N. R. Patel

Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status

High‐resolution soil moisture holds the key to improving weather forecast, drought monitoring and hydrological modelling. Therefore, the present study investigates the potential of the temperature/vegetation dryness index (TVDI) from the MODIS to assess soil moisture status in sub‐humid parts of India (western Uttar Pradesh). The TVDI was calculated by parameterizing the normalized difference vegetation index–surface temperature space from 8 day MODIS reflectance and surface temperature products. Correlation and regression analysis was carried out to relate the TVDI against in‐situ measured soil moisture during early (April) and peak (October) stages of growth in sugarcane crop. Spatio‐temporal patterns in the TVDI shows that northern areas had more surface wetness compared to southern areas. The results further reveal that a significantly strong and negative relationship exists between the TVDI and in‐situ soil moisture, particularly when vegetation cover is sparse. The dryness index was also found satisfactory to capture the temporal variation in the surface moisture status in terms of antecedent precipitation index.

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 Monteiths 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 Monteiths 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.

Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R2 = 0.65, whereas VWSI showed overestimated values with mean RD 4%.

Mapping a Specific Crop—A Temporal Approach for Sugarcane Ratoon

Mapping a specific crop using single date multi-spectral imagery remains a challenging task because vegetation spectral responses are considerably similar. The use of multi-temporal images helps to discriminate specific crops as the classifier can make use of the uniqueness in the temporal evolution of the spectral responses of the different vegetated classes. However, one major concern in multi-temporal studies is the selection of optimum dates for the discrimination of crops as the use of all available temporal dates can be counterproductive. In this study this concern was addressed by selecting the best 2, 3, 4… combinations dates. This was done by conducting a separability analysis between the spectral response of the class of interest (here, sugarcane-ratoon) and non-interest classes. For this analysis, we used time series LISS-III and AWiFS sensors data that were classified using Possibilistic c-Means (PCM). This fuzzy classifier can extract single class sub-pixel information. The end result of this study was the detection of best (optimum) temporal dates for discriminating a specific crop, sugarcane-ratoon. An accuracy of 92.8xa0% was achieved for extracting ratoon crop using AWiFS data whereas the optimum temporal LISS-III data provided a least entropy of 0.437. Such information can be used by agricultural department in selecting an optimum number of strategically placed temporal images in the crop growing season for discriminating the specific crop accurately.

Upscaling of leaf area index in Terai forest plantations using fine-and moderate-resolution satellite data

Ecophysiological variables, such as leaf area index (LAI), play a key role in the functioning of ecosystem processes and are thus a useful determinant of primary production, evapotranspiration, and biogeochemical cycling. In the present study, upscaling of LAI was carried out by applying a transfer function from field LAI measurements to fine-resolution LISS III images and subsequently to coarse resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data using a photosynthetically active radiation (PAR)/LAI ceptometer (AccuPAR model LP-80). Field data were collected from differently aged forest plantation types in the Terai Central Forest Division of Nainital district in Uttarakhand, India. The upscaling was done by establishing an empirical exponential relationship between normalized difference vegetation index (NDVI) and LAI. Results reveal a significant relationship (p < 0.01) between NDVI and LAI for each of the studied plantation types, i.e. teak, poplar, eucalyptus, and mixed plantation. The LAI was mapped at 23.5 m resolution by applying a plantation-specific LAI versus NDVI relationship derived from IRS Linear Imaging Self-Scanning Sensor images. The LAI maps were upscaled by using a simple linear averaging within nonoverlapping windows to match with Terra–MODIS 250 m resolution NDVI images. The upscaled time series of LAI was compared with representative field-measured LAI measurements and also with MODIS LAI at a resolution of 1000 m. Upscaled LAI was found to be significantly related to the field-measured LAI with a value of 0.69 for coefficient of determination and with a root mean square error of 0.77. On the other hand, upscaled LAI has less agreement with the MODIS LAI product (R2 = 0.5 and root mean square error = 0.70).

Mapping specific crop- A multi sensor temporal approach

This study explores the applicability of temporal and multi sensor data for specific crop mapping. For this, temporal data from a single sensor (LISS III from IRS- P6 satellite) was used and classified after selecting the best dates for mapping. In the second case a Landsat- 5 TM image (other sensor/ multi sensor approach) is added to the selected best LISS III temporal dates combination and classified again for evaluating the effect of the addition of a another sensor data (i.e. Landsat- 5 TM) on the overall accuracy of classification. A Possibilistic c-Means (PCM) classification technique has been used for extracting single class of interest (Sugarcane-ratoon) and for including the mixed pixels occurring in the heterogeneous landscape of the study area. In the absence of reference data, evaluation of the soft (fuzzy) classified outputs was done as an entropy measurement, where entropy provides an indirect absolute measurement of the classification accuracy in the form of an uncertainty measure.

Estimating Net Primary Productivity in Tropical Forest Plantations in India Using Satellite-Driven Ecosystem Model

Abstract Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon and source-sink nature of the ecosystem. This study was carried out in a forest plantation area and aimed to (i) estimate the spatio-temporal patterns of NPP during 2009 and 2010 using Carnegie-Ames-Stanford Approach [CASA] model and (ii) study the effects of climate variables on the NPP using generalized linear modelling (GLM) approach. The total annual NPP varied from 157.21 to 1030.89 gC m−2 yr−1 for the year 2009 and from 154.36 to 1124.85 g C m−2 yr−1 for the year 2010. The annual NPP was assessed across four major plantation types, where maximum NPP gain (106 and 139 g C m−2 yr−1 ) in October was noticed in teak (Tectona grandis) and minimum (77 and 109 g C m−2 yr−1 ) in eucalyptus (Eucalyptus hybrid) during 2009 and 2010.The validation, using field-estimated NPP, showed under-estimation of modelled NPP, with maximum MAPE of 34% for eucalyptus and minimum of 13% for teak. The dominant influence of precipitation on the NPP was revealed by GLM explaining more than 20% of variation. CASA model efficiently estimated the annual NPP of plantations. The accuracy could be improved further with inclusion of higher resolution data.

Assessment of large aperture scintillometry for large-area surface energy fluxes over an irrigated cropland in north India

Amount of available net energy and its partitioning into sensible, latent and soil heat fluxes over an agricultural landscape are critical to improve estimation of evapotranspiration and modelling parse (ecosystem modelling, hydrological and meteorological modelling). Scintillometry is a peculiar and robust methodology to provide structure parameter of refractive index and energy balance. Scintillometer has proven for assessment of sensible and latent heat flux, which is based on the principle of Monin–Obukhov similarity theory. Scintillometer has been installed in the agricultural experimental farm of ICAR-Indian Agricultural Research Institute, New Delhi, with a spatial covering path length of 990xa0m of irrigated and cultivable agricultural landscape. This paper discusses the patterns of energy flux as diurnal and seasonal basis at scintillometer path which was mainly covered by maize in Kharif and wheat in Rabi season during a crop growing seasons of 2014–2015. The biophysical parameters (leaf area, soil moisture, crop height) were recorded at a temporal resolution of fortnight basis along the path length at usual sampling distance. The Bowen ratio value for both Kharif and Rabi season was 0.76 and 0.88, respectively by scintillometer. Leaf area index had a significantly positive correlation with latent heat flux (