Land Surface Water Research Articles

Prediction of the Impact of Land Usage Changes on Water Pollution in Public Space Planning with Machine Learning

In urban public space planning, changes in land use, structure, and construction impact the urban environment to a certain degree. Land usage changes the urban surface water environment by impacting it through numerous ways. This paper studies about prediction of land use changes on surface water pollution in public space planning. This paper analyzes the characteristics of land use changes in public space planning from the quantitative characteristics of land use types, land use structure characteristics, and land usage degree in different years. The protection of natural resources is important, and water is one of the most important natural resources consumed by human beings. The environmental changes impacting these natural resources are to be studied to preserve the natural resources. The prediction of over-consumption of natural resources using soft computing techniques can certainly provide a solution for appropriate decision making. The prediction of relationship between land use changes and surface water pollution is required. In order to achieve this, the regression analysis on land use changes of different spatial scales with four surface water pollution indicators in the dry and wet seasons is performed to obtain the regression of each water pollution indicator. According to the determination coefficient, the determination coefficient of the model uses the comprehensive pollution index method to predict the impact of land use changes on surface water pollution. The experimental results show that the prediction accuracy of the proposed method is high and it is helpful in studying the impact of land use change on surface water pollution. It can help in decision making on consumption of natural resources to preserve the natural resources for next generations.

Bias correction of satellite soil moisture through data assimilation

Soil moisture exhibits great spatio-temporal heterogeneity and plays a critical part in land surface energy and water cycles, being identified as a terrestrial essential climate variable. Thus, it is urgently needed in a wide variety of environmental processes such as hydrology, meteorology, agriculture, and ecology. Microwave remote sensing has the potential to provide near real-time soil moisture estimates on large spatial scales according to the distinctive contrast between dielectric properties of water and dry soils. Thus, many space-borne microwave sensors have been launched for retrieving soil moisture. Especially, SMOS and SMAP at L-band frequency (1.4 GHz) supply an unprecedented opportunity for retrieving surface soil moisture due to their deeper penetration than instruments at other bands. However, these satellite soil moisture products need bias correction before application such as data assimilation. Two common correction methods require reliable land surface soil moisture simulations. However, the quality of these simulations relies heavily on model parameters, such as soil porosity and texture, which are almost unavailable in remote regions such as the Tibetan Plateau. In this study, a dual-cycle assimilation algorithm is taken to make on-line bias correction when assimilating SMAP soil moisture products. During the assimilation, a linear bias correction scheme is regarded as the observation operator to link the simulated soil moisture values and the satellite retrievals. In the inner cycle, a sequentially based assimilation algorithm is run with both model parameters and bias correction coefficients, which are provided by the outer cycle. At the same time, both the analyzed soil moisture and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of both model parameters and correction coefficients through an optimization algorithm. A series of numerical experiments are designed and conducted, indicating that the soil moisture estimates by the presented algorithm are superior to those with the existing bias correction schemes.

LONG-TERM LAND SURFACE WATER MONITORING IN THE YELLOW RIVER BASIN OF CHINA BASED ON LANDSAT IMAGERY ON THE GOOGLE EARTH ENGINE

Abstract. Land surface water is the most active part of the earth's surface layer and plays an important role in material balance and energy transformation. The monitoring of dynamic changes in surface open water helps to grasp the distribution and changes of water resources, which is important for understanding the impact of human activities on water resources and safeguarding the ecological security of the basin. In the study, 72,520 Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open surface water in the Yellow River Basin (YRB) from 2000 to 2020. The results show that (1) The multi-index water body extraction rules (MIWER) is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) From 2000 to 2020, both permanent and seasonal water in the whole YRB and sub-basins have increased. In general, the total open surface water in the YRB show an increasing trend with obvious spatial heterogeneity; (3) The changes of open surface water in the YRB are related to climate change and intensive human activities, but the influencing factors vary in different regions and need to be further studied in the future. The findings of this study can be used to help the policy-makers understand changing water resources and their driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

GIS-Based Modeling of Human Settlement Suitability for the Belt and Road Regions.

