Deriving cropping system performance indices using remote sensing data and GIS

The cropping system approach is a holistic management of variant and invariant resources to optimize the food production. Various indices are used to assess and evaluate the efficiency and sustainability of the systems. These indices are generally computed from the data collected by traditional survey methods that are time consuming and non‐spatial. An attempt has been made to derive such indices using satellite remote sensing data for the state of West Bengal, India. Three indices—Multiple Cropping Index (MCI), Area Diversity Index (ADI) and Cultivated Land Utilization Index (CLUI)—were attempted. Multi‐date, multisensor data from Indian Remote Sensing Satellite (IRS) and Radarsat Synthetic Aperture Radar (SAR) were used to derive cropping pattern, crop rotation, and crop calendar. Crop type, acreage, rotation and crop duration were used as inputs to compute the indices at district and state level. The indices were categorized as high, medium and low to evaluate the performance of each of the 16 districts. The average MCI of the state derived was 140. At district level it varied from 104 to 177. The average ADI of state was 2.5 and varied from 1.5 to 5.0.     

Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh )India)

The area under wheat was estimated and a forecast of production made in a predominantly un-irrigated region (36 per cent irrigated wheal crop, geographical area 5-61 Mha) of Madhya Pradesh (India) using digital data from LISS-I (Linear Imaging Self Scanner) onboard Indian Remote Sensing Satellite (IRS-IB), for the crop season 1991-92. A stratified sampling approach based on 5 km by 5 km sample segments, 10 per cent sampling fraction in conjunction with supervised maximum likelihood (MXL) classification was used for wheat acreage estimation. Yield forecasts were based on an optimal combination of forecasts from two different methodologies, viz., wheat yield-spectral relationship and time series analysis using ARIMA (Auloregressive Integrated Moving Average) approach. In the former, a two-year (1989-90, 1990-91) pooled regression relating LISS-I derived Near Infrared/Red (NIR/R) radiance ratio to district wheat yields was developed and used to forecast wheat yields for the year 1991-92 based on classified wheat pixels. In the latter case, historical district-wise wheat yield data of 35 years was used to develop appropriate ARIMA models and used to forecast 1991-92 yields. The relative deviation of remotely-sensed-based forecasted production, acreage and yield from the post-harvest estimates released later by the State Department of Agriculture were — 15.8, — 1002 and — 601 per cent, respectively. The acreage and yield meet the accuracy of 85 per cent at 90 and 95 per cent confidence levels, respectively.     

Cropland inventory in the command area of Krishnarajasagar project using satellite data

Inter-state disputes on water utilization from the Cauvery river have necessitated an accurate and timely estimate of the irrigated crop area in each season to optimise the water supply through the canal system and to suggest a departure from the traditional system of protective kharif irrigation to productive rabi irrigation. An inventory of the cropland in the command area of Krishnarajasagar Project was made by visually interpreting satellite imagery. Satellite data showed a reduction of about 6 per cent in the irrigated area from 1973 to 1986. Effect of scale of imagery and percentage of boundary pixels on accuracy of estimated irrigated area was studied. 1:250000 scale was found to be adequate for this large command area. The accuracy of estimated irrigated crop area was within 5 per cent of the DES estimate for the entire command area. At taluk-level, total enumeration gave an overestimate of irrigated area by 13 per cent compared to an overestimation of about 18 per cent using multistage-proportionate-probability sampling with a grid size of 2-5 km by 2-5 km. The per hectare cost of estimating the crop land using satellite remote sensing was found to be two to five times cheaper than the conventional system. An attempt was also made to evaluate various vegetation indices derived from Landsat-TM for identifying irrigated croplands and IRS-1A/LISS-II data for identification of crop types. It was concluded, from the results of this study, that satellite remote sensing is an effective time saving techqniue for providing seasonal assessment of irrigated cropland in the command areas.     

