Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in Kenya

Remote‐sensing data acquired by satellite have a wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near‐infrared remote‐sensing data have been extensively used for crop characterization, biomass estimation, and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant‐water stress. This paper reports the development of an operational spectro‐agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOT‐VEGETATION, meteorological data obtained from the European Centre for Medium‐Range Weather Forecast (ECMWF) model, and crop‐water status indicators estimated by the Crop‐Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted, and production were used in the model. The statistical multiple regression linear model has been developed for six large maize‐growing provinces in Kenya. The spectro‐agrometerological yield model was validated by comparing the predicted province‐level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model, the actual evapotranspiration (ETA) performs best. CNDVI and ETA in the model explain 83% of the maize crop yield variance with a root square mean error (RMSE) of 0.3298 t ha^?1. Very encouraging results were obtained when the Jack‐knife re‐sampling technique was applied, thus proving the validity of the forecast capability of the model (r ² = 0.81 and RMSE = 0.359 t ha^?1). The optimal prediction capability of the independent variables is 20 days and 30 days for the short and long maize crop cycles, respectively. The national maize production during the first crop season for the years 1998–2003 was estimated with an RMSE of 185 060 t and coefficient of variation of 9%.     

Spectro-meteorological modelling of sorghum yield using single date IRS LISS-I and rainfall distribution data

The yield of grain Sorghum cultivated in dry-land regions in India fluctuates widely in relation to its critical growth phases depending on the weather conditions. Vegetation indices derived form remote sensing data acquired at the time of maximum vegetative growth are indicative of crop growth and vigour and consequent potential grain yields. In this paper we investigate rabi (winter) sorghum yields using Indian Remote Sensing Satellite's Linear Imaging and Self Scanning-I (IRS LISS-I) sensor data and monthly rainfall distribution data of the recent four seasons for the 37 tehsils (sub-units of districts) that constitute the three principal sorghum producing districts of the central Maharashtra state (India). The multiple linear regression yield models with both the spectral and spectro-meteorological parameters have been developed for tehsil, as well as the district yields, by identifying critical parameters with model estimation errors of about 22 per cent on tehsil yields and about 5 per cent on district yields. The yields are found to be correlated significantly with monsoon rainfall about 1 to 2 months before sowing. This study brings out the problems of yield modelling of the semi-arid tropical crop in a small region using remote sensing data only, and shows that the vegetation indices deduced from remote sensing data are found to be good indicators of the yield on large spatial scales, as the effects of varying rainfall on yields largely cancel out.     

Yield prediction and waterlogging assessment for tea plantation land using satellite image‐based techniques

Motivated by the operational use of remote sensing in various agricultural crop studies, this study evaluates the application and utility of remote sensing‐based techniques in yield prediction and waterlogging assessment of tea plantation land in the Assam State of India. The potential of widely used vegetation indices like NDVI and SR (simple ratio) and the recently proposed TVI has been evaluated for the prediction of green leaf tea yield and made tea yield based on image‐derived leaf area index (LAI), along with weather parameters. It was observed that the yield model based on the TVI showed the highest correlation (R² = 0.83) with green leaf tea yield. The NDVI‐ and SR‐based models suffered non‐responsiveness when the yield approached maximum. The NDVI and SR showed saturation when the LAI exceeded a magnitude of 4. However, the TVI responded well, even when the LAI exceeded 5, and thus has potential use in the estimation of the LAI of dense vegetation such as some crops and forest where it generally exceeds the threshold value of 4.

An attempt was made for the innovative application of TCT and NDWI in the mapping of waterlogging in tea plantation land. The NDWI in conjunction with TCT offered fairly good accuracy (87%) in the delineation of tea areas prone to waterlogging. This observation indicates the potential of NDWI and TCT in mapping waterlogged areas where the soil has considerable vegetation cover.     

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.     

Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

Results from an approach to infer surface soil moisture from time series analysis of surface wetness index derived using the Special Sensor Microwave/Imager (SSM/I) are presented. Soil moisture quantification was based on the study of temporal changes in surface wetness index and its scaling to maximum and air‐dry limits of soil in each grid cell (0.33°). The estimated soil moisture of Illinois, USA was compared with field measured soil moisture (0–10?cm) obtained from the Global Soil Moisture Data Bank. A root mean square error of 7.18% was found between estimated and measured volumetric soil moisture. A consistency in soil moisture and rainfall pattern was found in the un‐irrigated areas of northern India (Jodhpur, Varanasi) and southern India (Madurai), influenced by southwest and northeast monsoons, respectively. Soil moisture of more than 0.30 m³m^?3 was observed in the absence of rainfall due to the irrigation of rice crop in (Punjab) during the pre‐southwest monsoon period (May).     

Predicting aphid (Lipaphis erysimi) growth in oilseed Brassica using near surface meteorological data from NOAA TOVS—a case study

Pest infestation in crops is highly influenced by agrometeorological parameters. Weather based early warning of pest infestation is being practised using statistical and dynamic simulation models on point scale. Satellite based inputs and epidemiological models can extend the application to areas with irregular and non‐existing ground observations. The present study describes the use of National Oceanic and Atmospheric Administration TIROS (Television and InfraRed Operational Satellites) Operational Vertical Sounder (TOVS) near surface air temperature at 1430 h Local Apparent Time (LAT) over India (0900 Universal Time Code) for modelling onset, build‐up and peak aphid (Lipaphis erysimi) infestation on Indian Mustard (Brassica juncea) crop over Bharatpur and Kalyani, falling in semi‐arid and sub‐humid regions, respectively. The daily cumulative TOVS air temperature from 1 October 2001 showed high correlation (R ²: 0.99) with observed datasets. Exponential relationships were found to be the best empirical fit between TOVS cumulative air temperature (CAT_TOVS) and crop age at aphid onset (R ²: 0.7–0.99) and peak infestation (R ²: 0.91–0.95) for two stations representing semi‐arid (Bharatpur) and sub‐humid (Kalyani) agroclimatic conditions. Second order polynomial fits were found (R ²: 0.81–0.85) at both the stations between peak aphid count and CAT_TOVS at peak. Estimates of intermediate linear aphid build‐up to peak, computed using location‐specific linear growth rate (LGR) showed a higher standard error (SE) of 20% of mean at Kalyani (0–99), compared to 8% at Bharatpur (4–58). The common prediction models on linear start and peak were developed using TOVS noon time specific humidity (SPH) weighted thermal units and sowing dates. The standard error (SE) of estimated intermediate aphid‐build‐up became less: 4.5% of observed mean counts for pooled datasets with a common model, irrespective of sowing dates. The correlation between estimates and observations was 71%. The common model will be useful for general application in the absence of availability of local models.     

Gain of the AVHRR visible channel as tracked using bidirectional reflectance of Antarctic and Greenland snow

On a 1984-1989 series of ARTEMIS-NDVI data derived from the NOAA-AVHRR sensor a case study on crop monitoring and early crop yield forecasting was elaborated for the provinces of Burkina Faso. In order to remove residual effects of clouds and other atmospheric influences on 10-day maximum NDVI images, a conditional temporal interpolation method was applied. Various NDVI regression parameters were compared. For the seven northern provinces, a simple linear regression based on averaged maximum 10-daily or monthly NDVI values proved to be superior to regressions based on the integrated NDVt and on NDVI increments. Multiple regressions led to significantly higher correlation coefficients, but only towards the end of the growing season (up to r² = 087). The simple linear regression was also found valid for a part of the central and southern provinces. The yields of the majority of the provinces however was best approximated using one second-order polynomial equation. A test of the regressions on 1989 data showed a forecast error percentage of less than 15 per cent for half of the 30 provinces in August, approximately 2 months before harvest. In the other half of the provinces, high forecast errors occurred mainly due to a locust invasion, excessive rainfall in August and drought in September, after the time of the forecast. Therefore correction factors for the occurrence of extreme pest and other problems have to be included in the model in close cooperation with the relevant organizations. Some of these problems could however be assessed indirectly from the NDVI dynamics.     

