冬小麦旱情遥感监测模型研究

本研究依据土壤水量平衡及能量平衡的原理,提出了一套利用遥感方法监测冬小麦干旱及建模的方法。首先,用热惯性方法建立试区土壤表观热惯量与土壤水分的关系,用其估算初始土壤含水量(W_0);再用NOAA-AVHRR数字图像和气象数据相结合的方法估算冬小麦地的蒸散(E_t);从而根据水量平衡方程获得某一时段(旬)的土壤含水量(W_t);最后,根据冬小麦的需水规律和土壤有效水含量构造干旱指数模型。试验表明,此模型反映了作物干旱的本质,能大范围有效地监测作物旱情。     

土壤水分的遥感监测方法概述

回顾了目前国内外土壤水分的遥感监测方法,介绍了反射率法、植被指数法、地表温度法、温度-植被指数法、作物水分胁迫指数法、热惯量法和微波法,并对各方法的优缺点进行了详细比对;在总结国内外土壤水分遥感监测研究方法的基础上,对目前该研究领域的重点、难点和未来的发展方向进行了评价。认为:热惯量法和植被温度指数法是较为成熟的方法;微波遥感因其独特的优越性,将是该领域的重点研究方向。     

遥感监测土壤湿度综述及其在新疆的应用展望

土壤湿度在全球水循环运动中扮演着非常重要的角色,是水文、气象和农业研究中的重要参数,国内外都极为重视对土壤湿度的研究。国外利用可见光、红外、热红外、微波遥感监测土壤水分已有三、四十年的历史,随着研究的深入和技术的发展,现已形成地面、航空、航天、多星的立体干旱遥感监测格局。国内遥感监测土壤湿度的方法主要有微波遥感、热红外遥感、距平植被指数法、植被供水指数、作物缺水指数等方法。本文通过对国内外已有的土壤湿度遥感监测方法的介绍和总结,对比分析了各种方法的原理、适用领域及其研究进展,并针对新疆的具体情况,认为借助Mod is影像进行新疆地区土壤湿度的监测是较为可行的一种方法。     

基于MODIS数据的农业干旱遥感指数对比和应用

以辽西北为研究区域,选取典型干旱年2009年作物(春玉米)主要生长季,采用表观热惯量(apparent thermal inertia,ATI)、距平植被指数(anomalies of vegetation index,AVI)和植被供水指数(vegetation supply water index,VSWI)3种基于不同理论的遥感干旱指数方法对土壤水分进行反演,分析其监测效果。结果表明,3种指数分别在一定程度上反映出了辽西北地区2009年的旱情趋势,但得到的反演结果并不一致;ATI在中高植被覆盖率下的监测效果高于预期结果,比较符合历史气象资料;AVI可以有效反映当年作物主要生长季各时期相对的受旱状况;VSWI夸大了植被的影响作用,存在严重的滞后性。     

基于MODIS数据对不同植被覆盖下土壤水分监测的可行性研究

论文利用2005年、2006年怀来实验场附近玉米生长期中的多次野外试验数据，以热惯量模型为基础，探讨MODIS数据监测土壤水分的可行性。论文对该模型与算法进行了分析，并探讨了不同植被覆盖度下，热惯量方法监测土壤水分的可行性。论文引入归一化植被指数对热惯量法进行修正，得到一个新的回归参数，通过实验分析验证，在不同植被覆盖下，该参数与土壤含水量具有较好的相关关系，具有一定的实际可行性。     

热惯量法在监测土壤表层水分变化中的研究

利用遥感方法监测一定层水深土壤水分变化，关键是建立卫星数据与地表水热变化关系，该文地遥感定量反演土壤水热变化的数值模拟系统及热惯量在其边界，初始条件确定中所起的作用进行的简述，并介绍了热惯量法求解表层水分含量的发展概况，为进一步提高定量化监测方法的地必琢计算精度，该文发展了地表能量平均方程的一种新的化简方法，经过这样的处理，可从遥感图象数据直接得到真实热惯量值，进而得到土壤水分含量分布，通过野外实     

基于MODIS数据的干旱区土壤水分反演

为了更好地研究内蒙古额济纳盆地的土壤水分时空分布及动态变化,基于MODIS数据,利用热惯量模型,计算了表观热惯量,并与实测数据进行回归分析建模,反演了内蒙古额济纳盆地的土壤水分。结果表明,利用MODIS数据产品,反演参量获取简单,可降低反演土壤含水量的复杂性,有利于大、中尺度的实际应用;砂土对应的表观热惯量均值较大,粘土次之,壤土最小;相比粘土,壤土和砂土的表观热惯量值比较大且分散;绿洲区表观热惯量值比戈壁和沙漠区大;热惯量模型在20 cm左右深度土壤水分反演效果最好,可有效反演干旱区土壤水分。     

2002年秋季山东省干旱遥感监测分析

利用极轨气象卫星遥感资料对2002年山东省秋季干旱进行监测和服务,其监测方法主要采用热惯量法。为了提高土壤水分模式的计算精度,将全省分为4个区域,对利用卫星资料反演的地表温度日较差进行植被指数等环境因素订正。在地理信息系统支持下,比较准确地计算出农田受旱面积,效果良好。     