基于卫星遥感预测作物成熟期的可行性分析

精准收获是精准农业的重要环节,首先分析了收获时间对作物产量与品质的影响,论述了作物成熟期监测的重要性,然后从气象统计模型、作物生长模型及遥感监测3个方面回顾了作物成熟期预测的研究进展。在此基础上,通过对目前主要作物成熟状态指示因子遥感监测研究进展的分析,认为在当前新型传感器不断涌现的条件下,利用卫星遥感预测大范围作物成熟期、制订收割顺序并指导农业生产的条件已经成熟。并指出研究面向遥感的作物成熟期指示因子及其变化规律,发展高精度的作物冠层叶绿素及水分含量的遥感估算方法,研究面向农田尺度动态监测的高时空分辨率数据集构建技术和多种模型的耦合将成为该领域未来的研究重点。     

Pre-harvest wheat yield and production estimation for the Punjab,India

Two different approaches to relate wheat yield with spectral indices derived from remotely-sensed data have been explored for the state of Punjab, India. In the study based on site-level approach yield obtained from crop-cutting sites was found to be linearly related to NIR/Red ratio derived from Landsat MSS data of corresponding sites in Ludhiana and Patiala districts of Punjab. Incorporation of agrometeorological data was also tried. Certain inherent limitations of the site-level approach led to the district-level studies which focused on the relation of district yields with corresponding average spectral indices derived from satellite sensors like Landsat MSS and lRS-LISS-i. Significant correlations were observed in all cases and the relation based on Landsat MSS/IRS LISS-I data was used for trial forecast of wheat yields for 1989–90 season. A comparison of remote-sensing based production forecast showed good agreement with the conventional estimate of Bureau of Economics and Statistics at state level although at district level, deviations were larger.     

基于遥感数据和作物生长模型的小麦变量施肥研究进展

就国内外基于遥感数据和作物生长模型在变量施肥技术的研究应用作了阐述, 提出了快速、无损农业测试技术将是精准变量农业和数字农业今后的发展方向, 对作物生长模型以及精准变量施肥技术的研究进展作了较系统的调查研究, 阐述了将遥感数据与作物生长模型进行数据同化, 实现以高产、优质、环保为目的农业生产的可行性。并结合我国国情提出了发展精准农业变量施肥技术所面临的困难和出路。     

Forecasting crop production using satellite-based vegetation health indices in Kansas,USA

This article shows the results of early crop yield prediction from remote-sensing data. The study was carried out in Kansas, USA. The methodology proposed allows the estimation of winter wheat (WW), sorghum and corn yields 3–4 months before harvest. The procedure uses the vegetation health (VH) indices (vegetation condition index (VCI) and temperature condition index (TCI)) computed for each pixel and week over a 21-year period (1985–2005) from the Advanced Very High Resolution Radiometer (AVHRR) data. Over this period, a strong correlation was found between crop yield and VH indices during the weather-related critical period of crop development, which controls much final crop productivity. The 3-month advanced yield forecasts were independently compared with official agricultural statistics, showing that the estimation errors for WW, sorghum and corn were 8%, 6% and 3%, respectively. Implementing the 3–4 months lead forecast in operational practice will aid farmers to mitigate weather vagaries using irrigation, diseases/insects control, application of fertilizers and so on during a growing season and will help decision-makers to regulate marketing strategies, import/export and price policies and address food security issues.     

Spring Maize Yield Estimation based on Radiation Use Efficiency and Multi-temporal Remotely Sensed Data

In order to develop highly accurate model for crop yield estimation,an approach of retrieving regional crop yield was studied by Radiation Use Efficiency (RUE) and remote sensing data,the Jifangzha irrigation is of Hetao irrigation district of Inner Mongolia Autonomous Region was selected as a research case.Based on this model,the difference of the Dry Matter Accumulation (DMA) between the maize’s different growing stages is made and a comparative analysis of the measured yields,and the predicted results based on this model.The results show that the DMA of the maize’s jointing stage is maximal,about 40% of total amount.The measured yield and the predicted ones based on the model has a greater correlation,the Correlation Coefficient was 0.853 and passed the reliability of 0.01.The difference of the DMA in different growing stages showed that the sensitive degree was displayed between main growing stages and yield,and that were some key aspects:the main limiting factor in growing stages and raising yield.These results indicate the model for spring maize yield estimation is feasible and effective based on the RUE and multi-temporal remotely sensing data.     

