Remote Sensing for Estimating Crop Area

Crop Area and Crop Yield are the two main required parameters for the crop information. Crop area estimation are assumed to be simply easier when the two parameters are compared, while this is usually not true. Different factors significantly impact the crop area estimation and making the process relatively arduous; such as field size, different cropping system, differences in phenology, and sometimes due to damage from weather and pests, while crop area estimation are to be taken at multiple growth stages of the crops. 

The notable review of the crop area estimation comes from Crag and Atkinson (2013): crop area estimated is done by complete inventory of all the farms or by the samples. The sampling can be Area Frame Sampling (AFS) or farm list sampling or combination of both. In all cases, experts opinions are usually seeked. 

The traditional estimation measures, as mentioned above, are time-taking, involves high-costs, difficults in its ways, and human errors are likely. For overcoming these, satellite remote sensing has been used directly or partially to support area sampling. Satellite remote sensing provides different images of the land use and helps in different usages for crops.

The history of satellite based remote sensing for crop area estimation started from the early 1970s when Corn Blight Watch Experiment was carried out by USDA, NASA and involvement from different univeristies. ERTS-A was lanched in 1972 which studied the applicability of multispectral remote sensing technology: Crop Identification Technology Assessment for Remote Sensing (CITARS) and Large Area Crop Inventory Experiment (LACIE) were conducted for the study of capabilites of remote sensing in the crops contexts. LACIE was sponsored by United States of America and relied on Landsat data to estimate wheat production. It was later extended to Canada and former Soviet Union. LACIE was followed by Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS) which included other crops. 


LACIE was the joint programme of NASA (NOAA) and USDA which operated to demonstrate the feasibility to study Landsat data  in agricultural assessment programmes. It was the exemplary model for upcoming experiments. Conducted in three phases, LACIE worked in crop, yield and area estimation of the crops in different modes: quasi-operational and feasibility test mode; the third phase worked for crop forecasting for wheat crop yield at country level.

The applicability of the Landsat was in improving the area sampling frame (AFS) and the regression estimation of the crop areras.

2. Approaches using Remote Sensing

Crops being different (in structure, physiology, phenology) provides different spectral signatures. Ground truth (selected ground information) are used to help in identification of crops. This concept is applied in: ASF Design, Direct estimation or pixel counting, regressioin estimator and calibration estimator

a. ASF design:

Satellite data provides the reference for elaboration for the population frame; areas of interests are divided into enumeration area (EAs). The crop proportion is derived from remote sensing data which is used to characterize spatial cariability and serves as a parameter for stratification for area frame sample design. Area frame can be with physical boundaries or be wtih regular shape.

In simple terms, it is to classify the digital image based on the crops, the grids are overlaid on the classified image. Each grid (can be square or others) is further classified based on the crop proportions. From each classificaatin sample segments are selected for final analysis. 

b. Direct Estimation:

The satellite image is classified from ground truth from sample locations. The number of pixels (within the boundary for each crop) is multiplied by the pixel size.

c. Multidate Data Analysis:

Based on the differences in the phenology of the crops, the spectral signatures are different. Moderate spatial resolution but temporal data is used for this purpose, where decision rule classification process is applied for the crop classification based on decrease or increade in NDVI.

d. Single Date Data Analysis:

Higher resolution satellite data is used in which ground truth information is used for the crop signature classification. The pixels are classified under a particular crop, pixels area is multipled with the number of pixels to obtain the crop area under the  boundary.

e. SAR Data for crop area estimation:

The cloud cover is addressed by the use of microwave SAR data which are based on the backscatter values. There is increase in the backscatter from the early stage to later stage in the plants, and later gets constant. This application remains mostly in rice fields. 

f. Ground Truth Data;

The use of mobile applications are common in the ground truth collection. The ground truth data collection includes geographical location, village, district, state, name of the crop, coverage, condition and so on.

f. Accuracy Estimation:

It is the actual or reference class and the predicted or classified pixels in columns and rows where the performance of the visualization is classified.  

g. Regression Estimator:

It is applied as hybrid based approach with other integrations.

h. Calibration Estimator:

The confusion matrix is used to readjust the pixel count area. Confusion matrix is computed using ground information on a sample of points or segements and the correction of extrapolation.

i. Small Area Estimaor:

This include statistical apporaches and spatial microsimulation approaches which further may have different approaches within them.

3. Crop Area Estimation Programmes:

a. National Level:

USDA/NASS's CDL: The USDA/NASS works in providieng the agriculture statistics in USA. 

FASAL programme: The FASAL programme (launcehed in 2007) works on providing different crop forecasts in the national level. 

b. Regional Programme:

EU/JRC's MARS Programme: It works on the regional crop inventories and estimation of the crop area change at EU level.

c. Global Programme;

CropWatch (China): Developed by Institute of Remote Sensing and Digital Earth (RADI) and Chinese Academy of Sciences (CAS), it works on the global, regional, national and subnational levels. 

USDA/FAS: It works on crop condition assessments for 159 countries. 

GEOGLAM: It works on providing the framework strengthening the capability of the international community to forecast agricultural production. 

3. Cost Effectiveness

The satellite data cost, ground truth collection and analysis cost are the cost involved in the remote sensing. The cost is benefitly reduced while achieving more accurate and timely results. 

4. Limitations

The difficulties can be related towards field size, cloud cover, different crop practices. However, the reduction of the cost in the overall process is the main advantage in the whole process. 

~As extracted from "Handbook on Remote Sensing for Agricultural Statistics" Ch: 5

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