Visual Analysis of Remote Sensing Monitoring of Soil Salinization

Soil salinization seriously restricts the development of agricultural production,the sustainable use of land resources,and the stability of the ecological environment.In order to objectively reveal the research status of soil salinization,CiteSpace software was used to conduct data mining and quantitative analysis on research papers on soil salinization from 2008 to 2023 in China National Knowledge Infrastructure(CNKI)and Web of science databases.The data sources were transformed into visual graphs by reproducing clustering statistics from aspects such as publication volume,authors,keywords,and publishing institutions.In addition,this paper also combined the actual needs and cutting-edge hotspots in relevant research in China,and proposed and analyzed the limitations and future development trends of soil salinity monitoring research in China.This has important practical significance for comprehensively grasping the current research status of salinization,further clarifying and sorting out the research ideas of salinization monitoring,enriching the remote sensing monitoring methods of saline soil,and solving the actual problems of soil salinization in China.

Detection of a Real Time Remote Sensing Indices and Soil Moisture for Drought Monitoring and Assessment in Jordan

Drought monitoring represents a challenge for water and agricultural sector as this natural hazard accelerates water deficiency and leads to adverse environmental and socioeconomic impacts. The use of remote sensing data and geospatial techniques to monitor and map drought severity expanded in the last decades with progressive developments in data sources and processing. This study investigates the correlations among drought indices derived with soil moisture stress (K) obtained from ground data collected from fields cultivated with barley. The study, carried out in Yarmouk basin in the north of Jordan, includes NDVI, PDI, MPDI and PVI derived from Landsat 8-OLI and Sentinel 2-MSI. Results showed different behavior among the indices and throughout the 2016/2017 growing season, with maximum correlation between PDI and MPDI followed by NDVI with PVI. Correlations among the remote sensing indices and K for different soil depths during March-April were significant for most indices with a maximum (R2) of 0.82 for K30-50 and MPDI, followed by K30-50 with NDVI. Drought severity maps for the month of March showed different trends for the different indices, with similarities between MPDI and PDI. The map of drought severity combined from the remote sensing indices and K showed that PDI and soil moisture could significantly explain 56% of variations in spatial patterns of drought, while the combination of MPDI, PDI and NDVI could significantly explain up to 59% of variations in drought severity map. Therefore, the study recommends the adoption of these remotely sensed indices for monitoring and mapping of agricultural droughts.

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

A New Software for GIS Image Pixel Topographic Fac－tors in Remote Sensing Monitoring of Soil Losses

Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This paper lays its emphasis on algorithmic skills and programming techniques as well as applicationof the software.

Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas

Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity （ECa）. Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.

Advances in Research on Soil Moisture by Microwave Remote Sensing in China

Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active re- mote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.

An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products

For the application of soil moisture and ocean salinity（SMOS） remotely sensed sea surface salinity（SSS） products,SMOS SSS global maps and error characteristics have been investigated based on quality control information.The results show that the errors of SMOS SSS products are distributed zonally,i.e.,relatively small in the tropical oceans,but much greater in the southern oceans in the Southern Hemisphere（negative bias） and along the southern,northern and some other oceanic margins（positive or negative bias）.The physical elements responsible for these errors include wind,temperature,and coastal terrain and so on.Errors in the southern oceans are due to the bias in an SSS retrieval algorithm caused by the coexisting high wind speed and low temperature; errors along the oceanic margins are due to the bias in a brightness temperature（TB） reconstruction caused by the high contrast between L-band emissivities from ice or land and from ocean; in addition,some other systematic errors are due to the bias in TB observation caused by a radio frequency interference and a radiometer receivers drift,etc.The findings will contribute to the scientific correction and appropriate application of the SMOS SSS products.

Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover

Soil moisture(SM), which plays a crucial role in studies of the climate, ecology, agriculture and the environment, can be estimated and mapped by remote sensing technology over a wide region. However, remotely sensed SM is constrained by its estimation accuracy, which mainly stems from the influence of vegetation cover on soil spectra information in mixed pixels. To overcome the low-accuracy defects of existing surface albedo method for estimating SM, in this paper, Qinghai Lake Basin, an important animal husbandry production area in Qinghai Province, China, was chosen as an empirical research area. Using the surface albedo computed from moderate resolution imaging spectroradiometer(MODIS) reflectance products and the actual measured SM data, an albedo/vegetation coverage trapezoid feature space was constructed. Bare soil albedo was extracted from the surface albedo mainly containing information of soil, vegetation, and both albedo models for estimating SM were constructed separately. The accuracy of the bare soil albedo model(root mean square error=4.20, mean absolute percent error=22.75%, and theil inequality coefficient=0.67) was higher than that of the existing surface albedo model(root mean square error=4.66, mean absolute percent error=25.46% and theil inequality coefficient=0.74). This result indicated that the bare soil albedo greatly improved the accuracy of SM estimation and mapping. As this method eliminated the effect of vegetation cover and restored the inherent soil spectra, it not only quantitatively estimates and maps SM at regional scales with high accuracy, but also provides a new way of improving the accuracy of soil organic matter estimation and mapping.

Studying the Condition of Soil Protection Agrolandscape in Ukraine Using Remote Sensing Methods

The article reviews the scientific approaches to monitoring of soil condition on the soil protection agrolandscape. In 1980s, the contour-meliorative soil protection system was established on the selected fields in Ukraine. The objective of the current research was to determine the capabilities of satellite survey to identify the changes of soil cover that had occurred on these fields during the past 25 years. Soil erosion processes are very dynamic, therefore it is essential to use time-series of operative satellite images to track those changes. Rills on the fields, caused by water erosion, are clearly identified on high-resolution satellite data. Erosion causes the decrease of humus content, which affects soil reflection values. This in turn leads to a corresponding change of color shade on satellite images. The research allowed to determine correlation between remote sensing data and soil organic carbon content and to acquire a mathematical model which describes this correlation. The condition of the agrolandscape soils was assessed using the regression model, which helped to evaluate erosion risk for different areas of the test polygon. The visual interpretation of satellite imagery led to a conclusion about a damaging effect of erosion on protective forest belts and accordingly on fields＇ soil cover and crops. Visual analysis results were approved by field research. Photos taken during the field research indicate an unsatisfactory status of forest belts and a devastating effect of eroding water flows. These are the results of irresponsible land use and constant violation of methodical principles of the contour-meliorative system organization. The article concludes that the use of time-series of high-resolution satellite imagery allows monitoring the condition of soil protection agrolandscape, in particular the forest belts＇ status soil cover conditions and their change over time. The research results can be used as an informational basis for the soil protection agrolandscape monitoring system.

A STUDY OF SOIL CONSERVATION MONITORING INFORMATION SYSTEM BASED ON REMOTELY SENSED DATA FOR A CATCHMENT ON THE LOESS PLATEAU

The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

Comparisons of Soil Moisture Datasets over the Tibetan Plateau and Application to the Simulation of Asia Summer Monsoon Onset

The influence of soil moisture on Asian monsoon simulation/prediction was less studied, partly due to a lack of available and reliable soil moisture datasets. In this study, we firstly compare several soil moisture datasets over the Tibetan Plateau, and find that the remote sensing products from the Advanced Microwave Scanning Radiometer for Earth Observing System （AMSR-E） can capture realistic temporal variations of soil moisture better than the two reanalyses （NCEP and ECMWF） during the pre-monsoon seasons. Using the AMSR-E soil moisture product, we investigate the impacts of soil moisture over the Tibetan Plateau on Asian summer monsoon onset based on a Spectral Atmospheric Model developed at IAP/LASG （SAMIL）. Comparison between results with and without the assimilation of remotely sensed soil moisture data demonstrates that with soil moisture assimilated into SAMIL, the land-sea thermal contrast during pre-monsoon seasons is more realistic. Accordingly, the simulation of summer monsoon onset dates over both the Bay of Bengal and South China Sea regions are more accurate with AMSR-E soil moisture assimilated. This study reveals that the application of the soil moisture remote sensing products in a numerical model could potentially improve prediction of the Asian summer monsoon onset.