Correlation between potato yield and MODIS‐derived vegetation indices

Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health, as well as predicting its yield. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) calculated from remotely sensed data have been widely used to monitor crop growth and to predict crop yield. This study used 8 day TERRA MODIS reflectance data of 500 m resolution for the years 2005 to 2006 to estimate the yield of potato in the Munshiganj area of Bangladesh. The satellite data has been validated using ground truth data from fields of 50 farmers. Regression models are developed between VIs and field level potato yield for six administrative units of Munshiganj District. The yield prediction equations have high coefficients of correlation (R ²) and are 0.84, 0.72 and 0.80 for the NDVI, LAI and fPAR, respectively. These equations were validated by using data from 2006 to 2007 seasons and found that an average error of estimation is about 15% for the study region. It can be concluded that VIs derived from remote sensing can be an effective tool for early estimation of potato yield.     

Remote sensing of regional yield assessment of wheat in Haryana,India

Regional estimates of crop yield are critical for a wide range of applications, including agricultural land management and carbon cycle modelling. Remotely sensed images offer great potential in estimating crop extent and yield over large areas owing to their synoptic and repetitive coverage. Over the last few decades, the most commonly used yield–vegetation index relationship has been criticized because of its strong empirical character. Therefore, the present study was mainly focused on estimating regional wheat yield by remote sensing from the parametric Monteith's model, in an intensive agricultural region (Haryana state) in India. Discrimination and area estimates of wheat crop were achieved by spectral classification of image from AWiFS (Advanced Wide Field Sensor) on‐board the IRS‐P6 satellite. Remotely sensed estimates of the fraction of absorbed photosynthetically active radiation (fAPAR) and daily temperature were used as input to a simple model based on light‐use efficiency to estimate wheat yields at the pixel level. Major winter crops (wheat, mustard and sugarcane) were discriminated from single‐date AWiFS image with an accuracy of more than 80%. The estimates of wheat acreage from AWiFS had less than 5% relative deviation from official reports, which shows the potential of single‐date AWiFS image for estimating wheat acreage in Haryana. The physical range of yield estimates from satellites using Monteith's model was within reported yields of wheat for both methods of fAPAR, in an intensive irrigated wheat‐growing region. Comparison of satellite‐based and official estimates indicates errors in regional yields within 10% for 78% and 68% of cases with fAPAR_M1 and fAPAR_M2, respectively. However, wheat yields in general are over‐ and underestimated by the fAPAR_M1 and fAPAR_M2 methods, respectively. The validation with district level wheat yields revealed a root mean square error of 0.25 and 0.35 t ha^?1 from fAPAR_M1 and fAPAR_M2, respectively, which shows the better performance of the fAPAR_M1 method for estimating regional wheat yields. Future work should address improvement in crop identification and field‐scale yield estimation by integration of high and coarse resolution satellite sensor data.     

Use of remote sensing data for estimation of winter wheat yield in the United States

This paper shows the application of remote sensing data for estimating winter wheat yield in Kansas. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982–2004) from Advance Very High Resolution Radiometer (AVHRR) data. The weekly indices were correlated with the end of the season winter wheat (WW) yield. A strong correlation was found between winter wheat yield and VCI (characterizing moisture conditions) during the critical period of winter wheat development and productivity that occurs during April to May (weeks 16 to 23). Following the results of correlation analysis, the principal components regression (PCR) method was used to construct a model to predict yield as a function of the VCI computed for this period. The simulated results were compared with official agricultural statistics showing that the errors of the estimates of winter wheat yield are less than 8%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.     

