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Abstract This paper investigates estimation of root zone soil moisture using two passive microwave remote sensing datasets, Advanced Microwave Scattering Radiometer – 2 and Soil Moisture Active Passive satellites sensors. The study is focused on two crops, namely rice and wheat for the Indo-Gangetic basin, India, having a dynamic crop and soil type and land use land cover. A total of 21 rice crop and 23 wheat crop locations are chosen from the states of Uttar Pradesh, Madhya Pradesh and Bihar falling in the basin. The root zone soil moisture information is derived by estimating soil wetness index from surface soil moisture at 10 and 40 cm depths using a recursive exponential filter. The soil wetness index based algorithm is implementable even in the absence of ground information for a basin level study. The reference soil moisture dataset is obtained from the Global Land Database Assimilation System – NOAH at 10 and 40 cm depth. The research has also demonstrated significant potential of GLDAS-NOAH soil moisture data in the absence of ground (in-situ) soil moisture data. Of the various factors affecting surface and root zone soil moisture, this work evaluates the control of soil constituents on root zone soil moisture. The Spearman rank correlation coefficient is estimated for characteristic time delay with sand, silt and clay percentage at different locations. Coupling between and trends of surface and root zone soil moisture for rice and wheat crop locations are studied. The accuracy of estimated soil wetness index at 10 and 40 cm from two different satellite sensors at two different acquisition times (ascending and descending passes) is investigated by calculating the coefficient of determination, mean absolute error and mean biased error. This work highlights the significant difference in surface soil moisture estimation by two satellite sensors to derive root zone soil moisture for rice and wheat crops. Coefficient of determination is more (∼0.9) for SMAP derived soil wetness index whereas it is lower (∼0.65) for AMSR-2 derived soil wetness index for both crops. Characteristic time delay variation is observed at two different times and at both the depths, with characteristic time delay increasing with depth. Also, at the descending pass characteristic time delay is lower as compared to the ascending pass. A strong relationship between root zone soil moisture and soil texture is observed. For rice crop, a positive correlation with sand and clay is observed for Uttar Pradesh, Madhya Pradesh and Bihar locations having loam and sandy loam as the major soil class. And, for wheat locations, a positive correlation is observed for silt and clay for Uttar Pradesh locations and sand for Madhya Pradesh locations having loam and clay (light) soil texture. This work delivers essential information in understanding sustainable irrigation scheduling and increasing irrigation potential for rice and wheat crop locations. Having the knowledge of all the factors influencing crop cultivation and the derived root zone soil moisture, crop production can be optimized. Graphical abstract Image 10347690 Highlights • Estimating root zone soil moisture from AMSR-2 and SMAP surface soil moisture data. • Correlation between derived root zone soil moisture and soil texture for rice and wheat crops. • Error of estimated soil moisture is higher at morning times than afternoon. • Precise root zone soil moisture estimation (higher R2) from SMAP than AMSR-2 sensor. • Understanding the potential of GLDAS-NOAH as the reference data in absence of in-situ data. [ABSTRACT FROM AUTHOR]