Quantitative Analysis of Moisture Effect on Black Soil Reflectance

Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content, or increases when the soil moisture reaches a certain content; however, there are few analyses on the quantitative relationship between soil reflectance and its moisture, especially in the case of black soils in northeast China. A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture. For the soil samples with moisture contents ranging from air-dry to saturated, the changes in soil reflectance with soil moisture can be depicted using a cubic equation. Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation. When the moisture range was smaller than MT, soil reflectance can be simulated with a linear model. However, for samples with different soil organic matter (OM), the parameters of the linear model varied regularly with the OM content. Based on their relationship, the soil moisture can be estimated from soil reflectance in the black soil region.

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

Relationship of Soil Moisture and Reflected GPS Signal Strength

Many agricultural fields across the country have a high degree of variability in soil type and water holding capacity that affects irrigation management. One way to overcome problems associated with the field variability for improving irrigation management is to utilize a site-specific irrigation system. This system applies water to match the needs of individual management zones within a field. A real-time continuous soil moisture measurement is essential for the success of site-specific irrigation systems. Recently the National Aeronautics and Space Administration (NASA) developed sensor technology that records the global positioning system (GPS) signal reflected from the surface of Earth, which estimates the dielectric properties of soil and can be used to estimate soil moisture contents. The overall objective of this study was to determine the feasibility of utilizing GPS-based technology developed by NASA for soil moisture measurements and to determine the influence of soil type, soil compaction, and ground cover on the measurements. The results showed strong positive correlations between soil moisture and reflected signals. Other factors (soil compaction and soil type), were not significantly related to reflectivity and did not significantly change the relationship between reflectivity and soil moisture contents. In addition, ground cover (rye crop) did not significantly reduce reflectivity. Therefore, this system could be used as a real-time and continuous nonintrusive soil moisture sensor for site-specific irrigation scheduling and watershed management.

Research on the Estimation Model of Soil Moisture Content Based on the Characteristics of Thermal Infrared Data

With the portable Fourier Transform Infrared Spectroscopy (FTIR), the reflectance spectra of soil samples with different moisture content are measured in laboratory for expounding the characteristic of radiation in the thermal infrared part of the spectrum with different soil moisture content. A model of estimating the moisture content in soil is attempted to make based on Moisture Diagnostic Index (MDI). In general,the spectral characteristic of soil emissivity in laboratory includes the following aspects.Firstly,in the region of 8.0-9.5 μm,along with the increase of soil moisture content,the emissivity of soil increases to varying degrees. The spectral curves are parallel relatively and have a tendency to become horizontal and the absorbed characteristic of reststrahlen is also weakened relatively with the increase of soil moisture in this region.Secondly,in the region of 11.0-14.0 μm,the emissivity of soil has a tendency of increasing.There is an absorption value near about 12.7 μm. As the soil moisture content increases,the depth of absorption also increases. This phenomenon may be caused by soil moisture absorption. Methods as derivative, difference and standardized ratio transformation may weaken the background noise effectively to the spectrum data. Especially using the ratio of the emissivity to the average of 8-14 μm may obviously enhance the correlation between soil moisture and soil emissivity. According to the result of correlation analysis, the 8.237 μm is regarded as the best detecting band for soil moisture content. Moreover,based on the Moisture Diagnostic Index ( MDI) in the 8.194-8.279 μm, the logarithmic model of estimating soil moisture is made.

Soil Moisture Retrieval Quantitatively with Remotely Sensed Data and Its Crucial Factors Analysis

The Ts/NDVI method was adopted to retrieve soil moisture with multi-temporal and multi-sensor remotely sensed data f ETM+ and ASTER in study area. The retrieved soil moisture maps were consistent with the soil type and vegetation, which were also the two main factors determining the distribution of soil moisture.

A Method for Surface Roughness Parameter Estimation in Passive Microwave Remote Sensing

Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.

