Ground Based Bistatic Scatterometer Measurement for the Estimation of Growth Variables of Ladyfinger Crop at X-Band

The present study describes the ground based bistatic scatterometer measurements of ladyfinger crop at its various growth stages in the specular direction with the azimuthal angle (\( \phi = 0 \)) for the angular incidence angle ranging from 20° to 60° at the interval of 10° at HH and VV polarization. An outdoor ladyfinger crop bed of an area 4 × 4 m² was specially prepared for the ground based bistatic scatterometer measurements. The crop growth variables like vegetation water content, leaf area index, fresh biomass, and plant height were also measured at the time of each bistatic scatterometer measurement. The specular bistatic scattering coefficients were found to be decreasing with the crop growth variables up to the maturity stage and then after it increased slightly. The linear regression analysis was carried out between specular bistatic scattering coefficient and crop growth variables at all the incidence angles for HH and VV polarization to select the optimum angle of incidence and polarization for the estimation of crop growth variables. The potential of subtractive clustering based adaptive neuro-fuzzy inference system was applied for the estimation of crop growth variables. The estimated values for different crop growth variables were found almost close to the observed values.     

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m² area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R^2?=?0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R^2?=?0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms.     

Inversion of PROSAIL Model for Retrieval of Plant Biophysical Parameters

The current development of satellite technology particularly in the sensors like POLDER and MISR, has emphasized more on directional reflectance measurements (i.e. spectral reflectance of the target measured from different view zenith and azimuth angles) of the earth surface features mainly the vegetation for retrieval of biophysical parameters at regional scale using radiative transfer models. This approach being physical process based and uses directional reflectance measurement has been found to better and more reliable compared to the conventional statistical approach used till date and takes care of anisotropic nature (i.e. reflectance from the target is different if measured from different view angles) of the target. Keeping this in view a field experiment was conducted in mustard crop to evaluate the radiative transfer model for biophysical parameter retrieval through its inversion with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments. The bidirectional reflectance data obtained at 5 nm interval for a range of 400–1100 nm were integrated to IRS LISS–II sensor’s four band values using Newton Cotes Integration technique. Biophysical parameters like leaf area index, leaf chlorophyll content, leaf length, plant height and average leaf inclination angle, biomass etc were estimated synchronizing with the bi-directional reflectance measurements. Radiative transfer model PROSAIL model was validated and its inversion was done to retrieve LAI and ALA. Look Up Table (LUT) of Bidirectional reflectance distribution function (BRDF) was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40° to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al. (1986). The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.     

Concurrent use of active and passive microwave remote sensing data for monitoring of rice crop

Estimation of crop area, growth and phenological information is very important for monitoring of agricultural crops. However, judicious combination of spatial and temporal data from different spectral regions is necessary to meet the requirement. This study highlights the use of active microwave QuikSCAT Ku-band scatterometer and Special Sensor Microwave/Imager (SSM/I) passive microwave radiometer data to derive information on important phenological phases of rice crop. The wetness index, a weekly composite product derived using brightness temperatures from 19, 37 and 85 GHz channels of SSM/I, was used to identify the puddling period. Ku-band scatterometer data provided the signal of transplanted rice seedlings since they acts as scatterers and increases the backscattering. Dual peak nature of temporal backscatter curve around the heading stage of rice crop was observed in Ku-band. The decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover. The symmetric (Gaussian) and asymmetric (lognormal) curve fits were attempted to derive the date of initiation of the heading phase. The temporal signature from each of these sensors was found to complement each other in crop growth monitoring. Image showing pixel-wise timings of heading stage revealed the differences exists in various parts of the study area.     