(1) Background: Human settlements are a basic requirement for human survival and development. The natural suitability of human settlements directly affects human health and their quality of life and, to some extent, also dictates the size of the regional population and economy, as well as the level of urbanization. (2) Methods: This study presents a kilometer grid-based comprehensive human settlement suitability (HSS) evaluation model (containing the relief degree of the land surface (RDLS), the temperature–humidity index (THI), the land surface water abundance index (LSWAI), and the land cover index (LCI)). (3) Results: Based on the correlations between the four factors (i.e., the normalized RDLS (NRDLS), normalized THI (NTHI), LSWAI, and LCI), the NRDLS and NTHI were determined to be the leading factors, and the LSWAI and LCI were considered to be the auxiliary factors. The auxiliary factors were used to enhance the comprehensive HSS model determined by the leading factors. Based on this logic, spatial and index models were established separately. The HSS index for the BRI regions ranged from 0.07 to 1.00. Six levels of HSS were determined—namely, unsuitable, critically unsuitable, critically suitable, generally suitable, moderately suitable, and highly suitable conditions. In particular, the human settlements suitability is dominated by critically suitable and suitable conditions that accounted for 63% of the BRI. (4) Conclusions: The evaluation results of human settlements showed that topographic and climatic conditions are important limiting factors for the suitability of human settlements. Due to the renewability and manmade adjustability of hydrological and land cover conditions, the comprehensive suitability of human settlements shows differences in different geographic spaces along the BRI. The results provide a decision-making basis for the research on the resource carrying capacity and reasonable distribution of populations in the BRI, so as to realize the sustainable development along the regions.

Irrigated cropland expansion exacerbates the urban moist heat stress in northern India

Agricultural irrigation has significantly reshaped the land surface energy and water balance. Previous studies have well investigated its cooling effect on air temperature (T air). However, its effect on increasing air humidity which can intensify the humid heat was often overlooked, particularly over urban areas with high population density, high T air, and consequently greater exposure to moist heat stress. In this study, using state-of-the-art reanalysis data at a high spatial resolution (∼9 km), we evaluated how changes in area equipped for irrigation (AEI) around a city affect urban moist heat stress (UMHS) in more than 1000 cities in China and India. In addition to T air, wet-bulb temperature (T WB) and wet-bulb globe temperature (T WBG), which consider humidity and are closer to the perceived temperature, were assessed. We found that although AEI expansion lowers urban T air, irrigation increases T WB and T WBG due to increased air humidity, thereby exacerbating the UMHS. This ‘warming’ effect of irrigation is more evident in northern India where AEI has expanded significantly in recent decades, and is prominent in the pre-monsoon and post-monsoon seasons, when precipitation and air humidity are low. However, this effect is not evident in China due to the lower intensity of AEI expansion and differing climatic conditions. Overall, this study highlights the side-effect of irrigation on regional climate, providing crucial information for better understanding urban heat stress and for future city planning.

Evolution of potential evapotranspiration and its sensitivity to climate change based on the Thornthwaite, Hargreaves, and Penman–Monteith equation in environmental sensitive areas of China

Global warming intensifies the water cycle and regional water condition differences by changing the surface environment. The ecological system in environmental sensitive areas of China (ESAC) is stressed by water resources, and the ecological effect of dry–wet evolution is even more profound. However, the applicability of different potential evapotranspiration (ET0) assessment methods and the response mechanism of ET0 to climate change remain unknown. In this study, we evaluated the reliability of ETTH, ETHarg, and ETPM derived from Thornthwaite (TH), Hargreaves (Harg), and Penman–Monteith (PM) formulas using the monthly average air temperature (Tavg), maximum air temperature (Tmax), minimum air temperature, sunshine duration (SSD), wind speed (WIN), and relative humidity (RHU) from 1961 to 2018. Further, non-parametric trend analysis, correlation analysis, multiple regression analysis, wavelet analysis, and seasonal and trend decomposition using Loess were used to analyze the spatial–temporal evolution of ET0 and the moisture index (MI) and its relationship with climate change in ESAC from 1961 to 2018. The results are as follows: (1) The P-M formula has excellent applicability in the research area. Under the background of average temperature rising at 0.035 °C yr−1, ETPM decreases with a rate of −0.59 mm yr−1, which indicates the “pan evaporation paradox” phenomenon. Using 1993 as the node, ETPM changed from a significant decrease to an increase. (2) WIN, SSD, Tavg, and Tmax significantly promoted changes in ETPM, and RHU and precipitation (Pr) were mainly reduced. The determinant factor of ETPM change was Tavg from 1961 to 2018. (3) MI change was mainly influenced by Pr, SSD, and ETPM. Pr promoted the change, whereas SSD and ETPM reduced it. The linear combination of ETPM and SSD could explain the change in MI. Our findings verified the applicability of PM in areas with severe interannual precipitation fluctuations, revealing the evolution of dry–wet pattern changes and their driving mechanisms in ESAC, which can deepen the cognition of the law of long-term land surface water cycle and effectively guide the formulation and implementation of regional drought and flood disaster response measures.

