Land Surface Water Research Articles

Reply to Comment on “Improved Water Savings and Reduction in Moist Heat Stress Caused by Efficient Irrigation” by Meetpal S. Kukal

AbstractIrrigation has a considerable influence on land surface energy and water budgets. Irrigation‐related processes influence energy partitioning, planetary boundary layer, land surface temperature, evapotranspiration, and runoff. However, observations related to irrigation and its influence on land surface hydrology and climate are limited. Among the limited observations available, most are from the field scale experiments that do not provide information on the regional scale impacts of irrigation. While land surface and climate models do not represent the complexity of field‐scale irrigation, these modeling experiments provide valuable insights into regional‐scale effects. We respond to a recent comment on our modeling study of irrigation impacts on regional climate. We acknowledge the limitations of our modeling experiments. However, we aimed to provide an experimental framework to understand the role of improved irrigation efficiency in water savings and in reducing moist heat stress at the regional scale.

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice, which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop, plays an important role in both food security and agroecology while requiring minimal agricultural inputs. However, accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems (e.g., double rice). Moreover, images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather. In this study, taking Qichun County in Hubei Province, China as an example, we developed a new phenology-based ratoon rice vegetation index (PRVI) for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2 (HLS) images. The PRVI that incorporated the red, near-infrared, and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection. Based on actual field samples, the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and land surface water index (LSWI). The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice, leading to a favorable separability between ratoon rice and other land cover types. Furthermore, the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop (GHS-TS2), indicating that only several images are required to obtain an accurate ratoon rice map. Finally, the PRVI performed better than NDVI, EVI, LSWI and their combination at the GHS-TS2 stages, with producer’s accuracy and user’s accuracy of 92.22 and 89.30%, respectively. These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages, which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

Present and Future Changes in Land‐Atmosphere Coupling of Water and Energy Over Extratropical Forest Regions

AbstractCouplings between land and the near surface atmosphere are modulated by interactions between soil conditions, vegetation dynamics, turbulent fluxes, and atmospheric properties. How the land‐atmosphere coupling responds to warming and elevated CO2 are important for understanding the land surface carbon, energy, and water cycles. In this work, we documented this coupled land‐atmosphere network based on observations and the Energy Exascale Earth System Model (E3SM) simulations over extratropical forest ecosystems. We employed a transfer entropy approach and novel network metrics to reveal patterns and strength of the land‐atmosphere coupling under historical conditions and a future high emission scenario (SSP585). We found that, in observations, the present‐day extratropical forest coupling network has high network connectivity (72%–88% of the targeted processes are coupled). E3SM reasonably captured the extratropical forest coupling network (modeled network connectivity was 81%–96%) and predicted that the coupling strength would significantly increase by 28% (±3%) under warming and elevated CO2 conditions. Furthermore, E3SM factorial coupled experiments suggested that warming enhanced soil nitrogen mineralization favoring plant nitrogen uptake and vegetation growth were responsible for the strengthening future land‐atmosphere coupling. This work provides new metrics to analyze and document complex couplings for coupled earth system processes and highlights the important roles soil nutrient availability and biogeochemistry have on land‐atmosphere coupling.

Mapping Forage Biomass and Quality of the Inner Mongolia Grasslands by Combining Field Measurements and Sentinel-2 Observations

Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CFarea), and crude protein per area (CParea) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R2 = 0.67, NRMSE = 12%), CFarea (R2 = 0.59, NRMSE = 14%), and CParea (R2 = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R785-900 and R2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R2 = 0.53; NRMSE = 14%; CFarea, R2 = 0.40, NRMSE = 17%; CParea, R2 = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CFarea, and CParea all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management.

Projection of net primary production under changing environment in Xinjiang using an improved wCASA model

Net Primary Productivity (NPP) is an important component of the carbon cycle of terrestrial ecosystems and plays an important role in the evaluation of vegetation growth. Among all the factors, wind also plays a vital role in modeling vegetation productivity. However, none of the current models have considered the impact of wind on the process of productivity estimation. An in-depth study was conducted on the temporal and spatial distribution and internal relations between wind speed and NPP. By introducing the influence factor wind, the Carnegie-Ames-Stanford model (CASA) based on the land-surface water index was modified and named as wCASA model. The estimation and prediction results of wCASA model considering the influence factor wind were better than that of the CASA model. The modified wCASA model was applied in Xinjiang, China. The simulation results were improved with the coefficient of determination (R2) increased by 9.59%, the root mean square error (RMSE) decreased by 13.78%, and the residual of prediction deviation (RPD) improved by 12.08% as compared to CASA model. The optimal model was used to simulate and predict the NPP of cropland and grassland for 2022–2050 under three climate scenarios, SSP126, SSP245, and SSP585, respectively, based on the CMIP6 dataset. The wCASA model can accurately estimate and predict NPP, which provides scientific basis for efficient agricultural water use and food production.

Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data

Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky–Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps.

Reconstructing long-term global satellite-based soil moisture data using deep learning method

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.

An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona

AbstractCities in warmer climate zones have experienced extreme heat and unbearable drought conditions due to climate change. Developing a practical and integrated framework to tackle the trade‐off between land surface temperature (LST) reduction and water conservation is essential for heat mitigation and resilience planning. We developed a multi‐objective framework of spatial optimization for priority areas that considers environmental justice. We identified the priority areas (i.e., residential districts, socio‐economically disadvantaged neighborhoods, hotspot regions, and opportunity areas) using ECOSTRESS‐based LST, actual evapotranspiration (ETa, as a proxy to outdoor water use [OWU]), Landsat‐based LST and ETa changes (2000–2020), and the evaporative stress index. The configuration of vegetation was spatially optimized to achieve the highest co‐benefits by equally balancing both objectives. We found that LST decreased and OWU increased as the vegetation percentage rose from 25% to 45%. The trade‐off between heat reduction and water conservation indicates that LST is not significantly reduced, as OWU reaches above the mid‐level if we focus mainly on LST reduction. The associated green space configurations suggest that grass coverage gradually increases as tree coverage decreases slightly in unmanaged soil. Overall, hotspot regions have a higher potential to lower LST by consuming less water than other areas with the same percentage of new vegetation. Based on this perspective, we suggest that urban planners and city managers prioritize hotspot regions by considering environmental justice and co‐benefits to ensure highly efficient LST amelioration and OWU conservation.

Land Use Land Cover (LULC) and Surface Water Quality Assessment in and around Selected Dams of Jharkhand using Water Quality Index (WQI) and Geographic Information System (GIS)

Abstract Surface water quality deterioration is mainly occurring due to anthropogenic activities at an alarming rate in developing countries. Jharkhand has been undergoing exponential urbanisation and mining, causing immense surface water pollution and water stress. The state is heavily dependent on artificial dams for its daily water supply demands. Therefore, an effort is made to monitor and ascertain the surface water quality and the influence of nearby land use pattern on water quality, in the selected five dams, namely, Hatia dam, Kanke dam, Getalsud dam, Galudih barrage, and Chandil dam are done. These dams are built on the Subarnarekha river basin, located in the Jharkhand state on a seasonal basis and associated land use land cover (LULC) changes, changes in vegetation cover using normalised difference vegetation index (NDVI) and water body changes using normalised difference water index (NDWI) that have occurred in a 5-year gap i.e. 2016 and 2021. The secondary data for the year 2016 was obtained from the Jharkhand pollution control board report published by the government of Jharkhand, India. For the year 2021, the samples were collected from sampling sites for pre, post and monsoon seasons. The chemical analysis of collected water samples was done in the laboratory for parameters like pH, dissolved oxygen, biological oxygen demand, total calcium and magnesium, hardness, total dissolved and suspended solids, alkalinity, chlorine etc. and compared with the standard values prescribed by world health organisation (WHO) and Indian standards (IS) 10500:2012. The seasonal water quality status was analysed using the water quality index (WQI) for the pre, post and monsoon seasons of 2016 and 2021. Then, the use of supervised classification method for land use land cover (LULC), normalised difference vegetation index (NDVI) and normalised difference water index (NDWI) was opted to understand the relation between the change in water quality and quantity concerning its land use and land cover, by comparison of results from the year 2016 to 2021. LULC were found using the supervised maximum likelihood classification method in ArcGIS and its accuracy was checked using the kappa accuracy method, which was found to be varying from 87 to 95% for all sites. The results showed that the overall water quality varied from good to poor indicating that it can be used for human activities but may need pre-treatment before drinking. NDWI showed a massive increase in severe drought areas for Hatia, Kanke, Chandil and Galudih barrage, whereas moderate drought regions increased for Hatia, Getalsud, and Kanke. NDVI showed dense and moderate vegetation both decreased massively for all the dam sites indicating an alarming situation and the need to adopt better land management practices.

