Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Biological soil crusts on agricultural soils of mesic regions promote microbial cross-kingdom co-occurrences and nutrient retention

Introduction

Biological soil crusts (biocrusts) are known as biological hotspots on undisturbed, nutrient-poor bare soil surfaces and until now, are mostly observed in (semi-) arid regions but are currently poorly understood in agricultural systems. This is a crucial knowledge gap because managed sites of mesic regions can quickly cover large areas. Thus, we addressed the questions (i) if biocrusts from agricultural sites of mesic regions also increase nutrients and microbial biomass as their (semi-) arid counterparts, and (ii) how microbial community assemblage in those biocrusts is influenced by disturbances like different fertilization and tillage regimes.

Methods

We compared phototrophic biomass, nutrient concentrations as well as the abundance, diversity and co-occurrence of Archaea, Bacteria, and Fungi in biocrusts and bare soils at a site with low agricultural soil quality.

Results and Discussion

Biocrusts built up significant quantities of phototrophic and microbial biomass and stored more nutrients compared to bare soils independent of the fertilizer applied and the tillage management. Surprisingly, particularly low abundant Actinobacteria were highly connected in the networks of biocrusts. In contrast, Cyanobacteria were rarely connected, which indicates reduced importance within the microbial community of the biocrusts. However, in bare soil networks, Cyanobacteria were the most connected bacterial group and, hence, might play a role in early biocrust formation due to their ability to, e.g., fix nitrogen and thus induce hotspot-like properties. The microbial community composition differed and network complexity was reduced by conventional tillage. Mineral and organic fertilizers led to networks that are more complex with a higher percentage of positive correlations favoring microbe-microbe interactions. Our study demonstrates that biocrusts represent a microbial hotspot on soil surfaces under agricultural use, which may have important implications for sustainable management of such soils in the future.

Towards large scale biocrust restoration: Producing an efficient and low-cost inoculum of N-fixing cyanobacteria

Dryland soil degradation is increasing due to global change and traditional restoration methods are not successful due to water scarcity. Thus, an alternative technology based on inoculating biocrust-forming cyanobacteria on degraded soils has emerged. Biocrusts are communities of mosses, lichens, cyanobacteria or fungi that colonize soil surface forming a stable and fertile layer. Previous studies have shown the benefits of inoculating cyanobacteria to restore soils at a small scale. However, to face field restoration projects, it is necessary to produce high quantities of biomass at an affordable cost. In this work, we analyze if the previously tested cyanobacteria Scytonema hyalinum, Tolypothrix distorta (heterocystous strains) and Trichocoleus desertorum (a bundle-forming one) can be produced with agricultural fertilizers. Different culture media were used: two containing pure chemicals (BG11 and BG11₀, this N-free medium was used just for heterocystous strains) and two containing fertilizers (BG11-F and MM-F). The performance of the cultures was monitored by measuring the biomass concentration and photosynthetic stress. Afterwards, we analyzed their capacity to induce biocrusts and improve soil properties by inoculating the biomass on a mine substrate indoors and measuring, three months later, the albedo, chlorophyll a and organic carbon content. Results show that the bundle-forming cyanobacterium was unable to grow in the media tested, whereas both heterocystous cyanobacteria grew in all of them and induced the formation of biocrusts improving the organic carbon substrate content. The best results for S. hyalinum were found using the MM-F medium, and for T. distorta using a medium containing pure chemicals (BG11). However, results were also positive when using a medium containing fertilizers (BG11-F). Thus, agricultural fertilizers can be used to undertake the production of heterocystous cyanobacteria for large scale restoration in drylands. On the other hand, more research is needed to find sustainable techniques to produce biomass of bundle-forming cyanobacteria.

