Remotely-sensed Variables Research Articles

Next-Generation Remote Sensing Data at Multiple Spatial Scales Improves Understanding of Habitat Selection by a Small Mammal

Recent advances in optical remote sensing (RS) technology in combination with lightweight Global Positioning System (GPS) tracking devices now make analyzing the multi-scale habitat selection (HS) of small mammals < 2 kg possible. However, there have been relatively few multi-scale HS studies integrating fine-scale RS data with data-rich, GPS-derived movement data from small mammals. This is critical because small mammals commonly select habitat features across multiple scales. To address this gap, we investigated the HS of a small mammal, fox squirrels (Sciurus niger), which are known to cover relatively large areas and select fine-scale environmental features. We specifically asked the following questions: (1) Do next-generation RS variables improve HS models at single spatial scales? (2) Do multi-scale HS models improve upon those at single spatial scales? Using data from 45 individuals, we constructed HS models at three spatial scales: 4 ha (210 m × 210 m), 0.09 ha (30 m × 30 m), and 0.01 ha (10 m × 10 m) using traditional and next-generation RS data. The 4-ha model, using traditional and next-generation RS data, produced the best single-scale model, explaining 58% of the variations in HS. However, the multi-scale model provided the most informative model, explaining 68% of the variations in HS. Our models provide evidence for the value of next-generation RS data when quantifying HS and additional support for the idea of studying HS at multiple spatial scales.

Assessing soil fungal diversity under different sampling schemes in conjunction with remote sensing technologies in a subtropical forest

Fungi, serving as real-time bioindicators to environmental changes and stressors, are crucial for effective forest conservation and management practices under ongoing global change. However, the large-scale assessment of soil fungi still encounters challenges in striking a balance between the extensive sampling costs and the limited accuracy of minimal sampling. In this study, we analyzed 1,606 soil samples collected from 625 quadrats (20 m × 20 m) within a 25-ha subtropical forest dynamic plot in East China. Our primary objective was to explore the impact of different sampling schemes, in conjunction with remote sensing (RS) technologies, on the interpolation of soil fungal diversity using Ordinary Kriging (OK) and Co-kriging (CoK) models. Our findings suggested that a sampling scheme including points at 0 m (the base points) and 8 m within each quadrat, totaling to 26 points/ha, would be a sufficient scheme. This scheme with OK model yielded comparable results to those of more intensive schemes (at 0, 2, 5 and 8 m), but required the fewest sampling points. Upon incorporating each RS variable separately into the CoK models, including two vegetation indices (normalized difference vegetation index and transformed chlorophyll absorption ratio index 2), three terrain attributes (Elevation, Aspect and Slope), and the synthesis of these RS variables, the accuracy of the predicted results was further improved for each sampling scheme. By leveraging high-precision soil DNA sequencing in conjunction with cost-effective RS technologies, this study proposes a rapid and affordable approach for monitoring soil fungal diversity on a large scale. This will facilitate data collection for understanding responses of forest soil fungi to ongoing global change.

The importance of spatial scale and vegetation complexity in woody species diversity and its relationship with remotely sensed variables

Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics. Here, we used two mathematical approaches to assess the relationship between tropical woody species diversity and spectral diversity in a human-modified landscape in two vegetation types differing in their degree of complexity. Vegetation complexity was measured through the fraction of species that concentrate different proportions of the cumulative importance value index. Species diversity was assessed using Hill numbers at three spatial scales, and metrics of spectral heterogeneity, vegetation indices, as well as raw data from Landsat 9 and Sentinel-2 sensors were calculated and analysed through general linear models (GLM) and Random Forest. Vegetation complexity emerged as an important variable in modelling species from remote sensing metrics, indicating the need to model species diversity by vegetation type rather than region. Hill numbers showed different relationships with remotely sensed metrics, in consistency with the scale-dependency of ecological processes on species diversity. Contrary to multiple previous reports, in our study, GLMs produced the best fits between Hill numbers of all orders and remotely sensed metrics. If we are to meet the need of conducting efficient and speedy woody species diversity monitoring globally, we propose modelling this diversity from remotely-sensed variables as an attractive strategy, so long as the intrinsic properties of each vegetation type are acknowledged to avoid under- or overestimation biases.

Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes

Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variation in pest ecology, but also from the widespread use of categorical land-use maps that do not adequately characterize habitat-availability for pests. We used 1163 field-year observations of Lygus hesperus (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely-sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because L. hesperus often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that L. hesperus densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.

Proximal and remote sensing – what makes the best farm digital soil maps?