Towards uninterrupted canopy-trait time-series: A Bayesian radiative transfer model inversion using multi-sourced satellite observations

Canopy traits play an important role in plant growth, carbon sequestration, and the land surface water and energy balance, and thus it is important to understand and monitor how these traits change over time. Canopy radiative transfer models, which use canopy traits to predict spectra, provide a powerful means to understand canopy trait variation and these models can be inverted, allowing us to use remotely sensed data to monitor canopy traits. However, a single inversion represents a snapshot in time for a single sensor. In this study, we used a Bayesian approach to assimilate snow-free observations from three different sensors (Landsat 8, Sentinel-2A and MODIS) over the course of 2019 by inverting the ProSAIL radiative transfer model, producing a robust estimate of six canopy traits and their joint uncertainties at each time point. Next, we used a state-space time series model to integrate observations from multiple sensors to capture variation in vegetation optical traits throughout the year. The results demonstrate that our model can capture seasonal variation of surface reflectance with less than 5% RMSE compared with observed reflectance. Moreover, linking observations in time narrows the uncertainty intervals compared to the posterior distributions for individual time points. We are also able to make statistical inferences on dates when no data is available, and provide reasonable and reliable time-series inversion results compared with LAI ground measurements.

Inspects and prospects of satellite remote sensing monitoring ability for land surface water in China

Inspects and prospects of satellite remote sensing monitoring ability for land surface water in China

Influence of Pixel Quality, Land Cover, and Hydroclimatic Cycle on Moderate Resolution Imaging Spectroradiometer Inundation Monitoring Performance in the Pantanal, Brazil

Moderate resolution imaging spectroradiometer (MODIS) time series (TS) have been widely applied for flood monitoring in large tropical wetlands. However, little systematic work is available on the influence of pixel quality, vegetation cover, and the annual hydroclimatic cycle on classification performance. In this study, this issue is examined based on a six-year, 250 m resolution MOD13Q1 TS underpinned by extensive in situ measurements. The most parsimonious logistic regression model was obtained for land surface water index (LSWI) and enhanced vegetation index (EVI). The inclusion of the 500 m MCD12Q1 land cover Type 2 product improves accuracy. Performance markedly decreases for subsets that include pixels with a VI quality assurance (QA) level poorer than 0110 and/or a pixel reliability (PR) of three. When a Savitzky-Golay filter was used for TS reconstitution, performance is slightly lower than those obtained in a classification of a VI QA 0001 or PR = 0 level strata; moreover, these have the advantage of gap-free flood monitoring. The overall accuracy (OA) of the PR = 0 subset is better for grasslands, and slightly lower for Savannah, and for woodland and forests. The average OA is highest for the dry season, intermediate for the rainy/flooded season, and lowest for the transitional seasons, when the wetland becomes flooded or dries. Comparisons of internal, k-fold, and external validations indicate that only external validation enables a realistic assessment of flood-mapping performance. The complete substitution of PR = 3 pixels by filled-in values is recommended for operational flood monitoring, and it is concluded that the use of the simplified PR metrics as filtering criteria for gap filling and smoothing is sufficient for flood monitoring in the Pantanal. Classification metrics vary more strongly as a function of the hydrological period than by vegetation cover. MOD13Q1 users should be aware that OA in forest stands during the transition seasons are, on average, 25 p.p. lower than the average OAs obtained for the entire series.

Accumulated soil moisture deficit better indicates the effect of soil water stress on light use efficiency of grasslands during drought years

Light use efficiency (LUE) models have been widely used in the estimation of gross primary productivity (GPP). However, many studies indicated that current LUE models generally overestimate GPP during drought years. Recent studies also showed that soil moisture (SM) regulated LUE during drought years, indicating the necessity of adding SM-related stress scalars in LUE models. Meanwhile, considering the persistence characteristic of drought, the effect of SM stress on LUE is expected to be not only instantaneous but also accumulated. However, it is unclear whether the accumulated SM stress can better capture LUE. Thus, based on observations from grassland flux sites in the United States and Australia, we compared the effectiveness of concurrent soil moisture deficit (SMD), lagged soil moisture deficit (LSMD), and accumulated soil moisture deficit (ASMD) in capturing actual LUE during drought years. ASMD had more stable and stronger linear correlations with LUE than SMD and LSMD at almost all the sites. Furthermore, ASMD outperformed water stress scalars commonly used in LUE models such as the Land Surface Water Index (LSWI) and vapor pressure deficit (VPD) for tracking actual LUE during drought years. The introduction of ASMD improved the accuracy of the LUE model for simulating grassland GPP during drought years (with a R2 and RMSE of 0.88 and 1.06 g C m−2 d−1). Our study highlights the accumulated effect of SM in regulating LUE, and proves that ASMD has the potential in improving the performance of the existing LUE models during drought years.

