Large-area rice yield forecasting using satellite imageries

Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice (Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha⁻¹ for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha⁻¹ provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.     

Visual discrimination of surface features of salt affected soils using satellite images in arid region of Rajasthan (India)

The study has been carried for visual discrimination of natural salt affected soils on FCC images of IRS 1 B in Pali district of Rajasthan. The salt affected soils show wide variations in salinity (EC_2.53.7 to 28 dSm^-1), alkalinity (pH 8.5-9.8), cover ofP. juliflora (10-90%), salt tolerant grasses (10–55%) and gravelly surface (20–35%). ThoughP. juliflora and grasses were present at most of the observation points their cover decreased with soil EC_2.5 values more than 10 and 13 dSm^-1, respectively. Five darkness categories derived as the result of visual interpretation of FCCs; and ground and laboratory studies revealed that the darkness category 1 represented fewer plant community with high salinity (EC 28.7 dSm^-1) and gravelly surface, categories 2 and 3 were characterised by grass cover and moderate salt affected soils (EC 3-10 dSm^-1) whereas category 4 was dominated by thicket ofP. juliflora. The derived numerical darkness categories of the FCC images were slightly low for February images. The darkness values of observation pixel on February images correlated positively withP. juliflora cover and negatively with grass cover and soil pH indicating that surface features on FCC were related with the immediate observation pixels.     

Long term shoreline oscillation and changes of Cauvery delta coastline inferred from satellite imageries

Coastal zone is highly volatile ecosystem which is always in adjustments. Loss of shore line will cause severe impact on human life and as well as their properties. Remote sensing is a reliable technique to study the historical shoreline changes. Therefore in this paper long term shoreline oscillations of Cauvery delta shorelines at Poompuhar, Tharangambadi and Nagapattinam were studied using satellite imageries and the same was physically observed at the above three locations with the help of reference pillars and compared mutually. It was observed that the shoreline at Poompuhar is under accretion at the rate of 1.79m/ year and other shoreline stretches at Tharangambadi and Nagapattinam were under erosion at 0.4888m/ year and 0.4985m/ year respectively. It was also observed that the remote sensing study qualitatively matches with the physical observation for all the three coastal stretches of the study area.     

Spectral reflectance characteristics of salt-affected arid soils of Rajasthan

The spectral reflectance characteristics of different types of natural and anthropogenic salt-affected soils have been studied under field conditions. The spectral reflectance value for non-saline and all types of salt-affected soils was maximum in near infra red region (800–1000 nm). The natural salt-affected soils having surface salt encrustation showed highest reflectance value followed by the sodic soils (formed due to high residual sodium carbonate water irrigation) natural saline soils and saline soils due to saline water irrigation. Soil texture, pH, CaC0₃ and organic matter together accounted for 29.6% variation in the maximum reflectance percentage value out of which only pH accounted for more than half (14.2% variation).     

Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach

Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to extract roads from high resolution satellite images. In this study, using few samples from road surface, a particle swarm optimization is applied to a fuzzy-based mean calculation system to obtain road mean values in each band of high resolution satellite colour images. Then, the images are segmented using the calculated mean values from the fuzzy system. Optimizing the fuzzy cost function by particle swarm optimization enables the fuzzy approach to be the best mean value of road with sub-grey level precision. Initially, this method was applied to simulated images where the calculated mean values are consistent with the hypothetic mean values. Application of the method to IKONOS satellite images has shown a prospective outcome for automatic road extraction. Mathematical morphology is subsequently used to extract an initial main road centreline from the segmented image. Then, small redundant segments are automatically removed. The quality of the extracted road centreline indicates the effectiveness of the proposed approach.     

Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images

In this paper, we present a method of earthquake damage detection by comparing the optical images with panchromatic bands for the Gujarat, India earthquake, which occurred on January 26, 2001. The data used in this study are optical remote sensing images taken by Landsat-7 satellite on January 8 and February 29, 2001, before and after the earthquake. We have investigated the pre and post-earthquake satellite images calculating the differences in the reflection intensity (digital number) of the two images. The estimated affected area has been subtracted on a pixel unit based on the obtained frequency distributions of the differences in the optical sensor values, which show significant changes in the reflectance due to the earthquake disaster. We have investigated the accuracy of our analysis result using a classification method for the training areas with aerial photographs taken after the earthquake. The two damage detection methods show a very similar result.     