Context Digital soil maps (DSM) across large areas have an inability to capture soil variation at within-fields despite being at fine spatial resolutions. In addition, creating field-extent soil maps is relatively rare, largely due to cost. Aims To overcome these limitations by creating soil maps across multiple fields/farms and assessing the value of different remote sensing (RS) and on-the-go proximal (PS) datasets to do this. Methods The value of different RS and on-the-go PS data was tested individually, and in combination for mapping three different topsoil and subsoil properties (organic carbon, clay, and pH) for three cropping farms across Australia using DSM techniques. Key results Using both PS and RS data layers created the best predictions. Using RS data only generally led to better predictions than PS data only, likely because soil variation is driven by a number of factors, and there is a larger suite of RS variables that represent these. Despite this, PS gamma radiometrics potassium was the most widely used variable in the PS and RS scenario. The RS variables based on satellite imagery (NDVI and bare earth) were important predictors for many models, demonstrating that imagery of crops and bare soil represent variation in soil well. Conclusions The results demonstrate the value of combining both PS and RS data layers together to map agronomically important topsoil and subsoil properties at fine spatial resolutions across diverse cropping farms. Implications Growers that invest in implementing this could then use these products to inform important decisions regarding management of soil and crops.

Habitat occupancy of the threatened Diademed Plover (Phegornis mitchellii) is not affected by llama grazing or peatland size, but declines with peatland humidity.

Many habitat-specialist organisms occur in distinct, patchy habitat, yet do not occupy all patches, and an important question is why apparently suitable habitat remains unoccupied. We examined factors influencing patch occupancy in near-threatened, little-known Diademed Plovers (Phegornis mitchellii), arguably the bird most specialized to life in High Andean peatlands. Andean peatlands are well-suited to occupancy modelling because they are discrete patches of humid habitat within a matrix of high-altitude steppe. We hypothesized that Diademed Plovers occupy preferably larger and more humid peatlands, and avoid peatlands used for grazing by llamas and vicuñas, which may trample vegetation and nests. From December 2021 to February 2022 (breeding season), we conducted plover occupancy surveys (2-4) on 40 peatlands at Lagunas de Vilama, a landscape of arid steppe and wetlands above 4,500 m in NW Argentina. We measured peatland size, grazing pressure, topographic and remotely-sensed variables that correlate with humidity, and incorporated these as covariates in occupancy models. Occupancy models showed that more than 50% of the studied peatlands were used by Diademed Plovers and most showed signs of reproduction, highlighting the importance of the Vilama Wetlands for Diademed Plover conservation. Within peatlands, Diademed Plovers were most often associated with headwaters. The top ranked occupancy model included constant detection, random spatial effects, and a single occupancy covariate: mean NDWI (Normalized Difference Water Index, an index correlated with water content and humidity) over the previous three years. Contrary to our prediction, Diademed Plovers preferred less water-saturated peatlands (lower NDWI), possibly to avoid nest flooding. This may be especially important in wet years, like the year when we conducted our surveys. Neither peatland size nor grazing by llamas and vicuñas affected peatland use by Diademed Plovers, suggesting that llama grazing at current levels may be compatible with plover conservation. For organisms that specialize on humid habitats, such as peatlands, factors affecting occupancy may vary temporally with variation in climate, and we recommend follow-up surveys across multi-year timescales to untangle the impact of climate on animals' use of humid habitats.

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda

BackgroundMalaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey.MethodsThe results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model’s ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated.ResultsModels fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier.ConclusionsThese results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.

Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques

Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0-5, 5-15, 15-30, 30-60, and 60-100cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0-5cm) to 63.8% (30-60cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity.

Estimating lichen α- and β-diversity using satellite data at different spatial resolutions