Spring wheat-leaf phytomass and yield estimates from airborne scanner and hand-held radiometer measurements†

Our objective was to relate radiance measurements from a hand-held radiometer (Exotech 100-A) and an airborne muitispectral scanner (Daedalus DEI 1260) to different wheat (Triticum aestiuum L.) stand densities (simulated winter wheat winterkill) and to grain yield. The field experiment was located 11 km north-west of Sidney, Montana (47°45'N, 104°16'W) on a Williams loam soil (fine-loamy, mixed Typic Argiborolls). Three rates—67, 27 and 13 kg/ha—of ‘Len’, a semidwarf, hard red spring-wheat cultivar, were seeded to represent stands of 100, 40 and 20 per cent. Radiances were measured with a hand-held radiometer on clear mornings throughout the growing season. Aircraft overflight measurements were made at three growth stages: tillering, stem extension and heading period

The near-IR/red ratio was used in the.analysis. Both aircraft and ground measurements made it possible to differentiate and evaluate wheat stand densities at an early enough growth stage to make management decisions. The aircraft measurements also corroborated hand-held radiometer measurements when related to yield prediction. Although there was some growth dependency, the near-IR/red ratio correlated with yield when measured from just past tillering until about the watery-ripe stage. The results reinforce the potential of remote sensing for estimating grain yields and evaluating winterkill.     

An optimal estimation technique for increasing the accuracy of crop forecasts by combining remotely sensed and conventional forecast results.

Abstract

An optimal estimation (OE) technique has been used to increase the accuracy of crop acreage and yield estimates by combining results from remotely sensed (RS) data and conventional models. For crop acreage estimation the OE increased the accuracy of wheat acreage estimation when the first forecasts of the Directorate of Economics and Statistics (DES) were combined with state level RS estimates over the states of Haryana and Punjab in India.

To increase the accuracy of wheat yield forecasts an autoregressive (AR) model was developed. Results of AR model were optimally combined with RS-based estimates for Hisar and Karnal districts in Haryana, India. The OE results for a total of eight forecasts had a lower mean absolute per cent deviation than the forecasts using RS and AR approaches. The power of OE was further demonstrated by combining weather-based wheat yield model results for the state of Punjab (India) with first order AR model results, suggesting an increase in accuracy of forecasts by optimally combining results from two or more algorithms.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

Thermal infrared remote sensing of diurnal crop canopy temperature variations represents a possible method for determining the availability of soil water to plants. This study was performed to assess the effects of soil water and crop canopy on apparent temperatures observed by means of remote sensors, and to determine the impact of these effects on remote soil water monitoring. Airborne thermal scanner and apparent reflectance data (one date) and ground PRT-5 data (three dates) were collected primarily over barley and other small grain canopies. Plant heights, cover, and available soil water for four layers in the top 20 cm were determined. Analysis of the data showed a close inverse linear relationship between the available water and the day minus night temperature difference δT, for thick barley canopies (plant cover above 90 per cent) only. The use of apparent reflectance values in the visible region did not improve available soil water regression equations substantially. These results suggest that the available water or plant stress could only be accurately determined for thick canopies, and that the reflectance data could probably be used to identify such canopies but would not improve regression estimates of soil water from remote sensing data.     

TM遥感与地块内冬小麦产量变异

卫星遥感可以为农作物的准确管理提供必要,及时并具有空间连续性的信息,但高成本一直是限制该项技术在农业上深入发展的主要障碍,利用价格相对较为低廉的TM卫星影像作为信息源来评价其对估测小区域内作物产量空间变异并为规划管理单元提供必要信息的可行性做了初步的研究,结果表明,利用TM图像所获得的植被指数能较好地反映小麦各生育时期的基本特点,两种植被指数（NDVI及RVI）都表现出一定程序的空间,而且都以小麦抽穗后期的变异程度为最大,而且,小麦生长发育的三个重要时期（分蘖期,抽穗期及拔节期）的两种植被指数之间具有极显著相关关系,两个试验地块小麦11月8日的归一化植被指数都与产量表现出了良好的相关关系,另外,两种植被指数在表现作物千粒重和亩穗数等产量指标信息方面,也有一定的效果。     

A non-linear regression form for vegetation index-crop yield relation incorporating acquisition date normalization

A non-linear form relating vegetation indices (VI) to crop grain yields which normalizes for differences in acquisition date is suggested. It is based on the assumption that deviations in VI near the peak VI follow a quadratic behaviour. This form gave a higher R2 value than a simple VI-yield linear model on a multi-year, multi-location data set of IRS (Indian Remote Sensing Satellite-1A) LISS-I(Linear Imaging Self Scanner-I) derived near-infrared (NIR)/red radiance ratios and wheat grain yields in a study site in Madhya Pradesh (India). As the suggested model includes time of peak as a variable, it allows integration of results from other sources, such as, weather-based crop phenology model or high repetivity spectral data into the VI-yield relation.     