In Living Color: Pigment-Based Microbial Ecology At the Mineral-Air Interface

Pigment-based color is one of the most important phenotypic traits of biofilms at the mineral-air interface (subaerial biofilms, SABs), because it reflects the physiology of the microbial community. Because color is the hallmark of all SABs, we argue that pigment-based color could convey the mechanisms that drive microbial adaptation and coexistence across different terrestrial environments and link phenotypic traits to community fitness and ecological dynamics. Within this framework, we present the most relevant microbial pigments at the mineral-air interface and discuss some of the evolutionary landscapes that necessitate pigments as adaptive strategies for resource allocation and survivability. We report several pigment features that reflect SAB communities' structure and function, as well as pigment ecology in the context of microbial life-history strategies and coexistence theory. Finally, we conclude the study of pigment-based ecology by presenting its potential application and some of the key challenges in the research.

UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust

Biocrusts (topsoil communities formed by mosses, lichens, bacteria, fungi, algae, and cyanobacteria) are a key biotic component of dryland ecosystems. Whilst climate patterns control the distribution of biocrusts in drylands worldwide, terrain and soil attributes can influence biocrust distribution at landscape scale. Multi-source unmanned aerial vehicle (UAV) imagery was used to map and study biocrust ecology in a typical dryland ecosystem in central Spain. Red, green and blue (RGB) imagery was processed using structure-from-motion techniques to map terrain attributes related to microclimate and terrain stability. Multispectral imagery was used to produce accurate maps (accuracy > 80%) of dryland ecosystem components (vegetation, bare soil and biocrust composition). Finally, thermal infrared (TIR) and multispectral imagery was used to calculate the apparent thermal inertia (ATI) of soil and to evaluate how ATI was related to soil moisture (r 2 = 0.83). The relationship between soil properties and UAV-derived variables was first evaluated at the field plot level. Then, the maps obtained were used to explore the relationship between biocrusts and terrain attributes at ecosystem level through a redundancy analysis. The most significant variables that explain biocrust distribution are: ATI (34.4% of variance, F = 130.75; p < 0.001), Elevation (25.8%, F = 97.6; p < 0.001), and potential solar incoming radiation (PSIR) (52.9%, F = 200.1; p < 0.001). Differences were found between areas dominated by lichens and mosses. Lichen-dominated biocrusts were associated with areas with high slopes and low values of ATI, with soil characterized by a higher amount of soluble salts, and lower amount of organic carbon, total phosphorus (Ptot) and total nitrogen (Ntot). Biocrust-forming mosses dominated lower and moister areas, characterized by gentler slopes and higher values of ATI with soils with higher contents of organic carbon, Ptot and Ntot. This study shows the potential to use UAVs to improve our understanding of drylands and to evaluate the control that the terrain has on biocrust distribution.

Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland

Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs’ activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the ‘Lieberoser Heide’ in northeastern Germany, is home to the country’s largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands.

Moss-biocrusts strongly decrease soil surface albedo, altering land-surface energy balance in a dryland ecosystem

Land surface albedo measures the degree to which the sun's radiation is absorbed or reflected, and thus can be highly influential in global climate trends, local weather phenomena, and biological processes. As an extensive living cover in drylands, biocrusts cover substantial land surface but their potential influences on surface albedo and energy balance are underdocumented, and its temporal dynamic is virtually unknown. We continuously measured the surface albedo, land-surface energy balance, temperature and moisture of moss-biocrust covered soil and bare soil for two years, and measured the surface color and roughness of the two land cover types. Our results showed that the surface albedo of the biocrusts was 43.4% lower than that of the bare soil, due to the increased darkness (43.7%) and roughness (90.4%) together with increased moisture (20.7%) of the biocrust layer. Through time, the albedo of the biocrusts were negatively and linearly related with surface soil temperature or moisture, which resulted in lower albedo in summer and higher albedo in other seasons. As a result of decreased albedo, biocrusts decreased outgoing short-wave radiation by 44.8% in comparison to the bare soil, and consequently they increased net short-wave radiation by 11.4% and net all-wave solar radiation by 22.9% However, the increased energy absorption by the biocrusts did not consistently increase soil temperature; instead, soil temperature increased by up to 9.3 °C under dry conditions but decreased by as much as 11.4 °C under wet conditions, resulting in a net cooling. This indicates that the temperature regimes of the biocrust-covered soil were not determined only by albedo, but also by modification of soil thermal properties by biocrusts. Because biocrusts are highly responsive to land use, it appears that altered albedo and energy balance may be one of the ways in which human activity can impact climate and weather.