Understanding biodiversity patterns and its environmental drivers is crucial to meet conservation targets and develop effective monitoring tools. Inconspicuous species such as lichens require special attention since they are ecologically important but sensitive species that are often overlooked in conservation planning. Remote sensing (RS) can be particularly beneficial for these species as in combination with modelling techniques it allows planners to assess and better understand biodiversity patterns. This study aims to model the lichen α-diversity (species richness) and β-diversity (species turnover) biodiversity components using high resolution RS variables across a subarctic region in Northern Quebec (∼190.25 km2). Two sensors, one commercial (WorldView-3, WV3) and another freely accessible (Sentinel-2, S2), at different resolutions (1.2 m and 10 m, respectively) were tested separately to develop our variables and feed the models. Lichens were sampled in 45 plots across different habitat types, ranging from forested habitats (coniferous, deciduous) to wetlands (bogs, fens) and rocky outcrops. Two sets of uncorrelated variables (Red and NIR; EVI2) from each sensor were parallelly used to build the α- and β-diversity models (8 models in total) through Poisson regressions and generalized dissimilarity modelling (GDM), respectively. Red and NIR variables were useful for modeling the two biodiversity components at both resolutions, providing information on stand canopy closure and structure, respectively. EVI2, especially from WV3, was only informative for assessing β-diversity, providing similar information than Red. Poisson models explained up to 32 % of the variation in lichen α-diversity, with Red, NIR and EVI2, either from WV3 or S2, showing negative relationships with lichen richness. GDMs described well the relationship between β-diversity and spectral dissimilarity (R2 from 0.25 to 0.30), except for the S2 EVI2 model (R2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation. This study contributes to improve our knowledge and to inform on the use of RS to understand biodiversity patterns of inconspicuous species, which we consider to be an essential step to enhance their representation in conservation planning.

Environmental and biotic filters interact to shape the coexistence of native and introduced bees in northern Patagonian forests

Native and introduced species can coexist in rich ecosystems where competition for resources is less intense; however, native species can be excluded from suitable habitats hosting a high abundance of introduced species. To test these hypotheses, we estimated the probability of introduced bees (buff-tailed bumblebees and honeybees) to co-occur and dominate giant bumblebees in forested landscapes of northern Patagonia. We evaluated the effects of 29 remote-sensing variables of forest ecosystems on bee abundance using data from 56 landscapes. Then, we used a probabilistic Bayesian model to estimate the effects of the most important predictors of bee abundance on their co-occurrence and dominance probabilities. Introduced and native bees exhibited heterogeneous and scale-dependent responses to environmental conditions, with only six variables affecting their co-occurrence and dominance probabilities. A high maximum temperature was associated with both a decreased co-occurrence and an increased dominance of introduced bumblebees, and the opposed pattern was found for vegetation continuous fields, an index of canopy continuity. Similar responses were found for the co-occurrence probability of honeybees and giant bumblebees. Thus, species dominance and co-occurrence changed differently along environmental gradients, with co-occurrence increasing as native and introduced species become more abundant. A high dominance of introduced bees in unsuitable habitats suggests interspecific competition is strengthened as resources become scarcer, thus reinforcing the idea that abiotic and biotic filters interact with each other.

Parameter Recovery Using Remotely Sensed Variables

Parameter Recovery Using Remotely Sensed Variables

Multiple environmental drivers for the Patagonian forest-dwelling beetles: Contrasting functional and taxonomic responses across strata and trophic guilds.

Forest disturbances modify microhabitats along the different vertical strata, triggering structural and functional changes in forest-dwelling beetle communities. However, the effects of multiple environmental factors can be complex to detect in ecosystems that offer a broad variety of microhabitats for a great variety of beetle species. This is the case in Patagonian temperate forests, where the use of remote sensing provides an opportunity to evaluate the sensitivity of beetle species to environmental changes. Here, we identified the environmental drivers of forest-dwelling beetle communities in the ground and canopy of 34 north Patagonian-forest landscapes. We analyzed the associations of the taxonomic and functional diversity of five trophic guilds with 30 remote-sensing variables of landscape structure, composition, and disturbances; vegetation and soil properties; and climate and physical variables. Hierarchical clustering analysis was used to identify trophic guilds responding similarly to predictors. Segmented regression analysis was used to evaluate functional redundancy from taxonomic-functional richness relationships. A total of 583 species (23,848 individuals) of beetles were recorded for both strata. The effects of environmental variables were heterogeneous across strata and guilds. Canopy beetles were especially sensitive to early successional conditions, and canopy attributes, but also benefited from the canopy openness. Forest specialists of the ground and canopy responded differently to environmental variables. Ground-dwelling beetles were mostly affected by fires, human modifications, edge closeness, high temperatures, and soil properties, responding weakly to canopy properties. Functional redundancy varied weakly along environmental gradients, being more likely in local communities of ground-dwelling beetles mostly composed of species with overlapping functional roles. Contrasting environmental responses between ground and canopy beetles, as well as among beetles of different trophic guilds, should arise from microhabitats that vary across strata and interact differently with response traits.