Active-Layer Soil Moisture Content Regional Variations in Alaska and Russia by Ground-Based and Satellite-Based Methods, 2002 through 2014

Soil moisture is a vital physical parameter of the active-layer in permafrost environments, and associated biological and geophysical processes operative at the microscopic to hemispheric spatial scales and at hourly to multi-decadal time scales. While?in-situ?measurements can give the highest quality of information on a site-specific basis, the vast permafrost terrains of North America and Eurasia require space-based techniques for assessments of cause and effect and long-term changes and impacts from the changes of permafrost and the active-layer. Satellite-based 6.925 and 10.65 GHz sensor algorithmic retrievals of soil moisture by Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard NASA-Aqua and follow-on AMSR2 onboard JAXA-Global Change Observation Mission—Water-1 are ongoing since July 2002. Accurate land-surface temperature and vegetation parameters are critical to the success of passive microwave algorithmic retrieval schemes. Strategically located soil moisture measurements are needed for spatial and temporal co-location evaluation and validation of the space-based algorithmic estimates. We compare on a daily basis ground-based (subsurface-probe) 50- and 70-MHz radio-frequency soil moisture measurements with NASA- and JAXA-algorithmic retrieval passive microwave retrievals. We find improvements in performance of the JAXA-algorithm (AMSR-E reprocessed and AMSR2 ongoing) relative to the earlier NASA-algorithm version. In the boreal forest regions, accurate land-surface temperatures and vegetation parameters are still needed for algorithmic retrieval success. Over the period of AMSR-E retrievals, we find evidence of at the high northern latitudes of growing terrestrial radio-frequency interference in the 10.65 GHz channel soil moisture content. This is an important error source for satellite-based active and passive microwave remote sensing soil moisture retrievals in Arctic regions that must be addressed.

兰州市南北两山土壤水分遥感反演及植被需水量估算

探究西北干旱区土壤水分和植被需水量动态变化特征,可为生态恢复不同阶段所需水资源量及水资源优化配置提供科学依据。以兰州市南北两山为研究区,基于Sentinel-2 L2A和Landsat 8 OLI遥感影像,结合实测土壤0~10 cm的111个数据,分别构建垂直干旱指数(Perpendicular Drought Index,PDI)、改进型垂直干旱指数(Modified Perpendicular Drought Index,MPDI)和植被调整垂直干旱指数(Vegetation-adjusted Perpendicular Drought Index,VAPDI)土壤水分反演模型,并采用4种模型指标定量决定系数(R^(2))、平均绝对误差(MAE)、平均相对误差(MRE)、均方根误差(RMSE)对模型反演的效果进行精度评价,选出最优的土壤水分反演模型并结合土壤水分限制系数,与研究区2019年林地、草地和耕地植被面积的空间数据、各站点生长季内的参考作物蒸散量,构建植被生态需水量模型,厘清研究区内土壤水分、植被需水量时空变化特征。结果表明:(1)2种数据源下的PDI、MPDI、VAPDI和实测数据之间均有着不同程度的线性负相关性,其中R^(2)分别为0.37、0.64和0.59,从评价指标的结果来看,MPDI的土壤水分回归模型的拟合决定系数最高,2种遥感数据反演的土壤水分空间分布格局具有一致性。(2)分辨率高的Sentinel-2L2A土壤水分反演更加精细,土壤水分整体呈波动增长趋势,多时段土壤水分的平均值为23.27%,呈现出降低再增加然后下降,总体增幅为74.07%。(3)兰州市南北两山4—10月植被需水量月均值也呈现先增加后下降的趋势,与土壤水分含量变化具有一致性,4—10月中7月植被需水量最大,为3.98×10^(7)m^(3),10月植被生态需水量最小,为0.97×10^(7)m^(3)。随着环境绿化工程的实施,兰州市南北两山从只能生长耐旱草本和低矮灌木的植物,逐步形成以多种类结合的群落结构。本研究可为兰州市南北两山土壤水资源合理利用及植被恢复提供参考。