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).

Spectral Response Assessment of Moss-Dominated Biological Soil Crust Coverage Under Dry and Wet Conditions

Biological soil crusts (BSCs) are a major functional vegetation unit, covering extensive parts of drylands worldwide. Therefore, several multispectral indices have been proposed to map the spatial distribution and coverage of BSCs. BSCs are composed of poikilohydric organisms, the activity of which is sensitive to water availability. However, studies on dry and wet BSCs have seldom considered the mixed coverage gradient that is representative of actual field conditions. In this study, in situ spectral data and photographs of 136 pairs of dry and wet plots were collected to determine the influence of moisture conditions on BSC coverage detection. Then, BSC spectral reflectance and continuum removal (CR) reflectance responses to wetting were analyzed. Finally, the responses of four commonly used indices (i.e., normalized difference vegetation index (NDVI); crust index (CI); biological soil crust index (BSCI); and band depth of absorption feature after CR in the red band, (BD_red)), calculated from in situ hyperspectral data resampled to two multispectral data channels (Landsat-8 and Sentinel-2), were compared in dry and wet conditions. The results indicate that: (i) on average, the estimated BSC coverage using red-green-blue (RGB) images is 14.98% higher in wet than in dry conditions (P < 0.001); (ii) CR reflectance features of wet BSCs are more obvious than those of dry BSCs in both red and red-edge bands; and (iii) NDVI, CI, and BSCI for BSC coverage of 0%–60% under dry and wet conditions are close to those of dry and wet bare sand, respectively. NDVI and BD_red cannot separate dead wood and BSC with low coverage. This study demonstrates that low-coverage moss-dominated BSC is not easily detected by the four indices. In the future, remote-sensing data obtained during the rainy season with red and red-edge bands should be considered to detect BSCs.

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

Water Regulation in Cyanobacterial Biocrusts from Drylands: Negative Impacts of Anthropogenic Disturbance

Arid and semi-arid ecosystems are characterized by patchy vegetation and variable resource availability. The interplant spaces of these ecosystems are very often covered by cyanobacteria-dominated biocrusts, which are the primary colonizers of terrestrial ecosystems and key in facilitating the succession of other biocrust organisms and plants. Cyanobacterial biocrusts regulate the horizontal and vertical fluxes of water, carbon and nutrients into and from the soil and play crucial hydrological, geomorphological and ecological roles in these ecosystems. In this paper, we analyze the influence of cyanobacterial biocrusts on water balance components (infiltration-runoff, evaporation, soil moisture and non-rainfall water inputs (NRWIs)) in representative semiarid ecosystems in southeastern Spain. The influence of cyanobacterial biocrusts, in two stages of their development, on runoff-infiltration was studied by rainfall simulation and in field plots under natural rainfall at different spatial scales. Results showed that cover, exopolysaccharide content, roughness, organic carbon, total nitrogen, available water holding capacity, aggregate stability, and other properties increased with the development of the cyanobacterial biocrust. Due to the effects on these soil properties, runoff generation was lower in well-developed than in incipient-cyanobacterial biocrusts under both simulated and natural rainfall and on different spatial scales. Runoff yield decreased at coarser spatial scales due to re-infiltration along the hillslope, thus decreasing hydrological connectivity. Soil moisture monitoring at 0.03 m depth revealed higher moisture content and slower soil water loss in plots covered by cyanobacterial biocrusts compared to bare soils. Non-rainfall water inputs were also higher under well-developed cyanobacterial biocrusts than in bare soils. Disturbance of cyanobacterial biocrusts seriously affected the water balance by increasing runoff, decreasing soil moisture and accelerating soil water loss, at the same time that led to a very significant increase in sediment yield. The recovery of biocrust cover after disturbance can be relatively fast, but its growth rate is strongly conditioned by microclimate. The results of this paper show the important influence of cyanobacterial biocrust in modulating the different processes supporting the capacity of these ecosystems to provide key services such as water regulation or erosion control, and also the important impacts of their anthropic disturbance.

