A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

Synergizing remote sensing and ecological indicators (RSEIs) for evaluating ecological environmental quality (EEQ) in Asansol Municipal Corporation: an integrated approach

Human activities have dramatically affected global ecology over the past few decades. Geospatial technologies provide quick, efficient, and quantitative evaluation of spatiotemporal changes in eco-environmental quality (EEQ). This study focuses on a novel approach called remote sensing-based ecological indicators (RSEIs), which has used Landsat imagery data to assess environmental conditions and their changing trends. Four ecological indicators, mainly heatness, dryness, wetness, and greenness, have been used to assess the EEQ in Asansol Municipal Corporation Region (AMCR). Assembling all the indicators to generate RSEI, the principal component analysis (PCA) approach was applied. Our findings show that wetness and greenness favorably impact the province's EEQ, whereas dryness and heat create a negative impact. The RSEI assessment revealed that 24.53 to 28.83% of the area was poor and very poor, whereas the areas with very good decreased from 18.80 to 4.01% from 2001 to 2021 due to urban expansion and industrialization. The relative importance analysis indicates that greenness has a positive relation with RSEI, and dryness and heatness have a negative relation with RSEI. Finally, the receiving operating characteristic (ROC) was used for validation (AUC-0.885) of the RSEI. This study offers valuable insights for ecological management decision-making, guiding planners, and policymakers.

AHSWFM: Automated and Hierarchical Surface Water Fraction Mapping for Small Water Bodies Using Sentinel-2 Images

Accurately mapping surface water fractions is essential to understanding the distribution and area of small water bodies (SWBs), which are numerous and widespread. Traditional spectral unmixings based on the linear mixture model require high-quality prior endmember information, and are not appropriate in situations such as dealing with multiple scattering effects. To overcome difficulties with unknown mixing mechanisms and parameters, a novel automated and hierarchical surface water fraction mapping (AHSWFM) for mapping SWBs from Sentinel-2 images was proposed. AHSWFM is automated, requires no endmember prior knowledge and uses self-trained regression using scalable algorithms and random forest to construct relationships between the multispectral data and water fractions. AHSWFM uses a hierarchical structure that divides pixels into pure water, pure land and mixed water-land pixels, and predicts their water fractions separately to avoid overestimating water fractions for pure land pixels and underestimating water fractions for pure water pixels. Results show that using the hierarchical strategy can increase the accuracy in estimating SWB areas. AHSWFM predicted SWB areas with a root mean square error of approximately 0.045 ha in a region using more than 1200 SWB samples that were mostly smaller than 0.75 ha.

A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

Soil Moisture and Salinity Inversion Based on New Remote Sensing Index and Neural Network at a Salina-Alkaline Wetland

In arid and semi-arid regions, soil moisture and salinity are important elements to control regional ecology and climate, vegetation growth and land function. Soil moisture and salt content are more important in arid wetlands. The Ebinur Lake wetland is an important part of the ecological barrier of Junggar Basin in Xinjiang, China. The Ebinur Lake Basin is a representative area of the arid climate and ecological degradation in central Asia. It is of great significance to study the spatial distribution of soil moisture and salinity and its causes for land and wetland ecological restoration in the Ebinur Lake Basin. Based on the field measurement and Landsat 8 satellite data, a variety of remote sensing indexes related to soil moisture and salinity were tested and compared, and the prediction models of soil moisture and salinity were established, and the accuracy of the models was assessed. Among them, the salinity indexes D1 and D2 were the latest ones that we proposed according to the research area and data. The distribution maps of soil moisture and salinity in the Ebinur Lake Basin were retrieved from remote sensing data, and the correlation analysis between soil moisture and salinity was performed. Among several soil moisture and salinity prediction indexes, the normalized moisture index NDWI had the highest correlation with soil moisture, and the salinity index D2 had the highest correlation with soil salinity, reaching 0.600 and 0.637, respectively. The accuracy of the BP neural network model for estimating soil salinity was higher than the one of other models; R2 = 0.624, RMSE = 0.083 S/m. The effect of the cubic function prediction model for estimating soil moisture was also higher than that of the BP neural network, support vector machine and other models; R2 = 0.538, RMSE = 0.230. The regularity of soil moisture and salinity changes seemed to be consistent, the correlation degree was 0.817, and the synchronous change degree was higher. The soil salinity in the Ebinur Lake Basin was generally low in the surrounding area, high in the middle area, high in the lake area and low in the vegetation coverage area. The soil moisture in the Ebinur Lake Basin slightly decreased outward with the Ebinur Lake as the center and was higher in the west and lower in the east. However, the spatial distribution of soil moisture had a higher mutation rate and stronger heterogeneity than that of soil salinity.