Spatio-Temporal Coherence Based Technique for Near-Real Time Sea-Ice Identification from Scatterometer Data

The identification of sea-ice has frequently been cited as one of the most important tasks for deriving the sea-ice parameters and to avoid erroneous retrieval of wind vector over sea-ice infested oceans using space-borne scatterometer data. Discrimination between sea-ice and ocean is ambiguous under the high wind and/or thin/scattered ice conditions. The pre-launch technique developed for Oceansat-2, utilizes the dual-polarized QuikSCAT scatterometer data by using the spatio-temporal coherence properties of sea ice in addition to backscatter coefficient and the Active Polarization Ratio. Results were compared with the operational sea-ice products from National Snow and Ice Data Center. The threshold API value of −0.025 was found optimum for sea-ice and ocean discrimination. The overall sea-ice identification accuracy achieved was of the order of 95 per cent, ranging from 92.5% (during December in Southern Hemisphere) to 98% (during March in Northern Hemisphere). The applicability of the algorithm for both the Arctic as well as Antarctic makes it suitable for its operational use with the Oceansat-2 scatterometer data.     

General Regression Neural Network and Radial Basis Neural Network for the Estimation of Crop Variables of Lady Finger

Estimation of crop variables is necessary for crop type monitoring as well as crop yield forecast. At the present era artificial neural network methodology are widely used to the remote sensing domain for numerous applications like crop yield forecasting and crop type classification. In the present work, two neural network models namely general regression neural network (GRNN) and radial basis function neural network (RBFNN) are used to estimate crop variables: leaf area index (LAI), biomass (BM), plant height (PH) and soil moisture (SM) by using ground based X-band scatterometer data. The both networks are trained and tested with X-band scatterometer data. The performance of the GRNN and RBFNN networks are found that the radial basis approach is more suitable for crop variable estimation in comparison to the GRNN approach. This work presents the applicability of neural network as an estimator and method employed could be useful to estimate the crop variables of other crops.     

Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India

The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.     

散射计资料的风场神经网络反演算法研究

建立一种神经网络反演海面风场的算法。该算法以ERS_1/2散射计数据和欧洲中期预报分析风场(ECM-WF)的配准点数据作为神经网络训练和检验数据集。研究表明,该算法具有运行速度快和精度高等特点,反演的风速和风向与C波段第4模型(COMD 4)和ECMWF吻合较好。     

Spectral response of maize at different growth stages

Maize crop was sown at weekly intervals on six dates in a randomized replicated trial under nonlimiting moisture conditions. The different dates of sowing represent different growth stages in the same given environment. Spectral data were collected using a portable radiometer at different wavelengths, ranging form visible to infrared on two different dates. The spectral reflectance data in the red and infrared region were analysed for their sensitivity to leaf area index and leaf dry biomass. During active crop growth period significant correlations existed between leaf area index and ratio of infrared to red as well as the normalized differences. Similar relationships were also observed between dry biomass and spectral data. However, these relationships were found to be valid upto the crop growth stage when the leaf area index has reached its maximum, corresponding to flowering. Beyond this stage, the spectral reflectances were found to be not related to LAI. The relsults suggest the possibility of obtaining crop phenological information from the spectral response data.     

A Case Study on Forewarning of Yellow Rust Affected Areas on Wheat Crop Using Satellite Data

Objective of this study was to identify stripe rust affected areas of wheat crop as well as evaluation of remote sensing (RS) derived indices. Moderately low temperature and high humidity favour the growth of yellow rust. Most affected areas of Punjab are the foothill districts such as Gurdaspur, Hoshiarpur and Ropar. Occurrence of yellow rust is possible when maximum temperature for day is below 15 °C and Temperature difference of day’s maximum and minimum temperature is less than 5 °C during the early growth of wheat. Forecast of the infestation was done using 3 days forecast of weather data obtained from Weather Research and Forecasting (WRF) model at 5 km resolution. Weather forecast used was obtained from Meteorological and Oceanographic Satellite Data Archival System (MOSDAC) site and post infestation, identification of specific locations were done using multi-date IRS AWiFS data. It is an attempt for early detection through 3 days advance forewarning of weather which will be handy tool for planners to expedite relief measures in case of epidemic with a more focused zones of infestation as well as for crop insurers to know the location and extent of damage affected areas.     

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data

In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R² (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R² (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R² (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models.     

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach

In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 10⁴ for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely.     