Sensitivity of the land surface hydrological cycle to human activities in China

While many studies have focused on the sensitivity of the water cycle to climate change, few have investigated this sensitivity to human activities. We don't know how sensitive the surface hydrological system is to human activity and which component of the water balance is most sensitive. This study performed model simulations to quantify the sensitivity of China’s land surface water cycle to the human activity intensity (HAI). The results indicate that lateral flow to streamflow (LATQ) was the most sensitive to HAI (0.679), followed by base flow (GW, 0.291), actual evapotranspiration (ET, 0.203), and surface runoff (SURQ, 0.174), while the percolation amount (PERC) and water yield (WYLD) sensitivities were 0.172 and 0.081, respectively. Hotspots of the ET sensitivity to HAI occurred mainly in Western and Northeast China. The spatial sensitivity patterns of GW and PERC were similar, i.e., lower in the south than elsewhere. Notably, SURQ was insensitive to HAI changes in the urban areas of South China (sensitivity: 0–0.06). Although the HAI variation was the lowest in semiarid regions during 1980–2018 (0.07–0.82%), their water cycle sensitivity to HAI was the highest among all four climatic zones. The ET, SURQ, LATQ, PERC, GW and WYLD sensitivity coefficients to the HAI in the semiarid zone were 20.441, 41.292, 5.276, 24.431, 52.06 and 1.032, respectively. Hydrologically, semiarid areas, the Qinghai-Tibet Plateau and the region near the Karamay oilfield should be prioritized. Specifically, LATQ should receive the most attention.

Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery

Monitoring changes within the land surface and open water bodies is critical for natural resource management, conservation, and environmental policy. While the use of satellite imagery for these purposes is common, fine-scale change detection can be a technical challenge. Difficulties arise from variable atmospheric conditions and the problem of assigning pixels to individual objects. We examined the degree to which two machine learning approaches can better characterize change detection in the context of a current conservation challenge, artisanal small-scale gold mining (ASGM). We obtained Sentinel-2 imagery and consulted with domain experts to construct an open-source labeled land-cover change dataset. The focus of this dataset is the Madre de Dios (MDD) region in Peru, a hotspot of ASGM activity. We also generated datasets of active ASGM areas in other countries (Venezuela, Indonesia, and Myanmar) for out-of-sample testing. With these labeled data, we utilized a supervised (E-ReCNN) and semi-supervised (SVM-STV) approach to study binary and multi-class change within mining ponds in the MDD region. Additionally, we tested how the inclusion of multiple channels, histogram matching, and La*b* color metrics improved the performance of the models and reduced the influence of atmospheric effects. Empirical results show that the supervised E-ReCNN method on 6-Channel histogram-matched images generated the most accurate detection of change not only in the focal region (Kappa: 0.92 (± 0.04), Jaccard: 0.88 (± 0.07), F1: 0.88 (± 0.05)) but also in the out-of-sample prediction regions (Kappa: 0.90 (± 0.03), Jaccard: 0.84 (± 0.04), and F1: 0.77 (± 0.04)). While semi-supervised methods did not perform as accurately on 6- or 10-channel imagery, histogram matching and the inclusion of La*b* metrics generated accurate results with low memory and resource costs. These results show that E-ReCNN is capable of accurately detecting specific and object-oriented environmental changes related to ASGM. E-ReCNN is scalable to areas outside the focal area and is a method of change detection that can be extended to other forms of land-use modification.

Flash drought identification from satellite-based land surface water index

Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the presented methodology improves our ability to identify flash drought at high spatial resolution using satellite remote sensing and detect rapid changes in surface conditions. In light of these results, the novel LSWI approach demonstrates that satellite remote sensing applications using an objective technique are advantageous for flash drought detection in near real-time and at fine spatial scales.

