The grass is not always greener on the other side: Seasonal reversal of vegetation greenness in aspect-driven semiarid ecosystems

Our current understanding of semiarid ecosystems is that they tend to display higher vegetation greenness on polar-facing slopes (PFS) than on equatorial-facing slopes (EFS). However, recent studies have argued that higher vegetation greenness can occur on EFS during part of the year. To assess whether this seasonal reversal of aspect-driven vegetation is a common occurrence, we conducted a global-scale analysis of vegetation greenness on a monthly time scale over an 18-year period (2000–2017). We examined the influence of climate seasonality on the normalized difference vegetation index (NDVI) values of PFS and EFS at 60 different catchments with aspect-controlled vegetation located across all continents except Antarctica. Our results show that an overwhelming majority of sites (70%) display seasonal reversal, associated with transitions from water-limited to energy-limited conditions during wet winters. These findings highlight the need to consider seasonal variations of aspect-driven vegetation patterns in ecohydrology, geomorphology, and Earth system models

Correction: Kumari et al. A Long-Term Spatiotemporal Analysis of Vegetation Greenness over the Himalayan Region Using Google Earth Engine. Climate 2021, 9, 109

There was an error in the original publication [...

A Long-Term Spatiotemporal Analysis of Vegetation Greenness over the Himalayan Region Using Google Earth Engine

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI

Influence of orographic precipitation on coevolving landforms and vegetation in semi-arid ecosystems

Topography affects the intensity and spatial distribution of precipitation due to orographic lifting mechanisms and, in turn, influences the prevailing climate and vegetation distribution. Previous modelling studies on the impact of orographic precipitation on landform evolution have considered bare-soil conditions. However, research on the effect of changes in precipitation regimes induced by elevation gradients (particularly in aspect-controlled semi-arid ecosystems) on landform patterns trying to understand feedbacks and consequences for coevolving vegetation has been limited. In this study, the channelHillslope Integrated Landscape Development (CHILD) landscape evolution model (LEM) coupled with the vegetation dynamics Bucket Grassland Model (BGM) is used to analyse the coevolution of semi-arid landform-vegetation ecosystems. The CHILD+BGM model is run under different combinations of precipitation and solar radiation settings. Three precipitation settings, including uniform, elevation control, and orographic control on precipitation, are considered in combination with spatially uniform and spatially varied radiation settings. Based on the results, elevation control, aspect, and drainage network are identified as the major drivers of the distribution of vegetation cover on the landscapes. Further, the combination of orographic precipitation and spatially varied solar radiation created the highest asymmetry in the landscape and divide migration due to the emergence of gentler slopes on the windward than the leeward sides of the domain. The modelling outcomes from this study indicate that aspect control of solar radiation in combination with orographic precipitation plays a key role in the generation of topographic asymmetry in semi-arid ecosystems

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Not AvailableCrop coefficient (Kc) represents the actual crop growth of the crop. It plays an important role in estimating waterrequirements at the different growth stages of the crop. However, FAO 56 Penman–MonteithKcmethod does not accountfor spatial heterogeneity and uncertainty for regional climatic conditions significantly. Therefore, this study aims todevelop the relation betweenKcand normalized difference vegetation index (NDVI) using a linear regression and backcalculations. These relationships were adjusted to local conditions using information from survey data obtained duringRabi season (2014–2015). The NDVI–Kcmodel (r2= 0.86) has developed using NDVI–Kcfrom a fine resolution Landsat 8remote sensing data. NDVI–Kcregression equation was utilized for generating crop coefficient for different month ofseason. The Vegetation Index-based AET estimated was evaluated with lysimeter data for different crop growth stageacross the season. The results have shown that NDVI–Kcestimated AET has been better correlated with NDVI–Kcremotesensing model. Thus, the output of this research can help to calculate actual water demand in a command area and behelpful in allocating water from less demand area toward more demand area.Not Availabl

The role of landscape morphology on soil moisture variability in semi-arid ecosystems

Previous studies on semi-arid ecosystems have shown high values of soil moisture variability (SMV) primarily induced by the combined effects of non-uniform precipitation, incoming solar radiation, and soil and vegetation properties. However, the relative impact of these various factors on SMV has been difficult to evaluate due to limited availability of field data. In addition, only a limited number of studies have analysed the role of landscape morphology on SMV. Here we use numerical simulations of a simple hydrological model, the Bucket Grassland Model, to systematically analyse the effect of each contributing factor on SMV on two different landscape morphologies. The two different landform morphologies represent landscapes dominated respectively by either diffusive erosion or fluvial erosion processes. We conducted various simulations driven by a stochastically generated 100-year climate time series, which is long enough to capture climatic fluctuations, in order to understand the effect of various soil moisture controlling factors on the spatiotemporal SMV. Our modelling results show that the fluvial dominated landscapes promote higher spatial SMV than the diffusive dominated ones. Further, the role of landform morphology on SMV is more pronounced in regions where the spatial variability of incoming solar radiation and precipitation is high

Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data

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Not AvailableCrop geometry plays a vital role in ensuring proper plant growth and yield. Check row planting allows adequate space for weeding in both direction and allowing sunlight down to the bottom of the crop. Therefore, a light detection and ranging (LiDAR) navigated electronic seed metering system for check row planting of maize seeds was developed. The system is comprised of a LiDAR-based distance measurement unit, electronic seed metering mechanism and a wireless communication system. The electronic seed metering mechanism was evaluated in the laboratory for five different cell sizes (8.80, 9.73, 10.82, 11.90 and 12.83 mm) and linear cell speed (89.15, 99.46, 111.44, 123.41 and 133.72 mm·s −1 ). The research shows the optimised values for the cell size and linear speed of cell were found to be 11.90 mm and 99.46 mm·s −1 respectively. A light dependent resistor (LDR) and light emitting diode (LED)-based seed flow sensing system was developed to measure the lag time of seed flow from seed metering box to bottom of seed tube. The average lag time of seed fall was observed as 251.2 ± 5.39 ms at an optimised linear speed of cell of 99.46 mm·s −1 and forward speed of 2 km·h −1 . This lag time was minimized by advancing the seed drop on the basis of forward speed of tractor, lag time and targeted position. A check row quality index (ICRQ) was developed to evaluate check row planter. While evaluating the developed system at different forward speeds (i.e., 2, 3 and 5 km·h −1 ), higher standard deviation (14.14%) of check row quality index was observed at forward speed of 5 km·h −1 .Not Availabl

Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches

Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin

Response of Contrasting Nutrient Management Regimes on Soil Aggregation, Aggregate-Associated Carbon and Macronutrients in a 43-Year Long-Term Experiment

The present investigation evaluated the effect of continuous application (>43 years) of organic and inorganic fertilisers on soil aggregate stability, aggregate size distribution, aggregate-associated carbon and its fractions, and total macro-nutrient content under the soybean–wheat cropping system in vertisols of the semi-arid region. Seven contrasting treatments consisted of T1 (50% NPK), T2 (100% NPK), T3 (150% NPK), T4 (100% NP), T5 (100% N), T6 (100% NPK + FYM) and T7 Control (crop raised without addition of any nutrient). The highest and lowest percentage of large macroaggregates (11.3%) was found in T6 and T7 treatments. The NPK + FYM (T6) treatments substantially increased the proportion of the macroaggregate fractions (>2 mm and 2–0.25 mm) than other treatments. However, different manure and fertilisation treatments did not affect the proportion of silt + clay aggregates. Long-term application of 100% NPK + FYM increased mean weight diameter (MWD) and stable water aggregates (WSA) by 35.7 and 6.01% over control. The aggregate-associated SOC followed the trend of large macroaggregates > microaggregates > small macroaggregates > silt + clay fractions. Application of long-term manure plus inorganic fertiliser (T6) has also increased Walkley Black soil organic carbon (WBSC), permanganate oxidisable carbon (KMnO4-C), soil microbial biomass carbon (SMBC), carbon mineralisation (CM), total soil carbon (TSC), total soil N (TSN), total soil phosphorus (TSP) and total soil potassium (STK) by 82.1, 71.6, 182, 42.4, 23.9, 41.6, 117 and 18.4%, respectively, over control (T7). The lowest metabolic quotient (MetQ) value of 5.13 mg CO2–C mg−1 MBC h−1 was obtained in the control treatment (T7). The lowest MetQ was recorded in the integrated application of manure + inorganic fertiliser, i.e., 100% NPK + FYM (T6). Similarly, microbial quotient (MiQ) was also higher in treatment T6 (100% NPK + FYM) and lower in T7 (control). It is concluded that the application of inorganic fertiliser alone is insufficient to maintain soil health and sustainability so, combined application of manure plus inorganic fertilisation is the most important nutrient management practice for long-term soil sustainability because it maintains SOC levels in soils for long periods and ultimately ensures the soil health of soybean–wheat cropping systems in the vertisols of semi-arid regions