Flash flood damage mapping in arid environment using satellite remote sensing — A case study of pali region

In the Bali and Pali tehsils of Pali district of western Rajasthan, which were affected by floods during the period August 6–10, 1990, using IRS-1A LISS-1 data of post-flood and ground truth, seven flood damage categories viz. (1) loss of bund and slight sheet erosion (2) loss of bunds, severe sheet and rill erosion and few gullies (3) deep gullies (4) water inundated area (5) moderate scouring and sand casting (6) severe scouring and sand casting and (7) river widening and bank cutting have been mapped. Out of seven, four categories could be mapped visually on the raw FCC (post-flood) and remaining three categories could be separated out from the digitally generated FCC. The PC₂ was found to contain maximum information on soil erosion/deposition and inundated areas. Density-slicing of band-ratioed output gave maximum information on newly formed channels, water bodies and flow direction. The damage caused to be human beings, animals, agricultural lands, engineering structure by different type of flood hazards under various geomorphic flood zone and comparison between pre-flood and post-flood product has been highlighted.     

Characteristics and morphogenesis of salt-affected soils in southeastern arid Rajasthan

Aerial photographs coupled with ground check and laboratory analysis have helped in mapping of four categories of salt affected soils located in the southeastern tract of arid Rajasthan. The categories are (1) Natural saline soils (2) Relict saline soils (3) Secondary salinized soils due to high water table and (4) Secondary salinised soils due to highly saline water use for irrigation Salinity in natural salt affected soils is mostly sodium chloride followed by sodium-calcium chloride and sodium-chloride-sulphate type. The distribution of the natural salt affected soils along the natural drainage or inconspicuous depressional areas suggest that their occurrence is due to insufficient surface drainage. Further, the pattern of distribution indicates that the origin of salt is within the catchment itself. Deep ploughing and application of organic material have reversed the upward flux of salts and improved the soils. This phenomenon also seems to explain the large occurrence of soils of relict salinity.     

Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data

The accuracy of cotton crop classification using satellite data has been assessed with respect to a detailed land cover map prepared by field survey. The effect of spatial resolution on classification accuracy was studied using LISS-I (spatial resolution 72.6 m) and LISS-II data (spatial resolution 36.25 m) of the Indian remote sensing satellite IRS-1B. The performances of the maximum likelihood and the minimum distance to mean as classifiers have also been assessed. LISS-II data have been found to give a higher classification accuracy. The estimate of cotton acreage using LISS-II data was closer to that obtained from the base map. The maximum likelihood classifier (MXL) and the minimum distance to mean (MDM) classifier performed equally well.     

Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method

Interest in high-resolution satellite imagery (HRSI) is spreading in several application fields, at both scientific and commercial levels. Fundamental and critical goals for the geometric use of this kind of imagery are their orientation and orthorectification, processes able to georeference the imagery and correct the geometric deformations they undergo during acquisition. In order to exploit the actual potentialities of orthorectified imagery in Geomatics applications, the definition of a methodology to assess the spatial accuracy achievable from oriented imagery is a crucial topic.In this paper we want to propose a new method for accuracy assessment based on the Leave-One-Out Cross-Validation (LOOCV), a model validation method already applied in different fields such as machine learning, bioinformatics and generally in any other field requiring an evaluation of the performance of a learning algorithm (e.g. in geostatistics), but never applied to HRSI orientation accuracy assessment.The proposed method exhibits interesting features which are able to overcome the most remarkable drawbacks involved by the commonly used method (Hold-Out Validation — HOV), based on the partitioning of the known ground points in two sets: the first is used in the orientation–orthorectification model (GCPs — Ground Control Points) and the second is used to validate the model itself (CPs — Check Points). In fact the HOV is generally not reliable and it is not applicable when a low number of ground points is available.To test the proposed method we implemented a new routine that performs the LOOCV in the software SISAR, developed by the Geodesy and Geomatics Team at the Sapienza University of Rome to perform the rigorous orientation of HRSI; this routine was tested on some EROS-A and QuickBird images. Moreover, these images were also oriented using the world recognized commercial software OrthoEngine v. 10 (included in the Geomatica suite by PCI), manually performing the LOOCV since only the HOV is implemented.The software comparison guaranteed about the overall correctness and good performances of the SISAR model, whereas the results showed the good features of the LOOCV method.     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

