Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 58

Five RS indices, namely normalized difference vegetation index (NDVI), surface temperature (T_s), water stress index (WSI), absorbed photosynthetically active radiation (APAR), and averaged yield for the last 5 years, were selected. The first four parameters were retrieved from Landsat 8 remote sensing images. The average yield is calculated from statistical and ground truth data.

The spectral information from free available high-resolution optical Landsat 8 satellite images is used in the present study. Several researchers reported that estimates from Landsat were considerably more accurate in yield estimates and its variability during growth stages [60, 61]. The Landsat 8 level 1 images were downloaded from USGS Earth explorer. Digital numbers are changed to TOA reflectance data. The developed indices are as follows.

The NDVI is most important and efficient index of crop growing conditions [62, 63], which is the response index to greenness and vegetative cover High NDVI values that reflect greater greenness, similarly, low NDVI values reflect too stress or senescence and low vegetation. It is the normalized difference between the near infrared (NIR) and visible RED (R) reflectance bands.

(2.1)

The next important parameter is solar radiation. The amount of light available for photosynthesis is known as photosynthetically active radiation (PAR) and ranges between 400 and 700 nanometers. Absorbed photo synthetically active radiation is the portion absorbed for photosynthesis by crop leaves.

(2.2)

Fraction of absorbed PAR (FAPAR) is related to absorbed PAR and can be used in the estimation of light use efficiency to estimate crop yields at the pixel level [64]. A linear, scale-invariant relationship between FAPAR and the NDVI was suggested by earlier scientists [65–67]. In the present study, computed FAPAR using NDVI, as suggested by [65] for Landsat images, is adopted in this study as:

(2.3)

Canopy surface temperature represents sunlight radiated onto leaves, and also, it is an indication of evaporation intensity. Surface temperature is calculated as

(2.4)

where, BT = Top of atmosphere brightness temperature (°C)

W = Wavelength of emitted radiance

ε = Land Surface Emissivity

Spectral radiance data were converted to top of atmosphere (TOA) brightness temperature by using the thermal constant (K₁ and K₂) values in Meta data file

(2.5)

where BT = Top of atmosphere brightness temperature (°C), L_λ = Top of atmospherespectral radiance (Watts/(m² * sr * μm)), K₁ = K₁ Band Constant, K₂ = K₂ Band Constant.

Crop Water Stress Index (NDVI/T_s) is taken as one of the indicators of crop yield. The water stress index (WSI or CWSI) quantifies moisture stress and relationship between plant temperature and stress [68]. Crop Water Stress Index was used for moisture deficit monitoring with best results and revealed that there was a close relationship in between WSI and crop water content [69].

The crop yield data (crop cutting data) are collected for 5 years. Five years of data are sufficient for estimates of yield potential for fully irrigated production systems [70] and is adopted for the present study. The crop yield per unit area of different crops for the years 2013 to 2017 is collected from Directorate of Economics and Statistics, Vijayawada. The location details of the data collected is in the form of survey numbers of revenue department. To know latitude and longitude and also to collect previous years’ data at each crop cutting point ground truth is done by using EpiCollect (A mobile based App) (Figure 2.3).

Figure 2.3 Illustration of collection of ground sample points using EpiCollect app (original figure).