Estimation and validation of LAI using physical and semi‐empirical BRDF models

This letter deals with estimation of LAI for a wheat crop using physical and semi‐empirical BRDF models and IRS‐1D LISS‐III sensor data. NDVI was computed for both the models with LAI as a free parameter. The model‐computed NDVI was compared with corresponding atmospherically corrected LISS‐III NDVI. The estimation of LAI was carried out on the basis of a look‐up table approach and minimum root mean squared deviation between model computed and observed NDVI. The estimated LAI was validated against field measurements carried out during the months of February and March 2003, at the Central State Farm, Rajasthan, India. It was found that LAI was underestimated in both physical and semi‐empirical models. Results show that inclusion of multiple scattering in physical models may not always lead to a more accurate estimation of LAI and that it may be possible to estimate LAI at early stages of crop growth using semi‐empirical models. The coefficient of determination (R ²) between model estimated and measured LAI was 0.57 (standard error of estimate (SE) 0.156) and 0.63 (SE 0.187) for semi‐empirical and physical models, respectively, in the single scattering approximation, for February data. The corresponding values for March data were 0.57 (SE 0.206) and 0.51 (SE 0.216), respectively.     

Modelling rainfall intensity from NOAA AVHRR data for operational flood forecasting in Malaysia

Many empirical studies in numerical weather prediction have been carried out that establish the relationship between top‐of‐the‐cloud brightness temperature and rainfall particularly in tropical and equatorial regions of the world. Malaysia is a tropical country that lies along the path of the north‐east and south‐west monsoon rainfall, which sometimes causes extensive flood disasters. Observations have generally shown that heavy cumulonimbus cloud formation and thunderstorms precede the usual heavy monsoon rains that cause flood disasters in the region. In this study, a model has been developed to process National Oceanic & Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) satellite data for rainfall intensity in an attempt to improve quantitative precipitation forecasting (QPF) as input to operational hydro‐meteorological flood early warning. The thermal bands in the multispectral AVHRR data were processed for brightness temperature. Data were further processed to determine cloud height and classification performed to delineate clouds in three broad classes of low, middle, and high. A rainfall intensity of 3–12 mm h^?1 was assigned to the 1‐D cloud model to determine the maximum rain rate as a function of maximum cloud height and minimum cloud model temperature at a threshold level of 235 K. The result of establishing the rainfall intensity based on top of the cloud brightness temperature was very promising. It also showed a good areal coverage that delineated areas likely to receive intense rainfall on a regional scale. With a spatial resolution of 1.1 km, data are course but provide a good coverage for an average river catchment/basin. This raises the opportunity of simulating rainfall runoff for the river catchment through the coupling of a suitable hydro‐dynamic model and GIS to provide early warning prior to the actual rainfall event.     

Correction to ‘A note on the discrete Lyapunov and Riccati matrix equations’

In Tran and Sawan (1984), we derived a lower bound for the determinant of the discrete algebraic Riccati equation where A, B, P, Q are n x n matrices, Q = Q ^T > 0 and Rank (B) = n. We assumed that BB ^T>Q and the matrix A is stable. The purpose of this note is to include an additional assumption about the above equation in order for the result of Theorem 1 given in Tran and Sawan (1984) to be valid. Without the additional assumption, this theorem would be invalid as has been pointed out by Kwon and Youn (1985). The additional assumption is given below with the same notation as in Tran and Sawan (1984).     

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.     

Comparison of neural network configurations in the long-range forecast of southwest monsoon rainfall over India

The accurate long-range forecast of southwest rainfall can have manifold benefits for the country, from disaster mitigation and town planning to crop planning and power generation. In this paper, the rainfall has been modeled using artificial neural network (ANN) with different network configurations. Performance of these networks are compared with some results found in the literature. The networks have also been tested for the data outside the range of the trained data and compared with known results. The present network is found to be better in term of predictions than the previous results by others. Southwest monsoon rainfall over India for 6 years in advance has been predicted.     

Determination of seasonal and interannual variation in New Zealand pasture growth from NOAA-7 data

The normalized vegetation index (NVI) has been calculated from afternoon overpasses of NOAA-7 for two important farming regions of New Zealand, approximately 1000 k² in area, for the period from October 1981 through June 1984. The uniform nature of the terrain and farming practices in these areas make them ideal targets for remote sensing from satellites with limited spatial resolution. The frequency of useful data coverage has been increased by sampling within cloud-free parts of a partly cloudy target area and also by deriving an empirical correction for off-nadir view angles. Daily area-mean rainfall and soil moisture were estimated for both regions and monthly area-mean pasture growth for one of them. The NVI was found to reflect the varying rainfall and soil moisture on time scales of one week or more during the growing season and between years. A correlation of 0.81 was found between NVI and pasture growth on a monthly mean basis. These results suggest that operational satellite monitoring of these and other areas would provide valuable assistance in agricultural management and forward planning.     

Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta,Vietnam

Predicting rice crop yield at the regional scale is important for production estimates that ensure food security for a country. This study aimed to develop an approach for rice crop yield prediction in the Vietnamese Mekong Delta using the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and leaf area index (LAI). Data processing consisted of four main steps: (1) constructing time-series vegetation indices, (2) noise filtering of time-series data using the empirical mode decomposition (EMD), (3) establishment of crop yield models, and (4) model validation. The results indicated that the quadratic model using two variables (EVI and LAI) produced more accurate results than other models (i.e. linear, interaction, pure quadratic, and quadratic with a single variable). The highest correlation coefficients obtained at the ripening period for the spring–winter and autumn–summer crops were 0.70 and 0.74, respectively. The robustness of the established models was evaluated by comparisons between the predicted yields and crop yield statistics for 10 sampling districts in 2006 and 2007. The comparisons revealed satisfactory results for both years, especially for the spring–winter crop. In 2006, the root mean squared error (RMSE), mean absolute error (MAE), and mean bias error (MBE) for the spring–winter crop were 10.18%, 8.44% and 0.9%, respectively, while the values for the autumn–summer crop were 17.65%, 14.06%, and 3.52%, respectively. In 2007, the spring–winter crop also yielded better results (RMSE = 10.56%, MAE = 9.14%, MBE = 3.68%) compared with the autumn–summer crop (RMSE = 17%, MAE = 12.69%, MBE = 2.31%). This study demonstrates the merit of using MODIS data for regional rice crop yield prediction in the Mekong Delta before the harvest period. The methods used in this study could be transferable to other regions around the world.     

The impact of assimilation of satellite derived wind observations for the prediction of a monsoon depression over India using a mesoscale model

The Penn State/NCAR mesoscale model (MM5) has been used in this study to ingest and assimilate the INSAT‐CMV (Indian National Satellite System‐Cloud Motion Vector) wind observations using analysis nudging (four‐dimensional data assimilation, FDDA) to improve the prediction of a monsoon depression which occurred over the Bay of Bengal, India during 28 July 2005 to 31 July 2005. To determine the impact of assimilation of INSAT‐CMV winds on the prediction of a monsoon depression, three sets of numerical experiments (NOFDDA, FDDA and FDDA CMV) were designed. While the FDDA CMV run assimilated satellite derived winds only, the FDDA run assimilated both satellite and conventional observations. The NOFDDA run used neither satellite nor conventional observations. The results of the study indicate that the simulated sea level pressure field from the FDDA run is more consistent with the sea level pressure field from NCEP‐FNL compared to the FDDA CMV and NOFDDA runs. The highest correlation and lowest rms error of the sea level pressure field are associated with the FDDA run, and this provides a quantitative verification of the improvement due to the assimilation of satellite derived winds and the conventional upper air observations for the prediction of monsoon depression. All the three model simulated winds are in good agreement with the analysis winds at 850 hPa, 500 hPa and 200 hPa levels. The simulated structure of the spatial precipitation pattern for the assimilation experiments (FDDA and FDDA CMV) are closer to the TRMM observations with more rainfall simulated over the east coast regions in the assimilation experiments. The rms errors of the wind speed for the FDDA run show lower values at 500 hPa for all the three model runs, with a reduction in all three levels of up to 0.8–1.4 m s^?1 for the FDDA run and 0.5–1.9 m s^?1 for the FDDA CMV run with respect to the NOFDDA run. The statistical significance of the sea level pressure and the precipitation differences between the FDDA and the NOFDDA as well as the differences between the FDDA CMV and the NOFDDA have been calculated using the two‐tailed Student's t‐test and were found to be statistically significant. The influence of varying the nudging coefficients in the FDDA experiment has been studied.     

Methodology to classify rice cultural types based on water regimes using multi-temporal RADARSAT-1 data

This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R ², value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice.     