A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery

Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation of “small farmers in a big country”. However, crop rotation mapping for smallholder agriculture systems remains challenging due to the diversity of crop types, complex cropping practices, and fragmented cropland. This research established a sub-seasonal crop information identification framework for crop rotation mapping based on time series Sentinel-2 imagery. The framework designed separate identification models based on the different growth seasons of crops to reduce interclass similarity caused by the same crops in a certain growing season. Features were selected separately according to crops characteristics, and finally explored rotations between them to generate the crop rotation map. This framework was evaluated in the study area of Shandong Province, China, a mix of single-cropping and double-cropping smallholder area. The accuracy assessment showed that the two crop maps achieved an overall accuracy of 0.93 and 0.85 with a Kappa coefficient of 0.86 and 0.80, respectively. The results showed that crop rotation practice mainly occurred in the plains of Shandong, and the predominant crop rotation pattern was wheat and maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation Index (SAVI), Green Chlorophyll Vegetation Index (GCVI), red-edge, and other spectral bands during the peak growing season enabled better performance in crop mapping. This research demonstrated the capability of the framework to identify crop rotation patterns and the potential of the multi-temporal Sentinel-2 for crop rotation mapping under smallholder agriculture system.

Spatiotemporal variation characteristics of hourly soil temperature in different layers in the low-latitude plateau of China

Soil temperature change has considerable impact on land surface energy and water balances, and hence on changes in weather/climate, surface/subsurface hydrology, and ecosystems. However, little is known regarding the spatiotemporal variations and influencing factors of changes in hourly soil temperature (depth: 5–320 cm) in low-latitude highland areas. This study analyzed the hourly soil temperature at each hour during 2004–2020 and at 08:00, 14:00, and 20:00 (Beijing Time) during 1961–2020. The results revealed the following. 1) As soil depth increased, average soil temperature increased in autumn and winter, and decreased annually and in spring and summer. It exhibited significant increase during 00:00–23:00 annually, seasonally, and monthly, especially at depths of 40–320 cm during 2004–2020. Average soil temperature increased at 08:00 and decreased at 14:00 and 20:00 with increasing soil depth, but the opposite trend was found annually, seasonally, and monthly at 08:00, 14:00, and 20:00 during 1961–2020. 2) With increasing elevation, average soil temperature decreased at 08:00, 14:00, and 20:00 at depths of 5–20 cm, and showed significant increase trend at 08:00 and 14:00 at depths of 10–20 cm (except at 14:00 at 10-cm depth). 3) At 5-cm depth, the critical accumulated soil temperature of ≥12°C and 14°C extended the potential growing season during 1961–2020. 5) Significant uptrend of hourly soil temperature annually, seasonally, and monthly potentially leads to additional release of carbon to the atmosphere and increased soil respiration, reinforcing climate warming. These findings contribute to better understanding of the variation of shallow soil temperatures and land–atmosphere interactions in low-latitude highland areas.

Analysis of Land Use Change Effects/Impacts on Surface Water Resources in Delhi

Rapid urbanization and haphazard development derive the changes in land uses and affect the naturally available resources which are essential for human development and other lives. Land use changes can undermine the environment and ecology of an urban area. Although many studies on the land use changes, trends, status, directions, and the relationship between them have been conducted for Chinese cities, none of them have been completed for Indian cities and also not for NCT Delhi. The aim of the study is to analyze the impact of land use changes on surface water resources. So, this study aims to analyze the effects of land use changes on surface water resources in NCT Delhi, one water-stressed city in India. The analysis is comprised of changes, trends, status, and directions for surface water resources and other types of land use for showing the effects. Comprehensive tools such as remote sensing, GIS, and the cross-tabulation method are used for the assessment of land use changes, trends, and status. Four decadal (1990, 2000, 2010, 2020) satellite maps have been used to study the temporal-spatial data of several land uses and to calculate the index of land use changes for investigating the trends and status. In the form of results, the comprehensive net change (18.28%) and total change (49.28%) with a trend value of 0.37 show the quasi-balanced, two-way transition and positive changes in the whole area. This metrics-based study shows that surface water resources land use type is decreasing, and built-up land use type is increasing since 1990. Population growth, economic and industrial development were the major factors for the variations in built-up, green, and other land uses. This metrics-based analysis study is an important perspective for protecting urban water bodies from effects of land use changes. These understandings on land use changes and temporal-spatial relationships are important for present and future land use development and surface water resource planning. This study will help the Delhi Government’s initiatives for the rejuvenation of urban water bodies by endorsing the land use regulations on surrounding land uses.

Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria

BackgroundInformation addressing soil quality in developing countries often depends on results from small experimental plots, which are later extrapolated to vast areas of agricultural land. This approach often results in misinformation to end-users of land for sustainable soil nutrient management. The objective of this study was to estimate the spatial variability of soil quality index (SQI) at regional scale with predictive models using soil–environmental covariates.MethodsA total of 110 composite soil samples (0–30 cm depth) were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State, Nigeria, and selected soil physical and chemical properties were determined. We employed environmental covariates derived from a digital elevation model (DEM) and Sentinel-2 imageries for our modelling regime. We measured soil quality using two approaches [total data set (TDS) and minimum data set (MDS)]. Two scoring functions were also applied, linear (L) and non-linear (NL), yielding four indices (MDS_L, MDS_NL, TDS_L, and TDS_NL). Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis (PCA). Random forest (RF), support vector regression (SVR), regression kriging (RK), Cubist regression, and geographically weighted regression (GWR) were applied to predict SQI in unsampled locations.ResultsThe computed SQI via MDS_L was classified into five classes: le 0.38, 0.38–0.48, 0.48–0.58, 0.58–0.68, and ge 0.68, representing very low (class V), low (class IV), moderate (class III), high (class II) and very high (class I) soil quality, respectively. GWR model was robust in predicting soil quality (R2 = 0.21, CCC = 0.39, RMSE = 0.15), while RF was a model with inferior performance (R2 = 0.02, CCC = 0.32, RMSE = 0.15). Soil quality was high in the southern region and low in the northern region. High soil quality class (> 49%) and moderate soil quality class (> 14%) dominate the study area in all predicted models used.ConclusionsStructural stability index, sand content, soil oganic carbon content, and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices, while land surface water index, Sentinel-2 near-infrared band, plane curvature, and clay index were the most important variables affecting soil quality variability. The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality, which can provide guidance for site-specific management of soils developed on diverse parent materials.

The role of radiative cooling and leaf wetting in air–leaf water exchange during dew and radiation fog events in a temperate grassland

During prolonged dry periods, non-rainfall water (NRW) plays a vital role as water input into temperate grasslands, affecting the leaf surface water balance and plant water status. Previous chamber and laboratory experiments investigated air–leaf water exchange during dew deposition, but overlooked the importance of radiative cooling on air–leaf water exchange because the chamber is a heat trap, preventing radiative cooling. To complement these previous studies, we conducted a field study, in which we investigated the effect of radiatively-induced NRW inputs on leaf water isotope signals and air–leaf water exchange in a temperate grassland during the dry-hot summers of 2018 and 2019. We carried out field measurements of the isotope composition of atmospheric water vapor, NRW droplets on foliage, leaf water, xylem water of root crown, and soil water, combined with meteorological and plant physiological measurements. We combined radiation measurements with thermal imaging to estimate leaf temperatures using different methods, and computed the corresponding leaf conductance and air–leaf water exchange. Our results indicate that radiative cooling and leaf wetting induced a switch of direction in the net water vapor exchange from leaf-to-air to air-to-leaf. The leaf conductance and air–leaf water exchange varied by species due to the species-specific biophysical controls. Our results highlight the ecological relevance of radiative cooling and leaf wetting in natural temperate grasslands, a process which is expected to influence land surface water budgets and may impact plant survival in many regions in a drier climate.

Response of crop water indices to soil wetness and vegetation water content

Hyperspectral sensors offer a unique opportunity to monitor crop stress through the multiple narrow bands from visible to short wave infrared wavelengths. It provides prominent water absorption bands in near and shortwave regions centered at 970 nm, 1200 nm, 1440 nm, 1664 nm, 1950 nm and 2250 nm, which are unavailable with conventional multispectral sensors. It gives bands which otherwise are not available with traditional multispectral sensors. However, the spectral regions of 1350–1480, 1800–2000 and 2350–2500 nm, has strong absorption of light due to water vapour and hence not useful. This study investigates the indices, namely the Water Band Index (WBI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII) and Land Surface Water Index (LSWI), derived from the narrow bands of the AVIRIS-NG sensor for its response to varying degree of soil wetness and vegetation water content. The analyses showed that the WBI and NDWI could discern the saturated soil. At the same time, The NDII and LSWI displayed a better dynamic range because of the stronger absorption due to soil/vegetation liquid water content with reference to the base wavelength of 862 nm, which enables the depiction of the different degrees of soil wetness and vegetation water content. Since the water indices have varied responses to the same soil wetness and vegetation water content, a Water Indices vs Vegetation Index triangle space was used to derive the Water Condition Index (WCI). The analyses of the WCI’s indicate that the values of the water indices are normalized and have similar values under different soil wetness and vegetation water content. However, the WCIWBI had lower values among WCIs, and WCILSWI could demonstrate the variation in the soil wetness and vegetation water content owing to its better dynamic range.