Using remotely sensed and ancillary data to predict spatial variability of rainfed crop yield

Rainfed agriculture is dominant in Sudan. The current methods for crop yield estimation are based on taking random cutting samples during harvesting time. This is ineffective in terms of cost of information and time. The general objective of this study is to highlight the potential role of remote-sensing techniques in upgrading methods of monitoring rainfed agricultural performance. The specific objective is to develop a relationship between satellite-derived crop data and yield of rainfed sorghum. The normalized difference vegetation index (NDVI), rainfall, air temperature (AT) and soil moisture (SM) are used as independent variables and yield as a dependent variable. To determine the uncertainty associated with the independent variables, a sensitivity analysis (SA) is conducted. Multiple models are developed using different combinations of data sets. The temporal images taken during sorghum’s mid-season growth stage give a better prediction than those taken during its development growth stage. Among predictor variables, SM is associated with the highest uncertainty.     

The relationship between ERS-2 SAR backscatter and soil moisture: Generalization from a humid to semi-arid transect

The sources of variation (environment, genotype and date of measurement) of spectral reflectance indices describing biomass and its physiological status, and their potential use for providing accurate and non‐destructive estimates of crop phenology and yield, were studied on canopies of several collections of durum wheat genotypes showing adaptation to different Mediterranean environments. Spectral reflectance was measured during grain filling. All spectral indices and grain yields showed significant differences between contrasting environments in terms of water availability. Photosynthetic area indices and senescence indices were good indicators, for all genotype collections, of biomass and phenology, respectively, when comparing a wetter site with a drier site. When crop development was accelerated by growing plants under high temperature, provided by a spring‐sown trial under Mediterranean conditions, all spectral indices showed significant variation within a period of one week through grain filling, reflecting the changes in crop phenology and the onset of senescence. The reported changes in the values, and even the signs, of the correlation coefficients across genotypes between grain yield and some reflectance indices might reflect genotypic differences in response (by avoidance) to high temperature and drought during late grain filling. Spectral reflectance data may help to understand phenological characteristics of durum wheat canopies, such as crop duration, provided the date of measurement is well chosen.     

Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model

Recent developments in unmanned aerial system (UAS) require an urgent introduction to monitoring technologies of crop diagnostic information because of their advantage in manoeuvering tasks at a high-spatial resolutions and low costs in a user-friendly manner. In this study, an advanced application method of an UAS remote sensing system was performed using the grid GRAMI-rice model such that it can be driven using weather and remote sensing data to monitor the spatiotemporal productivities of rice (Oryza sativa). Remotely sensed data for the model were supplied, along with normalized difference vegetation index images obtained using the UAS remote sensing system. The model was first evaluated using paddy data from experimental fields (treated with two nitrogen (N) applications) at Chonnam National University, Gwangju, Republic of Korea (ROK). Practical application was then performed using paddy data from farm fields under conventional farm management practices at the Gimje plain in ROK. The grid GRAMI-rice model statistically well reproduces the field conditions of spatiotemporal rice productivities, showing an acceptable statistical accuracy in the comparison of growth between the simulated and observed values, using a Nash–Sutcliffe efficiency range of 0.113–0.955. According to t-tests (α = 0.05), there were no significant differences between the simulated and observed grain yields from both the evaluation and practical applications. The scientific approach adopted here is unique, advanced, and practical, in a way that UAS remote sensing methods were effectively incorporated with crop modelling techniques. Therefore, it was concluded that the UAS-based remote sensing techniques proposed in this study could represent an innovative way of projecting reliable spatiotemporal crop productivities for precision agriculture.