Evaluation and assimilation of various satellite-derived rainfall products over India

Accurate prediction of rainfall from the numerical weather prediction model is one of the major objectives over tropical regions. In this study, four different satellite-derived rainfall products (viz. merged-rainfall product from TRMM (Tropical Rainfall Measuring Mission) 3B42 and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)), and Indian meteorological satellite INSAT-3D retrieved HEM (Hydro-Estimator Method) and IMSRA (INSAT Multi-Spectral Rainfall Algorithm) rainfall) are assimilated in the Weather Research and Forecasting (WRF) model using variational method. Before assimilation of satellite retrieved rainfall product in the WRF model, selected rainfall products are compared with ground rainfall from India Meteorological Department during Indian summer monsoon (June–September) 2015. Preliminary validation results show root-mean-square-difference (mean difference) of 18.1 (2.1), 21.3 (2.1), 15.4 (?0.72), and 14.4 (0.5) mm day^?1 in IMSRA, HEM, IMERG, and TRMM 3B42 rainfall, respectively. Further, the four-dimensional variational data assimilation method is used daily to assimilate selected rainfall products in the WRF model during the entire month of August 2015. Results suggest that assimilation of satellite rainfall improved the WRF model analyses and subsequent temperature and moisture forecasts. Moreover, rainfall prediction is also improved with the maximum positive impact from TRMM rainfall assimilation followed by IMERG rainfall assimilation. Similar nature of improvements is also seen in rainfall prediction when INSAT-3D retrieved rainfall products (HEM and IMSRA) are used for assimilation.     

A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data

The inflection point of spectral reflectance of crop in the red edge region (680–780 nm) is termed as the red edge position (REP), which is sensitive to crop biochemical and biophysical parameters. We propose a technique for automatic detection of four dynamic wavebands, i.e. two in the far-red and two in the near-infrared (NIR) region from hyperspectral data, for REP estimation using the linear extrapolation method. A field experiment was conducted at the SHIATS Farm, Allahabad, India, with four levels of nitrogen and irrigation treatments to assess the sensitivity of REP towards crop stress. A correlation analysis was carried out between REPs and different biophysical parameters, such as leaf area index (LAI) and chlorophyll content index (CCI), recorded in each plot at 50, 70, and 90 days after sowing of wheat crop under the field experiment. The inter-comparison among different REP extraction techniques revealed that the proposed technique, i.e. the modified linear extrapolation (MLE) method, has a better ability to distinguish different crop stress conditions. REPs extracted using the MLE technique showed high correlations with a wide range of LAI, CCI, and LAI × CCI, being comparable with results obtained using the traditional linear extrapolation and polynomial fitting techniques. The behaviour of the new techniques was found to be stable at both narrower and broader bandwidth, i.e. 2 and 10 nm. A new red-edge-based index, i.e. area under REP (A_REP), was used to detect the cumulative stress over wheat crop by utilizing the REP and its rate of change information at different crop growth stages. A high coefficient of determination (R² = 0.89) was found between A_REP and dry grain yield (Q ha^?1) up to 50 Q ha^?1 of wheat crop, whereas, beyond this range the relationship was found to be diminishing.     

Technical Note: Mapping and forecasting of North Indian winter fog: an application of spatial technologies

In India, the Indo‐Gangetic plain (part of Northern India) is invariably affected by dense fog in the winter months every year due to typical meteorological, environmental and prevailing terrain conditions. Pollution also plays an important role in the formation of fog (smoke+fog = smog) in India. Using National Oceanic and Space Administration‐advanced very high resolution radiometer data the fog‐affected regions in Northern India were delineated and the spatial extent of fog for the winter months of the years 2002–03, 2003–04 and 2004–05 (December–February) were studied and mapped. Forecast for future fog based on the analysis of satellite and meteorological (air temperature, relative humidity and wind speed) data was also done. The fog‐affected areas were classified into maximum‐fog‐affected area, moderately fog‐affected area and least fog‐affected area. It has been found that in the winter months of the years 2002–03, 2003–04 and 2004–05, the fog‐affected area in Northern India was about 867 000 km², 625 000 km² and 706 800 km² respectively. The maximum fog‐affected area was found to be 606 400 km², the moderately fog‐affected area was found to be 230 400 km² and the least fog‐affected area was found to be 404 500 km². Further, based on meteorological parameters, such as temperature, humidity and wind speed along with elevation data was used to derive an approach for future fog prediction in this region.     

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.