在GIS支持下用NOAA/AVHRR数据进行旱情监测

旱灾是影响农作物生产的一种重大自然灾害,它对中国北方春小麦生产的影响极大。本文介绍了一个应用遥感、ＧＩＳ技术对黄淮海平原春小麦的旱情进行监测的系统及其方法。它综合使用的ＮＯＡＡ／ＡＶＨＲＲ数据、地面气象资料和地图,选用作物缺水指数（ＣＷＳＩ）模型和热惯理模型对旱情进行监测。监测结果分别用分县的旱情等级分布图和相应不同等级的旱情面积、面积比例数统计表来表示。该系统自１９９４年起投入运行３年,监测结果     

旱情遥感监测研究进展与应用案例分析

在大范围、长时序的旱情监测中,遥感技术以其快速、经济和大空间范围获取的特点,弥补了基于台站气象数据旱情监测的不足,为防旱和抗旱决策提供了实时、动态、宏观的辅助决策数据。本文对已有旱情遥感监测方法进行分析和整理,将其总结为基于土壤热惯量、基于土壤波谱特征、基于蒸散模型和基于植被指数的旱情监测方法,并对各类方法从监测原理、适用范围和应用进展等方面进行了阐述。在此基础之上,详细介绍一种结合了全球植被水分指数和短波角度归一化指数的优势建立的旱情遥感监测模型和方法。以2010年春季西南地区旱情为应用案例,从监测模型方法、数据处理流程和应用分析等方面,介绍一种基于植被水分指数的旱情监测方法,并对其监测结果进行统计分析与评价。     

土壤湿度微波遥感监测研究进展

土壤湿度是农业生产的重要影响因子,获取土壤湿度信息以制定人工干预调节措施是稳固生产的重要保证,实时、有效地监测土壤墒情显得尤为重要。利用遥感数据反演土壤湿度有多种方法,微波遥感法被认为是目前最佳的监测方法。本文总结了被动、主动微波土壤湿度遥感监测的主要模型、方法及其优缺点和适用范围,分析了雷达遥感监测土壤湿度的最优参数选取等,展望了微波遥感监测土壤湿度的应用前景,以期为土壤湿度微波遥感监测研究提供参考和借鉴。     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

Remotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and aboveground biomass production. The present experiment was set up to identify the effects on soil-plant systems of biochar produced more than 150 years ago in charcoal mound kiln sites in Wallonia (Belgium). Although the impacts of biochar on soil-plant systems are being increasingly discussed, a detailed monitoring of the crop dynamics throughout the growing season has not yet been well addressed. At present there is considerable interest in applying remote sensing for crop growth monitoring in order to improve sustainable agricultural practices. However, studies using high-resolution remote sensing data to focus on century-old biochar effects are not yet available. For the first time, the impacts of century-old biochar on crop growth were investigated at canopy level using high-resolution airborne remote sensing data over a cultivated field. High-resolution RGB, multispectral and thermal sensors mounted on unmanned aerial vehicles (UAVs) were used to generate high frequency remote sensing information on the crop dynamics. UAVs were flown over 11 century-old charcoal-enriched soil patches and the adjacent reference soils of a chicory field. We retrieved crucial crop parameters such as canopy cover, vegetation indices and crop water stress from the UAV imageries. In addition, our study also provides in-situ measurements of soil properties and crop traits. Both UAV-based RGB imagery and in-situ measurements demonstrated that the presence of century-old biochar significantly improved chicory canopy cover, with greater leaf lengths in biochar patches. Weighted difference vegetation index imagery showed a negative influence of biochar presence on plant greenness at the end of the growing season. Chicory crop stress was significantly increased by biochar presence, whereas the harvested crop yield was not affected. The main significant variations observed between reference and century-old biochar patches using in situ measurements of crop traits concerned leaf length. Hence, the output from the present study will be of great interest to help developing climate-smart agriculture practices allowing for adaptation and mitigation to climate.     

Crop yield estimation model for Iowa using remote sensing and surface parameters

Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), vegetation condition index (VCI) and temperature condition index (TCI) for mapping and monitoring of drought and assessment of vegetation health and productivity. NDVI, soil moisture, surface temperature and rainfall are valuable sources of information for the estimation and prediction of crop conditions. In the present paper, we have considered NDVI, soil moisture, surface temperature and rainfall data of Iowa state, US, for 19 years for crop yield assessment and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction.     

Generation of thermal inertia image over a part of Gujarat: A new tool for geological mapping

Thermal inertia is a volume property and can be used to detect the subsurface features in an alluvium-covered area. A thermal inertia image has been generated over a part of Gujarat using consecutive day and night NOAA-AVHRR data. Gujarat contains two important rift basins in the western margin of India, namely, Cambay and Kutch basins. Major land covers exist in this region are alluvium, continental sediments, various rocks of Mesozoic and Cenozoic origin followed by Deccan Volcanics, tertiary and quaternary deposits. Validation of thermal inertia parameters with existing values obtained from literature indicates the efficacy of the developed technique. The study indicates that thermal inertia image can be used as a new method for geologic mapping/basin delineation where exposure is less or contrast is negligible between litho-units using traditional photography/sensing techniques.