Comparative assessment of drought monitoring index susceptibility using geospatial techniques

There are two main categories of dryness monitoring indices based on spectral feature space. One category uses the vertical distance from any point to a line passing through the coordinate origin, which is perpendicular to a soil line, to monitor the dryness conditions. The most popular indices are the Perpendicular Dryness Index (PDI) and the modified perpendicular dryness index (MPDI). The other category uses the distance from any point in feature space to the coordinate origin to represent the dryness status, for instance, the soil moisture (SM) monitoring index (SMMI) and the modified soil moisture monitoring index (MSMMI). In this study, the performances and differences of these four indicators were evaluated using field-measured SM (FSM) data based on Gaofen-1 (GF-1) wide field of view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 Multi-Spectral Instrument (MSI) sensors. Performance evaluations were conducted in two study areas, namely an arid and semi-arid region of northwest China and a humid agricultural region of southwest Canada. We employed gradient-based structural similarity (GSSIM) to quantitatively assess the similarity of the structural information and structural characteristics among these four indicators. Monitoring SM in bare soil or low vegetation-covered areas in the semi-arid region, the SMMI, PDI, MSMMI, and MPDI from Near-Infrared (NIR)-Red had significantly negative linear correlations with the FSM at 0-5-cm depth (P < 0.01). However, SMMI was better than PDI in estimating SM in bare soil, which was better than MSMMI and MPDI for GF-1. Moreover, the PDI and SMMI had similar SM evaluation abilities, which were better than those of MPDI and MSMMI for Landsat-8. The GSSIM map of the SMMI/PDI and the MSMMI/MPDI showed that the low change areas accounted for 99.89% and 98.89% for GF-1, respectively, and 95.78% and 94.45% for Landsat-8, respectively. This result indicated that the SMMI, PDI, MSMMI, and MPDI values from NIR-Red in low vegetation cover were similar. In monitoring SM in agricultural vegetation areas, the accuracy of the four indices from Short-Wave Infrared (SWIR) feature space was higher than that from NIR-Red feature space for Sentinel-2. The SM monitoring effect of MSMMI and MPDI was better than that of SMMI and PDI. Due to the lack of SWIR band, GF-1 was limited in monitoring SM in vegetation-covered areas. The SMMI and MSMMI, which do not rely on the soil line, were more suitable than PDI and MPDI for retrieving SM in the complex surface environment depending on the soil line and the number of parameters. GF-1 with 16-m resolution had higher accuracy in SM assessment than Landsat-8 with 30-m resolution and had almost the same accuracy as Sentinel-2 with 20 m.

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.

Advances in Remote Sensing-Based Disaster Monitoring and Assessment

Extreme weather/climate events have been increasing partly due to on-going climate change [...]

The NPP-Based Composite Indicator for Assessing the Variations of Water Provision Services at the National Scale

Water provision (WP) is an important service of the terrestrial ecosystem, which contributes to water availability for consumptive use and in situ water supply, sustains the production or flows of multiple ecosystem services (ES). Spatially explicit mapping of WP is critical for incorporating the ES concept into the decision-making processes of land-use and ecological conservation planning. Traditionally, regional complexes hydrological process models were simplified and used for mapping WP of the ecosystem at broad scales, but this approach is significantly limited by data accessibility and difficulty validating the results. To fill the gap, an NPP-based composite indicator model that simulates WP by multiplying NPP and its variations with the soil infiltration capacity factor, annual precipitation and the slope of the land surface is proposed in this paper. These parameters are chosen to map WP because they are closely related to hydrological processes. The model results were validated using observed runoff data of the eleven river basins in China. We then applied this approach to analyze the spatiotemporal changes of WP in China from 2000 to 2013. The results show that: (1) the average value of WP was lowest in the Northwest Arid Area ecoregions while the highest value of WP was in the South China ecoregion. (2) The linear trend of WP in the Loess Plateau and Hengduan Mountains ecoregions were increased while decreased in the other nine ecoregions. (3) The WP in the north of the Qinghai-Tibet Plateau presented a significant decrease trend mostly because the land cover change (e.g., grassland convert into dessert) and decreasing precipitation; the decreasing of the WP in Yunan-Guizhou Plateau are because the farmland convert into settlement land and the significant decrease of precipitation and significantly increase of temperature; the significant increase of the WP in Northeast China are because the increasing of forest and farmland, the grassland and wetland convert into farmland and forest, and the significant decrease of temperature and increase of precipitation; Although the increase of precipitation has played an important role in promoting WP, the significant increase of WP in the Loess Plateau was mainly due to the farmland convert into forest and grassland ecosystem types. The indicator explored by this research is benefiting for revealing the variations of WP under different land-use change and climate change, and informed the decision-making process of land-use policy or conservation planning at data-scarce regions or broaden spatial scales.