Desertification has become one of the greatest environmental concerns of our planet. Implementation of the action plans for arresting land degradation and for employing rehabilitation measures over a large spatial scale is not feasible due to the amount of time, effort, and cost involved. However, if the ‘hotspots’ the ‘brightspots’, and the ‘potential areas’ are identified, the task would be relatively easy. In this paper, a method is proposed to identify the pieces of degraded land with varying severity levels (in terms of ‘hotspots’, ‘brightspots’, and ‘potential areas’), using Bowen ratio, land surface temperature (LST), Ra, and Normalized Difference Vegetation Index (NDVI). Although the zone falls in the semiarid class, the microclimate analysis of the study area revealed high aridity. The combined analysis of LST, Ra, and NDVI helped in identifying the areas susceptible to land degradation (particularly, salinization and water erosion). Analysis of the vegetation type and condition showed their variable roles towards the protection of soil from erosion, drought, and fire. Using these analyses together with the ecosystemic approach of Bowen ratio, ‘hotspots’, ‘brightspots’, and ‘potential areas’ were identified at the pixel level. For validation, Desertification Status Map was employed. The investigations revealed that around 49% of the study area falls under the category of ‘hotspots’ (with an error estimate of 13%) and another 49% as ‘brightspots’. The findings revealed that instead of targeting the entire area for implementation of the mitigation measures with the same efforts, it would be better to focus on the specific pieces of land (‘hotspots’) to optimally utilize the available resources.

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Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best (F>115) in discriminating above classes, while LCA and SINDRI were best (F> 100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100-2300 m region would be sufficient to discriminate crop residue from other co-existing classes.

Coastal waters, in particular, are the regions of high productivity and biodiversity. Detailed investigations of the variability within them can aid in understanding many biogeochemical processes. With the advent of hyperspectral remote sensing having large number of closely spaced channels and highly improved signal-to-noise ratio (SNR), the coastal applications are expected to increase and improve. In India, very less work is done in the field of coastal studies, let alone using hyperspectral remote sensing. HICO, onboard ISS, is the most recent addition to this family of instruments. So, a pilot study was conducted to assess HICO data for coastal studies especially in deriving the shallow water bathymetry estimates. The methodology for deriving bathymetry estimates is based on the different responses of shallow-water reflectance on depth and substrate type because with decreasing water depth in case 2 waters, the spectral contributions arriving from pure water reduce while from other OCAs increase. This variability is typically higher in the wavelength range 480 to 610nm. Using this wavelength range, bathymetric estimates were made at pixel level. Bathymetry estimates were found to vary from 1m to >12m. Spectral variability is clearly observed in the continuum removed spectral plots from waters of different depths and is reported in this paper.

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