Annual characteristics of gross primary productivity (GPP) in mangrove forest during 2016-2020 as revealed by Sentinel-2 remote sensing imagery

Carbon dioxide (CO2) is the most responsible gas in the phenomenon of global warming on earth because of its greatest concentration and longevity in the long atmosphere. Meanwhile mangrove is one of the blue carbon parameters that can take CO2 for photosynthesis and store it into biomass and sediment, so the existence of Mangrove plays a key role in the balance of the global carboncycle. Gross Primary Productivity (GPP) is one of the key variables in conducting a carboncycle study because the GPP values constitute the total value of carbon fixation by terrestrial ecosystem through vegetation photosynthesis. The aim of this study is to recognize the annual characteristics of GPP values in the mangrove ecosystem using remote sensing satellite Sentinel-2 during the period of 2016 to 2020 in TAHURA Ngurah Rai, Bali, Indonesia. The vegetation photosynthesis model (VPM) model is used to calculate annual GPP by using remote sensing indices of Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). Both remote sensing indices are supported by temperature and solar radiation data to determine photosynthesis fraction. The results of the current study indicated that the annual GPP values in the mangrove forest of TAHURA Ngurah Rai have decreased during the observation year. The total value of GPP in 2016 is reached to 28790 tC m−2 year−1, while the total amount of GPP value in 2020 decrease to 26223 tC m−2 year−1. The decline in GPP values may be due to changes of land cover and mangrove mortality that occurred around Benoa port in 2018.

Using Artificial Neural Network (ANN) for Short-Range Prediction of Cotton Yield in Data-Scarce Regions

Short-range predictions of crop yield provide valuable insights for agricultural resource management and likely economic impacts associated with low yield. Such predictions are difficult to achieve in regions that lack extensive observational records. Herein, we demonstrate how a number of basic or readily available input data can be used to train an Artificial Neural Network (ANN) model to provide months-ahead predictions of cotton yield for a case study in Menemen Plain, Turkey. We use limited reported yield (13 years) along cumulative precipitation, cumulative heat units, two meteorologically-based drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)), and three remotely-sensed vegetation indices (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI)) as ANN inputs. Results indicate that, when EVI is combined with the preceding 12-month SPEI, it has better sensitivity to cotton yield than other indicators. The ANN model predicted cotton yield four months before harvest with R2 > 0.80, showing potential as a yield prediction tool. We discuss the effects of different combinations of input data (explanatory variables), dataset size, and selection of training data to inform future applications of ANN for early prediction of cotton yield in data-scarce regions.

Multi–Proxy Reconstruction of Drought Variability in China during the Past Two Millennia

Drought imposes serious challenges to ecosystems and societies and has plagued mankind throughout the ages. To understand the long-term trend of drought in China, a series of annual self-calibrating Palmer drought severity indexes (scPDSI), which is a semi-physical drought index based on the land surface water balance, were reconstructed during AD 56~2000. Multi-proxy records of tree-ring width and stalagmite oxygen isotope δ18O were used for this reconstruction, along with random forest regression. The spatiotemporal characteristics of the reconstruction results were analyzed, and comparisons were made with previous studies. Results showed that (1) China witnessed a drought-based state during the past 2000 years (mean value of scPDSI was −0.3151), with an average annual drought area of 85,000 km2; 4 wetting periods, i.e., the Han Dynasty (AD 56~220), the Tang Dynasty (AD 618~907), the Ming Dynasty (AD 1368~1644), and the Qing Dynasty (AD 1644~1912); and 2 drying periods, i.e., the Era of Disunity (AD 221~580) and the Song Dynasty (AD 960~1279). (2) Three different alternating fluctuation dry-wet modes (i.e., interannual, multidecadal, and centennial scales) in China were all significantly (p-value < 0.001) correlated with the amplitude and frequency of temperature in the Northern Hemisphere. (3) According to the spatial models disassembled from the rotated empirical orthogonal function, China was divided into nine dry-wet regions: northwestern China, Xinjiang, southwestern China, southeastern China, the Loess plateau, central China, southwestern Tibet, eastern China, and northeastern China. (4) The random forest (RF) was found to be accurate and stable for the reconstruction of drought variability in China compared with linear regression.

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.