无人艇载高清多光谱影像配准

针对无人艇载高清多光谱影像特性,提出了集成单应性矩阵内配准与置平薄板样条外配准的多光谱影像整体配准方案:(1)傅里叶-梅林变换引导下的多光谱影像相关匹配自动生成连接点;(2)单应性矩阵最小二乘联合估计实现多波段影像相互配准(内配准);(3)构建“置平”薄板样条变换模型实现多光谱影像序列无地理坐标配准(外配准),最后对无人飞艇搭载多CCD相机低空拍摄的高清多光谱影像进行配准验证,取得了约1个像元误差的内配准精度以及较好的外配准视觉质量。     

Tea acreage estimation and condition assessment using satellite data in nilgiri district,tamilnadu

In the present study an attempt has been made to estimate acreage and condition of tea plantations by using satellite based digital remotely sensed data in visible, near infra-red and middle infra-red spectral regions, in the Nilgiri district of Tamilnadu state. Landsat MSS and TM data, acquired on Dec. 26, 1990 were used in the analysis, Different spectral band combinations, Landsat MSS (1234), TM (1234), TM (2345) and TM (123457) were used for identification of tea plantations. District-boundary-overlaying approach with complete enumeration of digital data was used for estimation of tea acreages. Condition assessment of tea plantations is based on the Greenness Index. Use of Landsat MSS data resulted in an underestimation of area under tea whereas the acreages estimated by using TM spectral band combinations 1234 and 2345 compared closely with the estimates of Department of Horticulture (DOH). The distribution pattern of various condition classes of tea plantations compared well with the prevailing ground conditions as observed during post-classification field survey in September 1992 in the district.     

北斗三号全球卫星导航系统空间信号精度评估

针对北斗三号 (BDS-3)正式开通后的空间信号精度情况,选取2020-08-01—2021-07-31共 1 a的混合广播星历数据,以德国波茨坦地学研究中心(GFZ)和武汉大学国际GNSS服务(IGS)数据中心(WHU)提供的精密星历为参考分别从轨道精度、钟差精度和空间信号测距误差(SISRE)来进行BDS-3的空间信号精度评估. 结果表明:BDS-3的轨道精度在径向(R)、切向(A)、法向(C)三个方向上分别优于0.100 m、0.405 m、0.547 m,钟差精度优于1.926 ns,仅受轨道影响的SISRE (orb)为0.134 m,SISRE为0.612 m. 地球静止轨道(GEO)卫星的SISRE为1.137 m,倾斜地球同步轨道(IGSO)卫星和中圆地球轨道(MEO)卫星的SISRE相比GEO卫星分别减少36.3%、51.3%.      

"墨子号"卫星空间安全评估

“墨子号”卫星是2011年中科院空间科学战略性先导科技专项首批批准的五颗科学实验卫星之一,旨在建立卫星与地面远距离量子科学实验平台,并在此平台上完成空间大尺度量子科学实验任务．作为一颗科学卫星,它已经为中国在空间量子通信领域奠定了基础,受到国内外的普遍关注．2019年1月起,“墨子号”卫星开始进入延寿期工作,该卫星仍然承担着繁忙的拓展实验任务,包括国际合作实验任务等．“墨子号”卫星在轨空间安全问题成为“墨子号”卫星团队的关注点之一．本文基于国际空间碎片或空间目标数据信息,结合“墨子号”卫星星历数据,仿真了2020年3月17日-3月24日,“墨子号”最可能遭遇的空间碎片或空间目标情况,并给出了一些重要参数结果,对“墨子号”卫星运行具有参考作用．      

