Temporal persistence and stability of surface soil moisture in a semi-arid watershed

Satellite soil moisture products, such as those from Advanced Microwave Scanning Radiometer (AMSR), require diverse landscapes for validation. Semi-arid landscapes present a particular challenge to satellite remote sensing validation using traditional techniques because of the high spatial variability and potentially rapid rates of temporal change in moisture conditions. In this study, temporal stability analysis and spatial sampling techniques are used to investigate the representativeness of ground observations at satellite scale soil moisture in a semi-arid watershed for a long study period (March 1, 2002 to September 13, 2005). The watershed utilized, the Walnut Gulch Experimental Watershed, has a dense network of 19 soil moisture sensors, distributed over a 150 km² study region. In conjunction with this monitoring network, intensive gravimetric soil moisture sampling conducted as part of the Soil Moisture Experiment in 2004 (SMEX04), contributed to the calibration of the network for large-scale estimation during the North American Monsoon System (NAMS). The sensor network is shown to be an excellent estimator of the watershed average with an accuracy of approximately 0.01 m³/m³ soil moisture. However, temporal stability analysis indicated that while much of the network is stable, the soil moisture spatial pattern, as represented by mean relative difference, is not replicated by the network mean relative difference pattern. Rather, the network is composed of statistical samples. Geophysical aspects of the watershed, including topography and soil type are also examined for their influence on the soil moisture variability and stability. Soil type, as characterized by bulk density, clay and sand content, was responsible for nearly 50% of the temporal stability. Topographic effects were less important in defining representativeness and stability.     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.     

Aircraft based soil moisture retrievals under mixed vegetation and topographic conditions

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm³/cm³) was higher than over the Arizona domain (SEE = 0.014 cm³/cm³). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter

Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m^− 2 at BTS and DGS and 2 W m^− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes.     

作物残体去向与利用及对土壤氮素转化的影响

近年来,作物残体还田受到了很大的关注,一方面它可以改进土壤氮素的动态变化,减少硝态氮淋失;另一方面可作为提高耕地土壤作为潜在氮储库的一种手段。作物残体是碳、氮的重要源和库,因此还田后会影响土壤中的氮素循环。本文综述了作物残体还田后对土壤氮素转化的影响及残体氮素的利用与去向问题。具体阐述了以下几个方面内容:作物残体的降解过程及影响因素,残体氮素的利用率及去向问题,以及作物残体对土壤无机氮库、有机氮库、微生物特征的影响。     

江河源区高寒嵩草草甸土壤氨基酸成分和含量特征分析

通过高效液相色谱技术分析了青海省果洛州达日县窝赛乡原生嵩草草甸、严重退化草地及人工草地三类植被土壤中各种氨基酸成分及含量。结果表明:(1)三种类型土壤中都检测出19种常见氨基酸:精氨酸、天冬氨酸、丝氨酸、谷氨酸、苏氨酸、丙氨酸、甘氨酸、氨基丁酸、脯氨酸、蛋氨酸、缬氨酸、苯丙氨酸、异亮氨酸、亮氨酸、胱氨酸、组氨酸、鸟氨酸、赖氨酸、酪氨酸;(2)测定结果表明原生嵩草草甸土壤的氨基酸总量显著高于人工恢复重建草地和严重退化土壤氨基酸,而后两者之间差异不显著。原生高寒草地的土壤(6316.28μgg-1)严重退化草地土壤(2977.10μgg-1)人工恢复重建草地土壤(2975.90μgg-1)。(3)原生高寒草地土壤氨基酸总体呈现下降趋势:5月氨基酸含量最高,随后6月7月的显著下降,8月稍微有所回升,9月氨基酸含量到达最低;严重退化草地土壤与人工恢复重建草地土壤氨基酸含量季节变化相似,氨基酸总量在6月份到达最高点,随后7月8月显著下降,9月份稍微有所回升。     

苯磺隆和2,4-D胁迫对土壤微生物影响的研究

以盆栽试验研究了除草剂苯磺隆、2,4-D对石灰性褐土中土壤微生物群落的影响。结果表明,在供试浓度下,土壤微生物对两种除草剂的感应存在一定的差异性。苯磺隆高浓度处理在最初3d极显著地抑制细菌生长,之后对土壤细菌具有刺激作用,特别是培养结束时激活率还高达917.6%,低浓度处理呈极显著的刺激作用;苯磺隆对土壤放线菌具有显著的刺激作用,对土壤真菌有强烈的抑制作用,最高抑制率达100%,到培养结束时(60d)真菌数量还未恢复到对照水平。2,4-D处理对土壤放线菌具有明显的抑制、激活的波动性,对土壤细菌、真菌有强烈的抑制作用。     

利用植被覆盖度-地表温度特征空间提取厦门市土壤湿度信息

土壤湿度是水文学、气象学以及农业科学研究领域的一个重要参数。利用1989年和1996年两个时相Landsat TM遥感卫星影像来获取厦门的植被覆盖度和地表温度,并由此构建了植被覆盖度和地表温度组成的特征空间,最后利用该特征空间反演了土壤湿度,并分析湿度在两个时相中的变化情况。结果显示,在所研究的时间段内,厦门西南老城区的湿度得到增加。但是在东北部城市建成区扩展的地区,土壤湿度则明显降低。     

Targeting orthotopic gliomas with renal-clearable luminescent gold nanoparticles

A major clinical translational challenge in nanomedicine is the potential of toxicity associated with the uptake and long-term retention of non-degradable nanoparticles (NPs) in major organs.The development of inorganic NPs that undergo renal clearance could potentially resolve this significant biosafety concern.However,it remains unclear whether inorganic NPs that can be excreted by the kidneys remain capable of targeting tumors with poor permeability.Glioblastoma multiforme,the most malignant orthotopic brain tumor,presents a unique challenge for NP delivery because of the blood-brain barrier and robust blood-tumor barrier of reactive microglia and macroglia in the tumor microenvironment.Herein,we used an orthotopic murine glioma model to investigate the passive targeting of glutathione-coated gold nanoparticles (AuNPs) of 3 nm in diameter that undergo renal clearance and 18-nm AuNPs that fail to undergo renal clearance.Remarkably, we report that 3-nm AuNPs were able to target intracranial tumor tissues with higher efficiency (2.3x relative to surrounding non-tumor normal brain tissues) and greater specificity (3.0x)than did the larger AuNPs.Pharmacokinetics studies suggested that the higher glioma targeting ability of the 3-nm AuNPs may be attributed to the longer retention time in circulation.The total accumulation of the 3-nm AuNPs in major organs was significantly less (8.4x) than that of the 18-nm AuNPs.Microscopic imaging of blood vessels and renal-clearable AuNPs showed extravasation of NPs from the leaky blood-tumor barrier into the tumor interstitium.Taken together,our results suggest that the 3-nm AuNPs,characterized by enhanced permeability and retention,are able to target brain tumors and undergo renal clearance.