Problems with lowering of water table are common all over the world. Intensive pumping of water from aquifers for consumption, irrigation, industrial or mining purposes often causes groundwater depletion and results in the formation of cone of depression. This can severely decrease water pressure, even over vast areas, and can create severe problems such as degradation of agriculture or natural environment sometimes depriving people and animals of water supply. In this paper, the authors present a method for determining the area of influence of a groundwater depression cone resulting from prolonged drainage, by means of satellite images in optical, near infrared and thermal infrared bands from TM sensor (Thematic Mapper) and ETM+ sensor (Enhanced Thematic Mapper +) placed on Landsat 5 and Landsat 7 satellites. The research area was Szczercowska Valley (Pol. Kotlina Szczercowska), Central Poland, located within a range of influence of a groundwater drainage system of the lignite coal mine in Belchatow. It is the biggest lignite coal mine in Poland and one of the largest in Europe exerting an enormous impact on the environment. The main method of satellite data analysis for determining soil moisture, was the so-called triangle method. This method, based on TVDI (Temperature Vegetation Dryness Index) was supported by additional spatial analysis including ordinary kriging used in order to combine fragmentary information obtained from areas covered by meadows. The results obtained are encouraging and confirm the usefulness of the triangle method not only for soil moisture determination but also for assessment of the temporal and spatial changes in the area influenced by the groundwater depression cone. The range of impact of the groundwater depression cone determined by means of above-described remote sensing analysis shows good agreement with that determined by ground measurements. The developed satellite method is much faster and cheaper than in-situ measurements, and allows for systematic monitoring of the vast area in the vicinity of Belchatow lignite mine. Besides, this method could be useful as a helper in in-situ measurement allowing a significant reduction of the number of in-situ measurements by performing them only within problematic areas. Hence, the triangle method can be used as an effective supplement to field measurements. Although the research area is located in Poland, in the vicinity of lignite mine, the method of observation of depression cones provided in this study is universal and effective, and therefore could also be useful to an international audience. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on terrestrial wet and dry changes and the relationship between the two is of great significance to the sustainable development of terrestrial ecosystems. The farming-pastoral ecotone of northern China (FPENC) is an area that is sensitive to climate change, suffering from perennial drought and a clear aridification trend. Unlike previous single-factor, single-timescale studies, we identified aridification in the region based on a dataset established by remote sensing and ground-based monitoring stations from a combination of two perspectives: climate and soil. The results show that, in terms of climate, the period from 2000 to 2019 was the driest in the region during the last 120 years , and the summer drought was the most severe and shifted from a summer to spring drought; in terms of soil, the soil aridification trend in the region was severe, with 16.1% of the areas becoming significantly drier (P < 0.1) among the years and 41.6% in spring, respectively. Similar to climate change, soils exhibited recessive aridification due to the counterbalancing effect of the dry and wet seasons within the year. Then the coupling relationship between climate change and soil aridification was established in time and space. Moreover, the spatiotemporal response patterns of both were obtained. The results showed that the frequency of soil drought under meteorological drought conditions showed an increasing trend and that the sensitivity of soil drought occurrence increased. Among them, the effect of precipitation on relative soil moisture (RSM) was immediate, and the effect of prolonged warming on RSM is greater. The area of soil aridification that was caused by climatic aridification in spring accounted for 13.7% of the entire area. The regional aridification research mode proposed in this paper can provide ideas for subsequent studies. [Display omitted] • A comprehensive judgment mode of the regional aridification combining climate elements, soil moisture was established. • Over the last 20 years, the central part of the FPENC showed overall warming and wetting of the climate. • Over the last 20 years, the trend of the soil aridification in the central part of the FPENC remained severe. • The counterbalancing effect of the dry and wet seasons leads to recessive aridification. • The sensitivity of the occurrences of soil drought to the climate drought anomalies continuously became stronger. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on terrestrial wet and dry changes and the relationship between the two is of great significance to the sustainable development of terrestrial ecosystems. The farming-pastoral ecotone of northern China (FPENC) is an area that is sensitive to climate change, suffering from perennial drought and a clear aridification trend. Unlike previous single-factor, single-timescale studies, we identified aridification in the region based on a dataset established by remote sensing and ground-based monitoring stations from a combination of two perspectives: climate and soil. The results show that, in terms of climate, the period from 2000 to 2019 was the driest in the region during the last 120 years , and the summer drought was the most severe and shifted from a summer to spring drought; in terms of soil, the soil aridification trend in the region was severe, with 16.1% of the areas becoming significantly drier (P < 0.1) among the years and 41.6% in spring, respectively. Similar to climate change, soils exhibited recessive aridification due to the counterbalancing effect of the dry and wet seasons within the year. Then the coupling relationship between climate change and soil aridification was established in time and space. Moreover, the spatiotemporal response patterns of both were obtained. The results showed that the frequency of soil drought under meteorological drought conditions showed an increasing trend and that the sensitivity of soil drought occurrence increased. Among them, the effect of precipitation on relative soil moisture (RSM) was immediate, and the effect of prolonged warming on RSM is greater. The area of soil aridification that was caused by climatic aridification in spring accounted for 13.7% of the entire area. The regional aridification research mode proposed in this paper can provide ideas for subsequent studies. [Display omitted] • A comprehensive judgment mode of the regional aridification combining climate elements, soil moisture was established. • Over the last 20 years, the central part of the FPENC showed overall warming and wetting of the climate. • Over the last 20 years, the trend of the soil aridification in the central part of the FPENC remained severe. • The counterbalancing effect of the dry and wet seasons leads to recessive aridification. • The sensitivity of the occurrences of soil drought to the climate drought anomalies continuously became stronger. [ABSTRACT FROM AUTHOR]

This paper elaborates on recent advances in the use of ScanSAR technologies for wetland-related research. Applications of active satellite radar systems include the monitoring of inundation dynamics as well as time series analyses of surface soil wetness. For management purposes many wetlands, especially those in dry regions, need to be monitored for short and long-term changes. Another application of these technologies is monitoring the impact of climate change in permafrost transition zones where peatlands form one of the major land cover types. Therefore, examples from boreal and subtropical environments are presented using the analysed ENVISAT ASAR Global mode (GM, 1km resolution) data acquired in 2005 and 2006. In the case of the ENVISAT ASAR instrument, data availability of the rather coarse Global Mode depends on request priorities of other competing modes, but acquisition frequency may still be on average fortnightly to monthly depending on latitude. Peatland types covering varying permafrost regimes of the West Siberian Lowlands can be distinguished from each other and other land cover by multi-temporal analyses. Up to 75% of oligotrophic bogs can be identified in the seasonal permafrost zone in both years. The high seasonal and inter-annual dynamics of the subtropic Okavango Delta can also be captured by GM time series. Response to increased precipitation in 2006 differs from flood propagation patterns. In addition, relative soil moisture maps may provide a valuable data source in order to account for external hydrological factors of such complex wetland ecosystems. [Copyright &y& Elsevier]

Abstract: Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method – the RIparian Classification Algorithm (RICAL) – to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future. [Copyright &y& Elsevier]