**4. Crop condition assessment**

The use of remote sensing to assess plant health through spectral information can be beneficial in agriculture. It is possible to detect stress in plants by using remote

sensing techniques due to physiological changes caused by stress [19]. Monitor crop growth at regular intervals in order to take appropriate actions and also to determine whether any stress factor is likely to affect production. A variety of factors contribute

### **Figure 2.**

*A diagram of a hypothetical 12-month NDVI multi-temporal vegetation response curve for native vegetation that is typical of the Great Plains region, USA. Shown on the graph are selected vegetation phenology metrics that can be extracted through the analysis of the NDVI, near cloud-free datasets (adopted from Reed et al. [22]).*


### **Table 2.**

*Vegetation phenology metrics characterize vegetation phenology and are used to develop summary regional data for research on agro-ecosystem attributes (after Reed et al. [22]).*

to the growth stages and development of crops, including soil moisture, date of planting, air temperature, and day length. The conditions and productivity of plants are influenced by these factors. Too high temperatures at pollination, for example, can negatively impact corn crop yields. Forecasters may be able to better predict corn yields if they know the temperature when pollination occurs [20]. Siddiqui [21] explains that drought makes land inhospitable to humans, livestock, biomass potentials, and plant species, and also causes the land to be incapable of cultivation. Drought monitoring through satellite data has been widely accepted now and the Vegetation Condition Index (VCI) and Normalized Difference Vegetation Index (NDVI) are widely used to identify agricultural drought in regions with different ecological conditions. Many vegetation indices are used to measure crop growth and condition, such as reflectance ratios, NDVIs, PVIs, transformed vegetation indexes, and greenness indices. With operational remote sensing, NDVI profiles are extracted each year for 12 Vegetation Phenology Metrics (VPMs), and these metrics are used to analyze agricultural vegetation changes due to changes in climate and land management practices (**Figure 2** and **Table 2**) [22].