Machine learning and remote sensing-based modeling of the optimal stomatal behavior of crops

The slope (g1) of stomatal conductance to photosynthesis is an important parameter in the optimal stomatal behavior theory-based stomatal conductance model of Medlyn et al. (2011). Although studies have modelled the spatial variations in g1, disclosing its variations over environmental gradients and different plant functional types. However, the above methods are still not accurate enough on a global scale, as they do not consider the temporal variations in g1. To address this issue we used the Ensemble Kalman Filter (EnKF) to assimilate tower-based gross primary productivity (GPP) and latent heat flux (LE) of 17 cropland flux sites into a remote sensing (RS)-based evapotranspiration-photosynthesis coupled model, termed SCOPES-Crop, to derive the temporal variations in g1 for C3 and C4 crops. We also used the feedforward artificial neural network (FANN) along with RS variables to model g1. Results showed g1 to rise rapidly in spring and summer, and then decline in autumn. The value of g1 reached the lowest value and remained stable in wintertime. FANN-based modeling of g1 showed R(RMSE) = 0.81 (1.94 kPa0.5) and 0.90 (0.70 kPa0.5) for C3 and C4 Crops, respectively, for the testing dataset. The estimates of GPP and LE using FANN-derived g1 at the 17 flux sites were improved as compared to that using fixed g1. The mean values of site-level R(RMSE) for GPP and LE simulated using FANN-derived g1 are 0.92 (1.8 gC m−2 d-2) and 0.85 (22.5 W m−2), respectively. Our results revealed notable seasonal variations in g1, indicating the importance of considering the temporal variations in g1 in evapotranspiration-photosynthesis coupled model. The FANN along with RS variables showed great potential of representing the g1 variations.

Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

AbstractSoil organic C (SOC) plays an important role in soil quality. Thus, it is of great significance to know the spatial distribution characteristics of SOC. Environmental variables, such as natural predictors, remote sensing (RS) variables, and digital soil mapping approaches have been widely used in SOC prediction. However, it is still challenging to determine which methods and variables are effective to map SOC in farmland. In this study, we compare the performance of three machine learning models, including random forest, Cubist, and the support vector machine model for the prediction of SOC contents using RS variables, topographic variables, climate variables, soil property variables, and spatial position variables as environmental covariates. The recursive feature elimination (RFE) technique was used to select optimal variables. Of the 130 sampling sites of the cultivated layer that were collected in Shanggao county, 70% of the data were used for training and 30% for validation. Prediction accuracy and uncertainty was quantified using the bootstrap approach with 100 iterations. The performance of the model was verified by the mean absolute error, RMSE, R2, and Lin's concordance correlation coefficient (CCC); the SD was used as a measure of prediction uncertainty. Results showed that the Cubist model combined with the RFE method (Cubist_RFE) had a mean R2, RMSE, and SD of .84, 2.39 g kg–1, and 0.73 g kg–1, respectively, which showed the highest prediction accuracy and the lowest uncertainty. The soil property variables (soil alkaline hydrolysis N and pH) and RS variables (soil adjusted vegetation index, enhanced vegetation index, and band7 of Sentinel‐2) were the controlling factors of SOC prediction. Thus, the results indicate the potential of the Cubist model in SOC prediction, and the combination of the optical and radar RS data is a viable way for predicting SOC.

Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables

Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin’s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093.

Soil-Related Predictors for Distribution Modelling of Four European Crayfish Species

One of the most critical challenges in species distribution modelling is testing and validating various digitally derived environmental predictors (e.g., remote-sensing variables, topographic variables) by field data. Therefore, here we aimed to explore the value of soil properties in the spatial distribution of four European indigenous crayfish species. A database with 473 presence and absence locations in Romania for Austropotamobius bihariensis, A. torrentium, Astacus astacus and Pontastacus leptodactylus was used in relation to eight digitalised soil properties. Using random forest modelling, we found a preference for dense soils with lower coarse fragments content together with deeper sediment cover and higher clay values for A. astacus and P. leptodactylus. These descriptors trigger the need for cohesive soil river banks as the microenvironment for building their burrows. Conversely, species that can use banks with higher coarse fragments content, the highland species A. bihariensis and A. torrentium, prefer soils with slightly thinner sediment cover and lower density while not influenced by clay/sand content. Of all species, A. astacus was found related with higher erosive soils. The value of these soil-related digital descriptors may reside in the improvement of approaches in crayfish species distribution modelling to gain adequate conservation measures.

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.

Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods

Precision crop management in modern agriculture requires timely and effective acquisition of crop growth information. Recently, unmanned aerial systems (UASs) have rapidly developed and are now widely used in crop remote sensing (RS). Vegetation index (VI) and color index (CI) are commonly used RS methods to monitor crops. Texture is intrinsic information of the images, which can reflect the crop canopy structure and be used for vegetation classification. The objective of this study was to explore the potential of combining VI, CI, and texture to improve the estimation accuracy of wheat growth parameters based on fixed-wing UAS imagery. Wheat field experiments were carried out at the Xinghua Experimental Station for two consecutive years of 2017–2019 on three wheat cultivars under five nitrogen fertilization rates. Two commonly used wheat growth parameters, leaf area index (LAI) and leaf dry matter (LDM), synchronized with wheat field UAS images, were obtained at key growth stages. Simple regression (SR) was used to determine quantitative relationships between RS variables (VI, CI, and texture) and LAI, LDM. The data showed that individual texture does not correlate well with wheat growth parameters, while a texture index (TI), containing two texture measurements, showed stronger correlation with LAI and LDM. With the utilization of simple regression (SR), VI (R2 > 0.65, RRMSE < 21.87%) exhibited the best accuracy in estimating LAI and LDM, followed by TI (R2 > 0.51, RRMSE < 26.28%) and CI (R2 > 0.34, RRMSE < 27.74%). Multiple linear regression (MLR) and random forest (RF) were further employed to develop LAI and LDM estimation models using different input variable sets (VIs, VIs + CIs, and VIs + CIs + TIs). Compared with SR and MLR, the RF models that combined VIs, CIs, and TIs greatly improved the estimation accuracy of LAI and LDM, and the validated R2 of the best RF models for LAI and LDM estimation reached 0.78 and 0.78 (RRMSE = 17.32% and 13.83%) in pre-heading stages, 0.81 and 0.77 (RRMSE = 17.86% and 16.08%) in post-heading stages, and 0.76 and 0.75 (RRMSE = 18.13% and 16.79%) in all stages, respectively. This study demonstrated that image textures can assist wheat monitoring to achieve higher estimation accuracy of LAI and LDM, and fixed-wing UAS is a promising platform that can provide reliable data for large-scale crop management.

On the contribution of remote sensing-based calibration to model hydrological and hydraulic processes in tropical regions

The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. While calibration is usually performed with discharge estimates, the internal model processes might be misrepresented, and the model might be getting the “right results for the wrong reasons”, thus compromising model reliability. An alternative is to calibrate model parameters with remote sensing (RS) observations of the water cycle. Previous studies highlighted the potential of RS-based calibration to improve discharge estimates, focusing less on other variables of the water cycle. In this study, we analyzed in detail the contribution of five RS-based variables (water level (h), flood extent (A), terrestrial water storage (TWS), evapotranspiration (ET) and soil moisture (W)) to calibrate a coupled hydrologic-hydrodynamic model for a large Amazon sub-basin with extensive floodplains. Single-variable calibration experiments with all variables were able to improve discharge KGE from around 6.1% to 52.9% when compared to a priori parameter sets. Water cycle representation was improved with multi-variable calibration: KGE for all variables were improved in the evaluation period. By analyzing different calibration setups, a consistent selection of complementary variables for model calibration resulted in a better performance than incorporating all RS variables into the calibration. By looking at multiple RS observations of the water cycle, inconsistencies in model structure and parameterization were found, which would remain unknown if only discharge observations were considered.

Introducing the Time Series Change Visualization and Interpretation (TSCVI) method for the interpretation of global NDVI changes

This paper presents a novel method for the visualization of changes in vegetation related variables. This method, termed Time Series Change Visualization and Interpretation (TSCVI), allows to summarize changes associated to both vegetation productivity and phenology in a single map. To that end, three metrics are retrieved on an annual basis from plotting NDVI (Normalized Difference Vegetation Index) values on a polar plot. Changes in these metrics are then analyzed and mapped in an IHS (Intensity Hue Saturation) image, where colors indicate changes regarding the growing-season (earlier or later occurrence, stronger or weaker seasonality), while changes associated to productivity are evidenced by the image intensity. TSCVI metrics are presented for 445 sites corresponding to different land covers, and their sensitivity to dataset characteristics (temporal compositing, presence of clouds) is analyzed. We also applied the TSCVI method to GIMMS NDVI3g data, by analyzing the changes in the TSCVI annual metrics for the periods 1982–1986 and 2009–2013: the resulting map captures well-known changes evidenced in previous studies. Finally, we applied the proposed approach to Sentinel 2 data to visualize charges in the province of Valencia (Spain) between 2018 and 2019. The TSCVI approach can be used for a wide range of remotely-sensed variables in addition to NDVI. All the TSCVI routines used in this work are available freely for download at https://www.uv.es/juy/resources.htm.