Irrigation command area inventory and assessment of water requirement using IRS-1B satellite data

Monitoring the crop acreage and irrigation water requirements vis-a-vis irrigation water supplies is important to obtain a realistic view of the “irrigation potential” and “potential utilised”. Satellite data provides information on crop area and thereby net irrigation water requirements of crops. A pilot study was taken up in Mahendragarh distributary canal in Haryana State to estimate net irrigation water requirement of crops under 17 minors for kharif and rabi seasons of 1992–93 period using IRS-1B satellite geocoded FCC images. These water requirements, when analysed with canal and tubewell water supplies for crops, show largescale deficiencies in the irrigation command area.     

Evaluation of multisensor data for delineating salt affected soils in arid rajasthan

The Landsat (MSS and TM), SPOT (PLA and MLA) and IRS (LISS-I and LISS-II) images of crop free period (April, May), rainfed crop (October) and rabi irrigated crop (January, February) have been evaluated for their capabilities of mapping (1) primary salt affected soils: (slightly, moderately and severely) (2) saline water irrigated saline soils, (3) sodic water irrigated sodic soils and (4) salt affected soils due to tank seepage in the arid region of Rajasthan. The moderately and severe salt affected soils could be mapped with Landsat, (IRS LISS-I) and SPOT, images of any season. However, the summer season imagery provided maximum extent of salt affected soils. The LISS-II imagery also provided delineation of slightly salt affected soils in addition to the moderate and severely salt affected soils. The delineation of saline and sodic water irrigated areas was possible by using Landsat False Colour Composite for the January month by their characteristic reflectance, existing cropping pattern and the quality of irrigation water being used in the area. The IRS (LISS-II) and SPOT PLA images for the May month were also used for mapping of saline and sodic water irrigated soils.     

Improving satellite image classification by using fractional type convolution filtering

This letter shows how conventional methods for satellite image classification can be improved by applying some filtering algorithms as a pre-classifying step. We will introduce a filtering scheme based on convolution equations of fractional type. The use of this kind of filter as a pre-classification step will be illustrated by classifying MODerate-resolution Imaging Spectroradiometer (MODIS) data to map burned areas in Mediterranean countries. The methodology we propose improved the estimations obtained by merely classifying the post-fire images (i.e. without filtering) in the study areas considered.     

Evaluation of ground water potential in the environs of Jhansi city,Uttar Pradesh using hydrogeomorphological assessment by satellite remote sensing technique

An attempt has been made to delineate different hydrogeomorphological units in and around the immediate environs of Jhansi city with a view to attempt a correlation between the well yields and hydrogeomorphic units using satellite remote sensing technique. In general, a positive correlation is observed between the geomorphic units and the borewell yields with overlapping yields at the margin. The pediment residual hill complex is observed to provide wells with discharges ranging from 100 gallons per hour (gph) to 5000 gph, while the wells drilled in shallow weathered, buried pediplain has yields in the range of 2000 to 10000 gph moderately weathered, buried pediplain has discharges in the range of 8000 to 12000 gph, and deeply weathered, buried pediplain has discharges in excess of 12000 gph.     

Land use/land cover identification in an alpine and arid region (Nubra Valley, Ladakh) using satellite remote sensing

The remote sensing technology has been widely used for mapping the vegetation types in the tropical landscapes. However, in the temperate and alpine arid regions of India very few studies have been conducted using this technique. In the mountainous temperate arid conditions the vegetation is largely confined to marsh meadows, streams courses, river valleys and moist pockets close to snowfields. The ground truth collection in these zones are physically challenging due to tough terrain and restricted mobility. The detailed mapping of vegetation and other land use classes in these areas is therefore, extremely difficult. This paper describes the use of IRS-ID LISS III sensor for deciphering land cover details Nubra Valley, northern portion of Ladakh Autonomous Hill Council, Jammu & Kashmir (India). This analysis essentially emphasizes in bringing out various vegetation classes (speciallyHippophae rhamnoides and other medicinal plant communities) along the narrow river valleys.