purposes of digital soil mapping. Purpose. аnalysis of vegetation indices for digital soil mapping based on Sentinel-2 A images. Materials and research methods. The research was carried out on the territory of CJSC Mirniy, Kochenevsky District, Novosibirsk Region. The methods of digital processing of space images, mapping and geoinformation analysis with the use of Sentinel-2 A satellite image (May 10, 2021) were used. The method of equal intervals was used for comparative analysis of images. This allowed using GIS ArcGIS to make thematic maps of images with the allocation of gradations: very low, low, average, above average, high value. Results. Field soil surveys were carried out on the territory of CJSC Mirny farm in Kochenevsky district of Novosibirsk Region. Using SAGA geoinformation system the space image was atmospherically corrected and spatially referenced, NDVI, OSAVI, TSAVI, EVI2 raster maps were compiled. Geoinformation analysis of the large-scale 1:1000 soil map and raster EVI maps revealed that OSAVI allows to establish spatial boundaries between the main types of soils of automorphic, half-hydromorphic and hydromorphic moisture regimes. Very low values of VI are typical for the soils of hydromorphic humidification regime, formed near small lakes, along the banks of the Sharikh river. Very low values of UI have objects of hydrography, marsh peaty, meadow-marsh humus soils, marshy and peated marshes, marsh solonchaks, marsh solonchaks, formed in lowered areas of relief with depth of groundwater occurrence less than 0.5 m. Ploughed ordinary chernozems, deposited in the upper and middle part of the gentle slope, have low values of WP. These are soils of automorphous moisture regime with depth of groundwater occurrence more than 6 m. Average and above average values are characteristic of gray forest saltwort soils under woody vegetation, as well as meadow-chernozem soils under meadow vegetation in the lower part of the gentle slope with groundwater occurrence depth from 3 to 4 m. High WI values were obtained for meadow soils with dense grass cover. Wetting conditions of soils, location in the relief, and vegetation type significantly influence VI values. The obtained VV values can be used at the stage of training data preparation in the form of reference classes for basic soil types required for automatic image recognition.

Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance?

The processing of remote sensing images and their integration into a Geographic Information System (GIS) to analyse and manage an area represents a modern approach that is increasingly used. In the present paper, a predominantly moun-tainous area was studied and analysed, located in Hunedoara County – Romania, near the city of Hateg and the Retezat Mountains. A satellite scene from 09.24.2019 from the RapidEye remote sensing system was retrieved, processed and subjected to complex remote sensing analyses. These remote sensing data were analysed and processed, and based on them a series of specific indices were calculated and interpreted, namely, for the characterisation of the vegetation: NDVI (Normalised Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normal-ised Difference Red Edge Index), SAVI (Soil Adjusted Vegetation Index), MSAVI (Modified Soil Adjusted Vegetation Index), CI Green (Chlorophyll Index Green), CI Red Edge (Red Edge Chlorophyll Index), RTVI core (Red Edge Tri-angular Vegetation Index), SR (Simple Ratio), Red Edge SR (Red Edge Simple Ratio), LAI (Leaf Area Index).

Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model

Abstract. Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r2) values were obtained using multispectral band predictors for Sentinel-2 (r2 = 0.89) and Planetscope (r2 = 0.80); and vegetation indices for RapidEye (r2 = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r2 ranging from 0.62 to 0.92. Based on the r2 and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r2 = 0.92) and RapidEye data (r2 = 0.91).

Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam

Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize

Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data

Mapping crop phenophases in reproductive growth period by satellite solar-induced chlorophyll fluorescence: A case study in mid-temperate zone in China

Preventive control of desert locusts is based on monitoring recession areas to detect outbreaks. Remote sensing has been increasingly used in the preventive control strategy. Soil moisture is a major ecological driver of desert locust populations but is still missing in the current imagery toolkit for preventive management. By means of statistical analyses, combining field observations of locust presence/absence and soil moisture estimates at 1 km resolution from a disaggregation algorithm, we assess the potential of soil moisture to help preventive management of desert locust. We observe that a soil moisture dynamics increase of above 0.09 cm3/cm3 for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. We estimate the gains in early warning timing compared to using imagery from vegetation to be 3 weeks. We demonstrate that forecasting errors may be reduced by the combination of several types of indicators such as soil moisture and vegetation index in a common statistical model forecasting locust presence. Policy implications. Soil moisture estimates at 1 km resolution should be used to plan desert locust surveys in preventive management. When soil moisture increases in a dry area of potential habitat for the desert locust, field surveys should be conducted two months later to evaluate the need of further preventive actions. Remote sensing estimates of soil moisture could also be used for other applications of integrated pest management.