一种海面风场反演的快速风矢量搜索算法

本文以SeaWinds散射计为例，从最大似然估计的海面风场反演方法出发，根据其目标函数的一般分布特征，提出并实现了一种快速风矢量搜索算法。然后采用美国SeaWinds散射计的部分LA实测数据对该搜索算法进行了验证，并与常规算法对同一实验数据的反演偏差及运行时间进行了对比分析，结果表明该算法在能够保证反演精度的前提下，在较大程度上提高了运行效率。     

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

Microwave response to broad leaf vegetation (Spinach) and vegetation covered moist soil for remote sensing

Soil moisture is one of the most important parameter which controls the growth of the vegetation. For accurate data and sufficient information to increase food production, remote sensing technique is highly useful. This paper deals with the bistatic microwave response of spinach and spinach covered soil moisture at various growth stages on X-band if the frequency spectrum. The microwave response of spinach in different stages of growth have been studied in terms of scattering co-efficient (σ^°). The look angle effect on σ^° is observed for like polarization i.e. (VV-and-HH) only. A linear regression analysis has been done between the vegetation covered soil moisture and scattering co-efficient. It provides an idea that VV-polarization is more sensitive than HH-polarizalion for vegetation covered soil moisture and best suitable look angle for observing vegetation covered soil moisture is less than 40°(θ<40°).     

CO2倍增下遥感-光合作物产量响应模型的研究与应用

由于卫星散射计探视雷达回波的各向异性的双调和性质,同时由于散射物理模型函数的非线性及信号中存在噪声,使得常规点方式风场反演中风向有多至4 个解的多解存在。给出了一种改进的场方式反演方法,利用该方法从卫星散射计测量的后向散射强度的数据中唯一反演出大尺度海洋风场。通过数值模拟和实际算例计算表明反演结果在风向、风速上都与真解是吻合的。从结果可以看到,所采用的改进的场方式反演方法对模拟数据或真实散射计探测海面风场的反演是成功的。     

一种修正的星载散射计反演海面风场的场方式反演算法

由于卫星散射计探视雷达回波的各向异性的双调和性质,同时由于散向散射物理模型函数的非线性及信号中存在噪声,使得常规点方式风场反演哕向有多至４个解的多解存在。给出了一种改进的场方式钣演方法,利用该人卫星散射计测量的后向散射强度的数据中唯一反演出大尺度海洋风场。通过数值模拟和实际算例计算表明反演过程在风向、风速上都与真解是吻合的。从结果可以看到,所采用的改进的场方式反演方法对模拟数据或真实散射计探测海面     

风能方向性参量遥感反演

目前可遥感反演的海上风能参量主要为平均风速和平均风功率密度,缺乏对风能方向性参量的反演。本文建立了以风向频率、风能密度方向分布为核心的风能方向性参量体系及相应的反演方法,使用2007年—2016年ASCAT星载散射计观测数据进行了反演实验,并利用海上现场观测数据对反演结果进行比较验证,通过理论分析和模拟实验对反演方法的数据量需求和误差传递进行了分析。结果表明,90%的反演结果通过了所有的同一性检验,验证了其有效性和准确性;风向频率和风能密度方向分布准确反演所需的最小数据量分别为350条和800条;遥感反演的风速风向数据的误差使得最终反演的风能方向性参量趋于离散,真实的风能方向分布越集中,对其影响越敏感。     

Interpretation and analysis of multispectral data for aricultural crop cover types-role of crop spectral response and crop sectral signature

Airborne multispectral data obtained over mono and multiple cropping systems of small farming agriculture was studied for two cropping seasons for a possible development of crop spectral signatures and to utilize such signatures for interpretation of multispectral data and for assessing agricultural potentials of a region. In multiple cropping system, the unique crop spectral response exhibited by crop species at specific growth stages facilitated interpretation and analysis of multispectral data with the knowledge of crop phenology. For resolving spectral confusion between crop species due to growtn stages of different crop species, temporal data were observed to be useful. Development and use of crop spectral sigrature for interpretation and analysis multispectral data related to mono cropping system were found to be relatively less complex and offer great promise because of minimum spectral confusion.