Attribution of Dry and Wet Climatic Changes over Central Asia

Abstract Central Asia (CA; 35°–55°N, 55°–90°E) has been experiencing a significant warming trend during the past five decades, which has been accompanied by intensified local hydrological changes. Accurate identification of variations in hydroclimatic conditions and understanding the driving mechanisms are of great importance for water resource management. Here, we attempted to quantify dry/wet variations by using precipitation minus evapotranspiration (P − E) and attributed the variations based on the atmosphere and surface water balances. Our results indicated that the dry season became drier while the wet season became wetter in CA for 1982–2019. The land surface water budget revealed precipitation (96.84%) and vapor pressure deficit (2.26%) as the primary contributing factors for the wet season. For the dry season, precipitation (95.43%), net radiation (3.51%), and vapor pressure deficit (−2.64%) were dominant factors. From the perspective of the atmospheric water budget, net inflow moisture flux was enhanced by a rate of 72.85 kg m−1 s−1 in the wet season, which was mainly transported from midwestern Eurasia. The increase in precipitation induced by the external cycle was 11.93 mm (6 months)−1. In contrast, the drying trend during the dry season was measured by a decrease in the net inflow moisture flux (74.41 kg m−1 s−1) and reduced external moisture from midwestern Eurasia. An increase in precipitation during the dry season can be attributed to an enhancement in local evapotranspiration, accompanied by a 4.69% increase in the recycling ratio. The compounding enhancements between wet and dry seasons ultimately contribute to an increasing frequency of both droughts and floods.

HydroPy (v1.0): a new global hydrology model written in Python

Abstract. Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy.

On the emerging global relevance of atmospheric rivers and impacts on landscapes and water resources

This progress report discusses the lineage of atmospheric rivers (ARs) research, focusing on the transformation of the topic from an important regional atmospheric feature along the U.S. West Coast to a globally relevant driver of extreme hydrometeorological events. As the AR literature has advanced, so has the regional expanse covered, initially expanding into the Central U.S. and Europe. Recently, new, emerging regions are being explored in the AR literature such as the high latitudes, New Zealand, China, North Africa, and the Middle East. The literature on the impact of AR-driven hydrometeorological events on land surface processes (e.g., landslides and avalanches) and water resources is also rapidly developing. This progress report seeks to expose the broader physical geography discipline to the global relevance of ARs and promote new applied research frontiers at the intersection of ARs and those processes studied by physical geographers.

Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors

Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dual-cycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.

A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surface water and gives reliable data support for the accurate extraction of land surface water information. The convolutional neural network (CNN), widely applied in semantic segmentation, provides an automatic extraction method in land surface water information. This paper proposes a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information based on GaoFen-1D satellite data of Wuhan, Hubei Province, China. To verify the superiority of LMSWENet, we compared the efficiency and water extraction accuracy with four mainstream CNNs (DeeplabV3+, FCN, PSPNet, and UNet) using quantitative comparison and visual comparison. Furthermore, we used LMSWENet to extract land surface water information of Wuhan on a large scale and produced the land surface water map of Wuhan for 2020 (LSWMWH-2020) with 2m spatial resolution. Random and equidistant validation points verified the mapping accuracy of LSWMWH-2020. The results are summarized as follows: (1) Compared with the other four CNNs, LMSWENet has a lightweight structure, significantly reducing the algorithm complexity and training time. (2) LMSWENet has a good performance in extracting various types of water bodies and suppressing noises because it introduces channel and spatial attention mechanisms and combines features from multiple scales. The result of land surface water extraction demonstrates that the performance of LMSWENet exceeds that of the other four CNNs. (3) LMSWENet can meet the requirement of high-precision mapping on a large scale. LSWMWH-2020 can clearly show the significant lakes, river networks, and small ponds in Wuhan with high mapping accuracy.

Damage assessment of chilli thrips using high resolution multispectral satellite data


 Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

Problems of surface water protection in the context of land relations in the South of Russia

Aim . The aim was to analyse the use and protection of land surface water bodies in the context of land relations based on theoretical concepts of the inseparability of water bodies and the land occupied by them. Material and Methods . An integrated approach based on a combination of statistical methods and a comparative legal method were used to analyse and compare dynamic and territorial series of cadastral accounting data and cadastral maps. Results . The existing problems in the formation of lands of the Water Fund are caused primarily by inconsistency in maintaining the water register. Therefore, large areas under water bodies are not included in the land of the water Fund and are not protected from arbitrary use. This is fraught with potential harm to water bodies including their complete destruction. Such discrepancies are most typical for most regions within the North Caucasus Federal District. Conclusion . Analysis showed that the actual status of the majority of water bodies in the territory is quite irregular and that there are widespread inconsistencies with the norms stipulated by the legislation in relation to land under water in the independent category of "land of the Water Fund". In practice, the composition of this category is formed independently of the established criteria for such selection. These inconsistencies are due to the inconsistency of the norms themselves regarding the formation of water Fund lands and the disinterest of land users and local authorities in transferring land under surface water bodies to the category of water Fund lands due to restrictions imposed by legislation on their use.