Spectral Diversity Successfully Estimates the α-Diversity of Biocrust-Forming Lichens

Biocrusts, topsoil communities formed by mosses, lichens, liverworts, algae, and cyanobacteria, are a key biotic component of dryland ecosystems worldwide. Experiments carried out with lichen- and moss-dominated biocrusts indicate that climate change may dramatically reduce their cover and diversity. Therefore, the development of reproducible methods to monitor changes in biocrust diversity and abundance across multiple spatio-temporal scales is key for evaluating how climate change may impact biocrust communities and the myriad of ecosystem functions and services that rely on them. In this study, we collected lichen-dominated biocrust samples from a semi-arid ecosystem in central Spain. Their α-diversity was then evaluated using very high spatial resolution hyperspectral images (pixel size of 0.091 mm) measured in laboratory under controlled conditions. Support vector machines were used to map the biocrust composition. Traditional α-diversity metrics (i.e., species richness, Shannon’s, Simpson’s, and Pielou’s indices) were calculated using lichen fractional cover data derived from their classifications in the hyperspectral imagery. Spectral diversity was calculated at different wavelength ranges as the coefficient of variation of different regions of the reflectance spectra of lichens and as the standard deviation of the continuum removal algorithm (SD_CR). The accuracy of the classifications of the images obtained was close to 100%. The results showed the best coefficient of determination (r2 = 0.47) between SD_CR calculated at 680 nm and the α-diversity calculated as the Simpson’s index, which includes species richness and their evenness. These findings indicate that this spectral diversity index could be used to track spatio-temporal changes in lichen-dominated biocrust communities. Thus, they are the first step to monitor α-diversity of biocrust-forming lichens at the ecosystem and regional levels, a key task for any program aiming to evaluate changes in biodiversity and associated ecosystem services in drylands.

Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy

The spectral reflectance of crop canopy is a spectral mixture, which includes soil background as one of the components. However, as soil is characterized by substantial spatial variability and temporal dynamics, its contribution to the spectral reflectance of crops will also vary. The aim of the research was to determine the impact of soil background on spectral reflectance of crop canopy in visible and near-infrared parts of the spectrum at different stages of crop development and how the soil type factor and the dynamics of soil surface affect vegetation indices calculated for crop assessment. The study was conducted on three test plots with winter wheat located in the Tula region of Russia and occupied by three contrasting types of soil. During field trips, information was collected on the spectral reflectance of winter wheat crop canopy, winter wheat leaves, weeds and open soil surface for three phenological phases (tillering, shooting stage, milky ripeness). The assessment of the soil contribution to the spectral reflectance of winter wheat crop canopy was based on a linear spectral mixture model constructed from field data. This showed that the soil background effect is most pronounced in the regions of 350–500 nm and 620–690 nm. In the shooting stage, the contribution of the soil prevails in the 620–690 nm range of the spectrum and the phase of milky ripeness in the region of 350–500 nm. The minimum contribution at all stages of winter wheat development was observed at wavelengths longer than 750 nm. The degree of soil influence varies with soil type. Analysis of variance showed that normalized difference vegetation index (NDVI) was least affected by soil type factor, the influence of which was about 30%–50%, depending on the stage of winter wheat development. The influence of soil type on soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI2) was approximately equal and varied from 60% (shooting phase) to 80% (tillering phase). According to the discriminant analysis, the ability of vegetation indices calculated for winter wheat crop canopy to distinguish between winter wheat crops growing on different soil types changed from the classification accuracy of 94.1% (EVI2) in the tillering stage to 75% (EVI2 and SAVI) in the shooting stage to 82.6% in the milky ripeness stage (EVI2, SAVI, NDVI). The range of the sensitivity of the vegetation indices to the soil background depended on soil type. The indices showed the greatest sensitivity on gray forest soil when the wheat was in the phase of milky ripeness, and on leached chernozem when the wheat was in the tillering phase. The observed patterns can be used to develop vegetation indices, invariant to second-type soil variations caused by soil type factor, which can be applied for the remote assessment of the state of winter wheat crops.