Geoinfomatics, Civil Engineering Department, Indian Institute of Technology Kanpur, Kanpur, 208016, India.
J Environ Manage. 2019 Mar 15;234:75-89. doi: 10.1016/j.jenvman.2018.12.109. Epub 2019 Jan 4.
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.
本文使用两种被动微波遥感数据集,即高级微波散射辐射计-2 和土壤水分主动被动卫星传感器,研究了利用被动微波遥感估算根区土壤水分的方法。本研究以印度恒河平原的水稻和小麦两种作物为研究对象,该地区的作物和土壤类型以及土地利用类型动态变化。在该流域的北方邦、中央邦和比哈尔邦选择了 21 个水稻种植区和 23 个小麦种植区。通过递归指数滤波器,利用 10cm 和 40cm 处的地表土壤水分估算土壤湿润指数,从而得到根区土壤水分信息。该算法即使在没有流域水平地面信息的情况下也可实施。参考土壤水分数据集是从全球陆面数据同化系统-NOAH 在 10cm 和 40cm 深度处获取的。研究还表明,在没有地面(原位)土壤水分数据的情况下,GLDAS-NOAH 土壤水分数据具有很大的潜力。在影响地表和根区土壤水分的各种因素中,本研究评估了土壤成分对根区土壤水分的控制作用。在不同地点,用沙、粉砂和粘粒的特征时间延迟与 Spearman 秩相关系数进行估计。研究了水稻和小麦种植区的地表和根区土壤水分之间的耦合和趋势。通过计算决定系数、平均绝对误差和平均偏差误差,研究了两种不同卫星传感器在两个不同采集时间(升轨和降轨)时估算的 10cm 和 40cm 土壤湿润指数的精度。结果表明,两种卫星传感器对水稻和小麦作物的地表土壤水分的估算有显著差异,进而导致对根区土壤水分的估算也存在差异。由 SMAP 衍生的土壤湿润指数的决定系数较高(约 0.9),而由 AMSR-2 衍生的土壤湿润指数的决定系数较低(约 0.65)。在两个不同的时间和两个不同的深度都观察到了特征时间延迟的变化,随着深度的增加,特征时间延迟也随之增加。此外,在降轨时,特征时间延迟低于升轨。还观察到根区土壤水分与土壤质地之间存在很强的关系。对于水稻作物,在北方邦、中央邦和比哈尔邦,主要土壤类型为壤土和砂壤土,观察到与沙和粘粒呈正相关。而对于小麦种植区,在北方邦,观察到与粉砂和粘粒呈正相关,在中央邦观察到与沙呈正相关,而在中央邦和比哈尔邦,主要土壤类型为壤土和粘质壤土(轻壤土),观察到与沙呈正相关。本研究为了解可持续灌溉调度和提高水稻和小麦种植区的灌溉潜力提供了重要信息。了解影响作物种植的所有因素以及由此产生的根区土壤水分,有助于优化作物的生产。